週次AIニュース 2026-W34

From Atari to EVE Online: Building on 15 Years of AI Research in Games

Google DeepMind partners with game studios to prototype breakthrough AI gameplay.

Introducing AI Futures

Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual free…

Offering Zero Data Retention for frontier models

OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compr…

Replit expands access to software creation with GPT-5.6 Luna

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

Strengthening democratic oversight in national security

OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools,…

Partnering with CodeAI to prepare the first AI generation

OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it…

Pacing model development in an era of cyber-critical capabilities

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model de…

Introducing ChatGPT for Teens: Built for learning, backed by protections

ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features…

Asana cleared 5 years of engineering work in 2 weeks with Codex

Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.

OpenAI、「GPT-5.6 Sol」のAPI料金を値下げ　入力20％出力33％安く、11月21日まで

OpenAIは、フラグシップモデル「GPT-5.6 Sol」のAPI利用料金を期間限定で最大33％引き下げた。入力4ドル、出力20ドルとするプロモーション価格を少なくとも3カ月間適用し、ChatGPT WorkやCodexのクレジット消費量も削減。利用量や支出上限を追跡・制御で…

OpenAI、「GPT-5.6 Sol」のAPI料金を値下げ　入力20％出力33％安く、11月21日まで

OpenAIは、フラグシップモデル「GPT-5.6 Sol」のAPI利用料金を期間限定で最大33％引き下げた。入力4ドル、出力20ドルとするプロモーション価格を少なくとも3カ月間適用し、ChatGPT WorkやCodexのクレジット消費量も削減。利用量や支出上限を追跡・制御で…

Harvard’s $699 startup bootcamp offers AI avatars of its instructors

In the HBS Foundry program, AI avatars provide feedback during practice pitches and board meetings.

Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research

Built by DeepMind alumni, British AI lab Inherent released Faraday, an AI agent whose ability to replicate scientific papers could be a ste…

OpenAI says California should strengthen its AI safety bill

OpenAI is calling for California to strengthen SB 53, an AI safety bill that the company previously opposed.

Frontier AI labs still won’t say how they’d contain a rogue model

A new study finds leading AI labs have few publicly documented plans for containing rogue models, raising questions about preparedness as A…

Anthropic’s Opus 4.6 is a smut-machine

Anthropic forbids its Claude models from generating sexually explicit content. But a series of tests conducted by TechCrunch found that it…

Nvidia partners with data center developer Cloverleaf

Nvidia continues to pour money into data center development — just as AI data centers bring lots of money into Nvidia.

Anthropic、「ミュトス 5」を脆弱性スキャンに開放──「Claude Security」経由でEnterprise顧客が利用可能に

Anthropicは、最上位モデル「Claude Mythos 5」をセキュリティサービス「Claude Security」の脆弱性検出に導入したと発表した。モデル本体への直接アクセスは開放せず、パッチ提案などの出力のみに限定して提供する。オープンソース保護に向けた総額3500…

Nvidia just showed that the harness, not the AI model, is now the real hero

Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that gr…

The DOJ is investigating a16z. What does this mean for venture capital?

Andreessen Horowitz has two partners sitting on the boards of companies that now compete with each other: Ben Horowitz at Databricks and Ma…

Starcloud raises $250 million for orbital data centers as launch options dry up

There's about to be a big fight to secure access to space.

From Atari to EVE Online: Building on 15 Years of AI Research in Games

Google DeepMind partners with game studios to prototype breakthrough AI gameplay.

SNSのウソ画像、どう見破る？　熊本県庁やテレビ局も頼る“すごい企業”の正体

スペクティが提供する「Spectee Pro」は、さまざまな情報を収集し、その時に起きている「危機」を可視化するシステムだ。多くの自治体やマスコミも活用しているというSpectee Proは、どうやってデマや虚偽の情報を見分けるのか。

エイベックス松浦会長「AIで仕事が楽になると思ってたけど、真逆」　note記事作成の“苦労”明かす

「AIで仕事が楽になると思ってたけど、真逆でした」――エイベックスの松浦勝人会長は、自身のXアカウントでこのように投稿した。AIを活用したnoteの記事制作の一端を明かした。

中国AI「Kimi」が日本進出か　有料プランのプレゼントキャンペーンも　「はじめまして、日本」

AIモデル「Kimi」を開発する中国Moonshot AIは、Kimiの日本語版公式X（@KimiAI_Japan）で「今日から、日本での歩みを始める」と投稿した。日本に本格進出するとみられる。

FANZAで「成人向けAIコンテンツ制作サービス」開始　8月24日から先行体験

成人向けECサイト「FANZA」を運営するデジタルコマースが、成人向けAIコンテンツプラットフォーム「FANZAスタジオ」の提供を始める予定だ。8月24日からβ版の先行体験を始める。

Active Inference as Context Acquisition for AI Agents

Interactive AI agents must acquire the right context as efficiently as possible. When a user omits a constraint, preference, file, or task…

Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review

The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportat…

How to Navigate Uncertainty About AI Consciousness

Given deep uncertainty about the possibility of artificial consciousness, it is unclear how we should treat potentially sentient AI. On the…

Bounded Sovereignty and the Control Tax: Pricing AI Oversight When the Deployer Does Not Own the Model

AI control research asks how to deploy models safely even when they may be misaligned, but many control protocols assume that the deployer…

Interaction valence reveals contrasting social networks in dairy cattle

Social relationships shape access to resources, exposure to conflict and group stability, yet automated livestock monitoring typically trea…

Air Traffic Control Using Large Language Models: Prompt Engineering, Architecture, and Evaluation

Air traffic control (ATC) communication is a safety-critical dialogue that remains largely human-driven even as other parts of air traffic…

Outcome Monitors: Recovery Affordances for Silent Tool Failures

When a tool call times out, the agent sees the failure and can route around it. A cached error page or negative price can instead arrive in…

Beyond Imitation: Filtering On-Policy Distillation by Reasoning Progress

On-policy distillation (OPD) has emerged as an effective framework for post-training language models by pairing student-generated trajector…

Symposium: Trust via Auditable Records for Communities of AI Scientist Agents

Symposium is a formal framework and practical implementation to record the operation of AI agents deployed by small scientific research com…

From Retrieved Context to Runtime Control: Adaptive Compression for Edge-based RAG

Retrieval-augmented generation (RAG) improves language-model responses by grounding generation in external passages, which comes with overh…

Enforcing LLM Safety through DMD-based Classification of Prompt-Response Embedding Dynamics

Large Language Models (LLMs) are increasingly deployed in high-stakes applications, yet their tendency to generate toxic, harmful, or polic…

Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scale

Scientific data are increasingly used by AI agents, yet existing dataset representations provide limited support for autonomous discovery,…

Can Agent Memory Systems Track Evolving State?

As LLM-based agents are deployed for longer and higher-stakes tasks, their memory systems continue to have crucial gaps. While existing mem…

Frequency-Aware Continual Learning for Smart Contract Vulnerability Detection with Large Language Models

Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability c…

Learning Hierarchical Skill Policies with Offline Quality-Diversity Reinforcement Learning

Recent studies investigate how to leverage pre-collected datasets to improve the policy performance and sample efficiency of RL. One promis…

Rethinking the Evaluation and Optimization of LLM-Based Social Simulation

LLM-based social simulation is a promising complement to traditional methods such as surveys and behavioral experiments. A core question is…

Beyond Memory Majority: Latent-Source Reasoning for Multi-Agent Memory Arbitration

Long-term multi-agent systems continuously accumulate the memories produced by different agents. Existing memory methods typically treat re…

SafeBranch: Branch-Pair Safety Alignment for Embodied Agents

Vision-language-model-based embodied agents can complete instructed tasks but often violate safety constraints in the process, a problem re…

GenMatch: An End-to-End Generative Matching Framework for Micro-View Order-Dispatching in Ride-Hailing

Micro-View Order-Dispatching assigns available drivers to passenger orders within each dispatch batch and is critical to the service qualit…

TT-net: Quantum Inspired Tensor Network Denoising in Conditional GANs

Developed as a workhorse for classical simulations of quantum algorithms and quantum many-body systems, Tensor Network methods have entered…

LLMs as Acquisition Policies for Finite-Pool Materials Optimization: A Controlled Study

Discovering materials with desirable properties often requires searching large candidate spaces while experimental or computational evaluat…

Towards general embodied intelligence: integrating large language models, knowledge bases, and reasoning capabilities to build the next generation of AI agents

The convergence of large language models (LLMs), structured knowledge bases (KBs), and reasoning ability (RA) presents a promising trajecto…

ADAPT: Physics-Aware Diffusion-based World Models for Adaptive Predictive Transferable HVAC Control

Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critic…

When Saying No Makes Better Videos: Designing Dual Gatekeeping for Pedagogically Grounded AI Content Creation

To prevent the adoption of aesthetically polished but pedagogically flawed AI content, we study a video authoring pipeline featuring two la…

Causal Reasoning with Bipartite Graphical Causal Models

Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they ca…

Specification-delta-driven data governance: an empirical study of the {\guillemotleft}spec-delta{\guillemotright} as the unit of change in lakehouse data platforms

Spec Driven Development SDD has consolidated the idea that the specification rather than the code should be the primary artefact governing…

SAPO: Single-Rollout Autoregressive Policy Optimization for Agentic Reinforcement Learning

Agentic reinforcement learning (RL) has become a critical stage in the post-training of large language models. Existing critic-free, group-…

PolicyGuide: From Guarding One Action to Guiding the Whole Workflow for Policy-Compliant LLM Agents

Customer-service LLM agents must follow organizational policy when acting on a user's behalf. Compliance failures arise from either forbidd…

EnvHarness: Awakening Static Worlds for Agent Learning

LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agent's weaknesses, and qu…

TESTNAV: Pareto-Guided Search for Compositional Robustness Testing

Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (…

Write Once, Run Everywhere: The Axon DSL for Shape-Safe and Framework-Agnostic LLM Architectures

The entire ecosystem of open-source language models effectively relies on a single platform. What if this platform was forced to shut down…

EXIMO: VLM Guided Exploration of VLA Policies

How to efficiently finetune robot policies to learn new tasks on the fly? State of the art robotic manipulation policies are based on behav…

Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis

AI agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the cl…

Spike-based Belief Propagation in Nonlinear Dynamical Systems

This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Ba…

A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model

Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared corresponde…

Learning Early-to-Final Solution Consistency for MILP Acceleration

Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad ap…

Rethinking Patch Based Multivariate Time Series Forecasting with Semantic Structured Partitioning

Multivariate time series forecasting (MTSF) is a fundamental task in many real world applications. Existing patch based forecasting methods…

ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance

LLM agents in financial markets may cite rules yet still submit orders that violate executable constraints or misread surveillance evidence…

Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees

Loading reusable skill documents into a bounded context window is now the primary way large language model (LLM) agents acquire task-specif…

ExPhy: A Benchmark for Explicit Physical Property Learning in Multi-Object Trajectory Forecasting

Understanding object dynamics requires not only predicting future trajectories but also examining whether a model captures the physical pro…

Manifold Drift in Flow Preference Optimization: A Root Cause of Reward Hacking

Preference optimization is a standard alignment method for generative models, yet extending it to continuous-time dynamics remains non-triv…

Contrastive Mixed Prompt Learning for Incomplete Multimodal Sentiment Analysis with Unseen Modality Combination

Incomplete multimodal sentiment analysis has garnered significant attention in recent years. Existing approaches typically assume that data…

A three-dimensional typology of agency for advanced AI systems

Research on the agency of advanced artificial intelligence (AI) systems focuses on agency as a normative concept and on the agency of parti…

On the Applicability of Safety Nets: A Safety-By-Design Solution for Certifying Neural Networks

The integration of Artificial Intelligence (AI) in safety-critical aviation systems presents significant challenges for certification and d…

What You Can't See Is What You Learn: Restricted Evidence Visibility Favors Compositional Generalization in Shared-Genome Language-Model Societies

Multi-module systems often expose every module to the full input. We test whether restricting evidence visibility changes which solutions g…

DECOWAM: Decoupled Whole-Body World-Action Model for Legged Mobile Manipulation

Mobile manipulation requires a robot to predict how locomotion and arm motion jointly alter future observations and control. Existing world…

DARS: Dual-Level Credit Assignment RL with Structured Reasoning for Instruction-Based Image Editing

Instruction-based image editing uses a planner-renderer pipeline: a vision-language model (VLM) first converts the instruction into an edit…

The Third Restructuring of Software Form: From the Three-Tier Architecture to Storage, Models, and Agents

Software form has undergone two paradigm shifts since its inception: Software 1.0, in which instructions determine behavior, and Software 2…

MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use

Memory has become a key component of large language models, enabling them to retain information and learn from long-term interactions. Howe…

ContractScrub: A benchmark for final review of legal contracts

Legal work, with its heavy reliance on processing large amounts of text, is often considered one of the domains most exposed to the use of…

Electronic Navigational Chart Change Classification

Electronic Navigational Charts (ENCs) are geospatial vector datasets used in maritime navigation systems that represent hydrographic and na…

InsufficiencyBench: Evaluating LLM legal advice on underspecified user queries

Legal AI systems are increasingly used to answer legal questions, yet existing benchmarks assume queries arrive fully specified. In practic…

Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models

Multimodal large language models (MLLMs) combine linguistic reasoning with visual perception, yet their ability to perform visual spatial p…

QUASAR: A Quantum-Classical Neural Network for SAR Satellite Physical-Layer Authentication

X-band SAR satellites (8-12 GHz) play a critical role in disaster response, environmental monitoring, and military intelligence. Yet, they…

Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation

Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adapti…

Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning

The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls. While…

Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents

Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience. In prac…

Phantom Gains: Auditing Self-Improvement Against a Measured Null

Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and lose…

MidTool: Mid-training Data Synthesis for Agentic Tool Use

Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown th…

Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation

Heterogeneous AI systems composed of multiple models, architectures, harnesses, or inference-time settings can improve quality and efficien…

AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement

Recursive self-improvement (RSI) asks whether an AI system can improve the process that produces AI systems, so that the next system inheri…

An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction

Travel behavior research increasingly combines digital data collection with predictive modeling, yet these stages are often developed and e…

A Virtual Member of a Community of Practice for the Society of Petroleum Engineers: From Prototype to Deployment

We describe the evolution of a virtual assistant, called ATHENA, designed to support the capture, retrieval, and dissemination of knowledge…

Transformer Models for Text Summarization: A Comparative Study of BART, BERT, and RoBERTa

Text summarization refers to the task of condensing a document into a shorter version while preserving its key information. Automatic text…

Automatic bioinformatic software named entity recognition from literature

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific litera…

Asymmetric Attention Heads: Structured Head-Wise Context Allocation for Transformer Attention

Standard multi-head attention (MHA) gives every head the same full causal context span, although heads can serve different contextual roles…

Hallucination as a Feature, not a Defect: Evaluating a multi-agent architecture to transform speculative language-model outputs into testable scientific hypotheses

Contemporary Large Language Models (LLMs) are increasingly aligned to suppress hallucinations, prioritizing factual retrieval over combinat…

Towards On-Board Implementation of ML-Based Helicopter Weight Estimator

This paper focuses on the implementation of a novel supervised Machine Learning model for estimating helicopter weight during takeoff, util…

Represented but Ignored: A Causal Account of Prosodic Underuse in Audio-Language Models

Human speech is richly expressive, with prosody carrying linguistic and emotional information beyond the lexical content. A capable large a…

Time-Series Retrieval for Grounding Multimodal Language Models in Remaining Useful Life

Large language models (LLMs) and agentic AI systems are increasingly being explored for domain-specific maintenance and prognostics tasks,…

Can Conversational AI loosen Us-Versus-Them Boundaries? The Effects of Common, Dual, and Separate Identity Framings on Pro-Immigrant Intergroup Helping

Rising immigration has intensified intergroup tensions in many countries. Traditional bias-reduction programs remain difficult to scale and…

Causal Inference under Interference with Learned Exposure Mappings

Exposure mappings are often assumed to be known in causal spillover analyses. In environmental settings, however, they are typically induce…

When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models

As language models move from drafting prose to running literature-search agents with tool calls, fabricated references are becoming easier…

Active Spiking Perception: The Membrane Potential as a Belief State for Anytime 3D Point Cloud Recognition

Spiking point cloud networks usually scan space in a fixed, input-agnostic order, which leaves the most distinctive resource of spiking com…

Incident-Data Robustness Analysis of the OWASP Top 10 for LLM Applications (2026): How a Community-Expert Ranking Holds Up Against a Large-Scale LLM Incident Corpus

The OWASP Top 10 for LLM Applications ranks the risks that a community of security practitioners judges most important. We ask a narrower q…

Mapping General-Purpose AI Governance in Twenty AI Middle-Power Jurisdictions

The most capable general-purpose AI (GPAI) models are mostly built in two jurisdictions, the United States and China, but the risks they ca…

Quantum Kernel Estimation for the Discovery of Early Lung Cancer Detection

Lung cancer screening with low-dose chest computed tomography reduces mortality, but its impact is limited by uptake, adherence, and manage…

Improved Confidence Estimates for Black-Box Large Language Models

Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized con…

Mechanistic Tomography: Designed Measurement for Control-Oriented Interpretability

Mechanistic interpretability seeks quantities that models do not expose directly: represented states, component effects, interactions, and…

Does Marginal Coverage Guarantee Class-Conditional Safety for Zero-Shot VLMs Under Shift?

Split-conformal prediction provides marginal coverage under exchangeability and is increasingly used as an abstention layer for zero-shot v…

Fairness-Aware Network Embeddings: Methods, Applications, and Challenges

Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classific…

Concentrated Liquidity Provision: a Reinforcement Learning Perspective

Automated market makers (AMMs) are a cornerstone of decentralised finance (DeFi). Constant product markets with concentrated liquidity, suc…

HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloads

Hybrid Transformer-Mamba large language models (LLMs) enhance long-context efficiency, but their heterogeneous computation and communicatio…

HiRA-CAM: Preserving Fine-Grained Spatial Relevance in Gradient-Based Visual Explanations

Deep Learning models can include billions of parameters or more, making it difficult to explain their internal transformations and outputs.…

SCAPE: Scenario-Conditioned Simulation-Augmented Policy Evaluation

Reliable performance evaluation is a central bottleneck for deploying robot-learning policies in real-world conditions. Real-world testing…

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence me…

Are LLMs becoming similarly creative? Evidence from three years of models

Many benchmarks track Large Language Model (LLM) performance on tasks with verifiable answers, but less is known about how LLM performance…

Measuring What a Specification Determines: A Formal Semantic-Block Model and an Execution-Judged Benchmark

This work introduces a formal semantic-block model for specifications and an execution-judged benchmark for evaluating specification qualit…

Accelerated Genetic Programming Hyper-Heuristics for Simulation-Based Scheduling via Agentic AI

Python is widely used in scientific research because it enables rapid development and provides rich ecosystems for data analysis, artificia…

In Two Minds about Lifelong Learning: Exploring Hemispheric Redundancy and Specialisation in Neural Models

Persistent intelligent systems require the ability to learn continually, but current machine learning approaches face significant challenge…

Automated Summarization of Financial News Using Large Language Models and Retrieval-Augmented Generation: An Early Empirical Study (Fall 2023)

Stock market analysts and investors face a daily challenge: too much financial news, too little time. Manually reading and synthesizing hun…

When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models

Many real-world AI systems represent entities, behaviors, and structured information using discrete machine-native symbols rather than natu…

CVSD-Reg: Cross-Modal Visual Semantic Prior Distillation for Robust LiDAR Registration

Learning-based global point cloud registration has achieved remarkable progress, yet its reliance on geometric representations makes existi…

Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models

Streaming autoregressive diffusion models enable real-time, long-horizon video generation, but their training objectives optimize local fra…

DraftFM: A FoundationModel for Day-Zero Drafting in Magic: The Gathering

Drafting a new Magic: The Gathering expansion begins before any pick from it has been observed: the complete card list is public, but the d…

VGI-BENCH: Probing Visual Intelligence in Video Generation Models

Recent studies suggest that video generation models can exhibit certain forms of zero-shot visual reasoning through generated frames. Yet r…

PEA-DPO: Perception-Enhanced Alignment Direct Preference Optimization for MLLMs Alignment

Direct Preference Optimization (DPO) has emerged as an effective approach for aligning large language models (LLMs) with human preferences.…

Forking Fast: Efficiently Estimating Uncertainty Dynamics in Text Generation

LLM reasoning is stochastic, and so understanding a model requires grappling with the distribution of reasoning chains that it might produc…

DeltaML-Bench: Evaluating Machine Learning Agents on Real-World Research Repositories

Autonomous agents for machine learning experimentation must navigate heterogeneous repositories, repair training pipelines, and evaluate ca…

Escaping the Quicksand: A Call to Arms

Computing has been an astonishing success - but the accumulated technical debt exposes us all to huge costs in business and societal risk.…

Loreley: Repository-Scale Program Evolution with Quality-Diversity Search

Sequential agent search accumulates changes from its current champion but discards alternative branches; independent proposals preserve bre…

Robust Cross-Modal Foundation Model Perception for Underwater Robots under Degraded Visual Conditions

Reliable underwater robotic perception remains difficult because optical imagery degrades under turbidity, wavelength-dependent attenuation…

Scale-Separated Conditioning for Style-Encoder-Free Diffusion Stylization

Reference-based diffusion stylization requires separating target geometry from transferable appearance. Existing tuning-based methods often…

A Locally Tokenized Generative Model for Robust Time-Series Watermarking

Watermarking is a central tool for provenance in generative models, yet its application to multivariate time series remains hindered by rel…

TempJail: Temporal Jailbreak Attack against Large Vision-Language Models via Subtitle Scheduling

Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning. Despite extensive studies on t…

Learning to Beat: Phenotype-Guided Latent Flow with Regional Motion Priors for Biventricular Motion Synthesis

Full-cycle biventricular geometry is essential for characterizing cardiac function. However, dense and temporally consistent 3D+t biventric…

Question-Guided Evidence Acquisition for Multimodal Visual Question Answering

Multimodal LLMs can see a document, but they often can't read it reliably. Small text, tables, visual cues, and topological elements still…

Truncate Bad, Upweight Good: BoN-Style Distillation via Rank-Based Classification

Inference-time selection methods, such as Best-of-N, improve generation by sampling a pool of candidates and selecting the top-ranked compl…

GOAG: Generative and Object-Agnostic Grasp Planner for Dexterous Robotic Manipulation

Multifingered grasping is a crucial robotic skill, but current deep-learning grasp planners often struggle to generalize to new objects bec…

Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay

Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signa…

Finite-Horizon Input-Output Dynamics of Minibatch Perturbations in AdamW

A minibatch can influence training beyond the update at which it is observed because AdamW stores past gradient information in its optimize…

CoToGrasp: Contact-Topology-Conditioned Dexterous Grasp Synthesis via Canonical Workspace Learning

Current dexterous grasp planners primarily optimize for physical stability, focusing on whether an object can be grasped rather than how it…

Distilling Aggregate Mobility Statistics into a Language Model Policy for Post-Event Crowd Simulation

Pedestrian simulators need a behaviour rule for every agent, but privacy usually limits the data for setting one to aggregate statistics, n…

An Irreducible Quantum Advantage in Aligning World Models with Reality

World models provide digital simulacra of the true world, allowing agents to be trained and tested before costly real-world deployment. At…

LoRA-GA$^2$: Low Rank Adaptation with Multi-step Gradient Adaptive Alignment

Low-Rank Adaptation (LoRA) is a prominent fine-tuning method for large models, achieving competitive performance with reduced memory overhe…

MileGPO: Milestone Inference with Local Evidence for Graph-Based Policy Optimization of Long-Horizon LLM Agents

Credit assignment is challenging in long-horizon agentic reinforcement learning, where supervision often comes only from final rewards. Exi…

Core-KAN: Continuous Vision Kernels with Kolmogorov-Arnold Networks

Conventional convolutional kernels are typically defined on fixed discrete grids, limiting their ability to accommodate heterogeneous local…

Adaptive Probabilistic Shielding by Learning MDPs for Safe Reinforcement Learning

Probabilistic shielding is a technique for safe reinforcement learning (RL). Typically, a static observer -- called the shield -- constrain…

Repo0: Design-Driven Zero-to-All Code Generation

Large language model agents have made substantial progress in code generation, yet most existing systems assume a predefined repository arc…

Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly…

A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries

Patients often submit short, underspecified queries to healthcare chatbots that lack the patient-specific information needed to determine a…

Separating Covariate Shift from Mechanism Change with Two Discriminators: CJSD, a Conditional Discrepancy with an Exact Covariate-Concept Decomposition

Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn…

Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools

Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn…

Interrupting the Loop: Periodic Subject Changes Raise Judged Surprise and Connection in Base Language Models

Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see? We d…

MaliciousSkillBench: A Comprehensive Benchmark for Malicious Agent Skill Detection

Agent Skills extend LLM agents with reusable instruction packages that may also include scripts, resources, and service configuration. This…

Towards Quantifying Benchmark Optimization in ASR Models

Public benchmarks are important measures of Automatic Speech Recognition (ASR) model capabilities. However, by nature of being public, ther…

Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness

Artificial intelligence (AI) systems are increasingly used across domains to provide personalized information, recommendations, and decisio…

Open-Vocabulary 3D Object Detection with Co-Distillation Discovery and Dual Guidance Robust Training

Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scene…

An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage

Artificial intelligence can support cultural heritage and digital humanities through large-scale retrieval and analysis of digitized collec…

EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models

Hidden chain-of-thought (CoT) traces, especially those from frontier proprietary large reasoning models (LRMs), are valuable model assets.…

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost. However,…

SABET-QA: Temporal Knowledge Graph Question Answering

Question Answering over Temporal Knowledge Graphs (TKGQA) requires reasoning over time-sensitive facts, yet existing embedding-based method…

Evidence-Gated Task and Motion Planning with Vision-Language Models

Robots executing long-horizon manipulation tasks from natural-language instructions must reason about both semantic task structure and geom…

Towards Professional Tennis Styles for Humanoid Robots with Adaptive Motion Planning and Tracking

Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles…

Structured Affinity for Unsupervised Visual Class-Incremental Memory in Deep Artificial Immune Networks

Artificial immune networks (AINs) are naturally memory-forming systems, but conventional visual AINs often rely on flattened vector affinit…

Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection

We present a novel approach to efficient LLM agent harness optimization through adaptive validation task selection. Harness optimization it…

A Standardized Framework for Machine Learning in Power System Protection

Studies of machine-learning-based power-system protection increasingly report near-perfect scores, yet the meaning of those scores depends…

Multi-Method Causal Evidence Synthesis: Ranking Candidate Drivers by Convergent Cross-Method Evidence from Observational Data

Practitioners inferring causality from observational data usually rely on a single method and treat its output as causal truth. Recent tool…

From Agent Behaviour to Agent-Friendly Documentation: An Empirical Study of How Coding Agents Discover, Read, and Write Technical Documentation

Technical documentation is written for human developers, but an increasing share of software changes is now authored by autonomous coding a…

Daedalus-150M: A Convolution-Attention Hybrid Designed for CPU Inference

Small language models are usually built like large ones and then squeezed onto a CPU afterwards. We did the opposite: we fixed the target f…

Prompt-Conditioned Channel Attention for Hierarchical Feature Modulation toward Anatomy-Agnostic Segmentation

Anatomically plausible segmentation remains challenging because of low contrast, ambiguous boundaries, and modality-specific artifacts. Int…

Growth Without Us: Machine Consumers, Corporate Circularity, and the Decoupling of GDP from Humanity after AGI

The standard objection to full automation is demand-side: if humans earn nothing, who buys the output? This confuses an accounting role wit…

Inject, Align, Recover: Staged Post-Training for Retrieval-Free Document Knowledge Internalization

Large language models often fail to answer questions about a bounded document collection when the source documents are not retrieved at inf…

Inducing Task Models from Computer-Use Traces

Naturalistic computer-use traces, passively recorded screenshots and mouse or keyboard actions, are a valuable resource for deriving symbol…

G-CARL: Grounded Checklist-Aligned Reward Learning for Patient-Oriented Medical Report Interpretation

Personalized interpretation of medical reports has emerged as an increasingly important need among patients. Addressing this need requires…

Toward Greater Autonomy in Materials Discovery Agents: Unifying Planning, Physics, and Scientists

We aim at designing language agents with greater autonomy for crystal materials discovery. While most of existing studies restrict the agen…

GridCodex: A RAG-Driven AI Framework for Power Grid Code Reasoning and Compliance

The global shift towards renewable energy presents unprecedented challenges for the electricity industry, making regulatory reasoning and c…

Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts

Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality D…

Gen AI in Proof-based Math Courses: A Pilot Study

With the rapid rise of generative AI in higher education, understanding how students use AI is increasingly important. This exploratory stu…

ATBench: A Diverse and Realistic Agent Trajectory Benchmark for Safety Evaluation and Diagnosis

Evaluating the safety of LLM-based agents is increasingly important because risks in realistic deployments often emerge over multi-step int…

CharTool: Tool-Integrated Visual Reasoning for Chart Understanding

Charts are ubiquitous in scientific and financial literature for presenting structured data. However, chart reasoning remains challenging f…

Agent-First Tool API: A Semantic Interface Paradigm for Enterprise AI Agent Systems

As AI agents transition from research prototypes to enterprise production systems, the tool interfaces they consume remain rooted in human-…

The First Drop of Ink: Nonlinear Impact of Distracting Information in Long-Context Reasoning

As large language models are increasingly deployed in retrieval-augmented generation and agentic systems that accumulate extensive context,…

ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction

Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit phy…

From Accuracy to Auditability: A Survey of Determinism in Financial AI Systems

Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critic…

CADRE: Stable, Parameter Efficient Adaptation of Medical Vision Language Models with Bounded Forgetting and Prior Drift

Medical vision-language models (VLMs) such as BiomedCLIP generalize broadly, but adapting them to a clinical service is as much a safety pr…

FlowEvo: Self-Evolving Agents through the Co-Evolution of Workflows and Executable Skills

Large language model agents can adapt to complex tasks by constructing workflows at inference time, but procedures discovered in one episod…

LLM Capability Limits: Static Emergence and Dynamic Boundary Control

Test-time emergence in LLM systems has a deployment boundary: additional computation can realize decisions already supported by the deploye…

Evaluating Investment Logic in Large Language Models: A Real-World Benchmark Towards Personalzied Financial Agents

Investment competence is inherently personalized: the same market evidence can justify different actions for investors with different goals…

WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN

Recent vision-language navigation (VLN) systems increasingly adapt pretrained vision-language models (VLMs) into vision-language-action (VL…

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains su…

Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing

The proposed model, CarSpeedNet, estimates scalar vehicle speed from a window of three-axis smartphone acceleration, without gyroscope, whe…

Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection

Knowledge distillation (KD) has been widely used to improve lightweight AI models by transferring soft-label knowledge from a large teacher…

Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion

Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landsc…

Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment

People with Parkinson's Disease (PD) often experience progressively worsening gait, including changes in how they turn around, as the disea…

Virtual Sensing to Enable Real-Time Monitoring of Inaccessible Locations & Unmeasurable Parameters

Real-time monitoring of safety-critical interior states is an open problem across energy, environmental and industrial systems where direct…

Improving Requirements Classification with SMOTE-Tomek Preprocessing

This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified…

Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media

Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often r…

HiFi-KPI: A Dataset for Hierarchical KPI Extraction from Earnings Filings

Accurate tagging of earnings reports can yield significant short-term returns for stakeholders. The machine-readable inline eXtensible Busi…

ReynoldsFlow: Physics-Inspired Spatiotemporal Flow Representation for Video Understanding

Video understanding has largely relied on deep spatiotemporal architectures, including 3D convolutional networks and optical flow (OF) base…

Learning-Augmented Power System Operations: A Unified Optimization View

With the increasing penetration of renewable energy and inverter-based resources, traditional physics-based power-system operation faces gr…

The Thousand Brains Theory 2.0: An Extension for the Long-Range Connections of the Neocortical Heterarchy

Vernon Mountcastle hypothesized that the basis for intelligence in mammals is the replication of a general computational unit, the cortical…

PrefixAgent: An LLM-Powered Design Framework for Efficient Prefix Adder Optimization

Prefix adders are fundamental arithmetic circuits, but their design space grows exponentially with bit-width, posing significant optimizati…

The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices

Large language models (LLMs) increasingly act on people's behalf: they write emails, buy groceries, and book restaurants. While the outsour…

DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values

Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks oft…

FMT$^{\mathrm{X}}$: Lazy Wavefront Search for Dynamic Replanning

FMT$^{*}$ plans efficiently in static worlds by expanding a cost-ordered wavefront and collision-checking lazily, but its single-pass unvis…

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning

Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery. While existing…

SUM-AgriVLN: Spatial Understanding Memory for Agricultural Vision-and-Language Navigation

Agricultural robots are emerging as powerful assistants across a wide range of agricultural tasks, nevertheless, they are still heavily rel…

The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain

In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, know…

MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation

Active Surveillance (AS) is a treatment option for managing low and intermediate-risk prostate cancer (PCa), aiming to avoid overtreatment…

A Physics-Informed Neural Network Approach for UAV Path Planning in Dynamic Environments

Unmanned aerial vehicles (UAVs) operating in dynamic wind fields must generate safe and energy-efficient trajectories under physical and en…

PACT: Phenotype-Aware Contrastive Team Representation for Multi-Phenotype Grouped Ad Hoc Teamwork

Learning to collaborate with various unfamiliar teammates poses a great challenge in the domain of multi-agent systems. Existing ad hoc tea…

WaveVerif: Acoustic Side-Channel based Verification of Robotic Workflows

In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly execu…

Towards Audio Token Compression in Large Audio Language Models

Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate t…

Extended to Reality: Prompt Injection in 3D Environments

Multimodal large language models (MLLMs) have advanced the capabilities to interpret and act on visual input in 3D environments, empowering…

Qworld: Question-Specific Evaluation Criteria for LLMs

Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Bi…

Power Couple? AI Growth and Renewable Energy Investment

Artificial intelligence (AI) and renewable energy are increasingly being described as a \mbox{``power couple,''} based on the idea that rap…

Generative AI Use in Entrepreneurship: An Integrative Review and an Empowerment-Entrapment Framework

Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To ad…

VISD: Enhancing Video Reasoning via Structured Self-Distillation

Training VideoLLMs for complex reasoning remains challenging due to sparse sequence level rewards and the lack of fine grained credit assig…

MKG-CARE: Case-Aware Reasoning with Multimodal Knowledge Graphs for Explainable Medical Image Diagnosis

Medical image diagnosis has achieved significant progress with deep learning, yet existing methods often rely on isolated visual evidence a…

The Concept Allocation Zone: Tracking How Concepts Form Across Transformer Depth

Concept formation in transformer language models is a depth-extended process, not a single-layer event: a concept becomes separable across…

Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization

Chain-of-Thought (CoT) faithfulness, i.e., whether CoTs genuinely reflect large language models' (LLM) underlying behavior, is typically ev…

Geometric Evolution Maps: Extracting Stable Concept Probes from Transformer Residual Streams

A concept probe is only as reliable as the layer it is taken from. Probing at a fixed late layer, or at the peak of a separation curve, ign…

Beyond Recall: Behavioral Specification as an Interpretive Layer for AI Personalization

If an AI agent makes decisions on a person's behalf, those decisions must align with its user. We introduce representational accuracy to me…

The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction

Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other fea…

Can Predicted Dynamics Exist in the Physical World?

Can learned state-action proposals exist in the physical world? To filter infeasible commands before execution, policies are often wrapped…

GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows

While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synth…

MPCoT: Reward-Guided Multi-Path Latent Reasoning for Test-Time Scalable Vision-Language-Action

Vision-Language-Action (VLA) policies remain brittle in long-horizon and high-uncertainty control, where one-pass action decoding provides…

Right Family, Wrong Skill: Benchmarking Risk Exposure in Agent Skill Retrieval

Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and…

A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors

In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creat…

PO-PDDL: Learning Symbolic POMDPs from Visual Demonstrations for Robot Planning Under Uncertainty

Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Ob…

Every Step of the Way: Video-based Parkinsonian Turning Step Counting

As a prominent symptom of Parkinson's disease (PD), turning impairment is evaluated through parameters such as turning angle, duration, and…

SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)

Deformable object manipulation poses challenges beyond task completion: successful execution must also maintain safe physical interaction,…

Drift-Adaptive ICU Intervention Prediction: Freezing the Physiological Encoder for Auditable Model Updating

Clinical decision support degrades as treatment protocols evolve, but the obstacle to updating a deployed model is governance as much as ac…

A Distributional Robustness Margin For Pathology Foundation Models

Pathology foundation models encode non-biological variation introduced by tissue preparation, staining and scanning, enabling shortcut lear…

Anatomy Contextualized Adaptation of CT Foundation Models

CT vision-language foundation models have demonstrated promising performance across downstream tasks, but are typically trained with whole-…

FinVerse: Financial Time-Series Benchmark

As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has b…

Socioduality: A Relational Process Framework for Human-AI Interaction

Human-AI research often evaluates individual capabilities, joint performance, or final outputs, but these approaches can lose the interacti…

A Model-Internal Protocol for Assessing Multimodal Models as Integrated Systems

As Large Vision-Language Models increasingly aim to integrate visual generation and understanding within a single parameter space, evaluati…

Why Do AI Agents Break Rules? How Framing, Context, and Social Signals Shape Compliance

Specifying a penalty can turn a legal obligation into a cost-benefit calculation that favors violation. We show that this enforcement infor…

「Claudeの使い方」を無料で学べる公式サイト登場　「Code」「Cowork」などサービスごとに解説

米Anthropicは、無料の学習サイト「Claude Academy」を公開した。AI技術の基本や、AIサービス「Claude」関連製品の使い方を解説している。

Googleのオープンモデル「Gemma」、累計10億ダウンロード超　GitHubに公式ディレクトリ公開

Googleは、オープンモデル「Gemma」ファミリーの累計ダウンロード数が10億回を突破したと発表した。派生モデルは10万種を超え、公式リポジトリ「Awesome Gemma」をGitHubで公開。宇宙空間での衛星データ解析や新規のがん治療経路の発見など、多様な活用事例を紹介…

macOS版ChatGPT、Appleの「メッセージ」と連携　会話検索や下書き、送信に対応

OpenAIは、macOS版ChatGPT向けにAppleの「メッセージ」アプリと連携するプラグインを公開した。CodexやChatGPT Workのチャット上で過去の会話検索や下書き作成、送信が可能になる。誤送信防止のため都度承認フローを備える。Appleシリコン搭載Mac向…

AI data startup Micro1 reaches $500M gross run rate amid AI training boom

Surging demand for AI training data is driving rapid growth for the startup and its rivals.

「Fable禁止」で仕事が止まったあの日々を振り返る　日本企業が取るべき「脱・単一モデル」戦略

米政府の輸出管理で「Claude Fable 5」の提供が突如停止し、業務が止まった実体験を基に、単一AIモデル依存の地政学リスクを指摘。オンプレミス化やマルチモデル統合基盤など、日本企業が取るべき分散戦略を整理する。

データをつなぎ、AI活用へ――オートデスクが示す設計／製造DXの未来像

オートデスクは「Design & Make Summit Japan 2026」を東京都内で開催した。本稿では、米Autodeskのビック・ベダンサム氏による基調講演から、AI時代の製造業に求められるデータ基盤と設計／製造の変革について紹介する。

業務標準化の手間を9割減　三菱UFJ銀行は生成AIに「業務知識」をどう教えた？

海外事務の標準化を進めている三菱UFJ銀行。熟練従業員に頼ってきた業務プロセスの精査を生成AIに置き換える過程で直面したのは、AI特有の誤情報や回答のバラつきだった。同行はこれをどう克服したのか。

Snowflakeが過去最高業績　CEOが「他社との差別化は容易になった」と語るワケ

競合が同じ方向に走り出した今こそ、差別化はむしろ容易になっている――。SnowflakeのCEOが年次カンファレンスでこう言い切った根拠はどこにあるのか。AIエージェント時代のSnowflakeの戦略に迫る。

「チャピる」「ギュられる」って何？　今年流行った「就活用語」にAI関連ワード　マイナビ調査

マイナビは、2027年卒の就職活動で流行した用語を発表した。米OpenAIのチャットAI「ChatGPT」を示す「チャッピー」に加え、「チャピる」「ギュられる」といったAIに関する新語が登場した。

OpenAI is gaining on Anthropic with business users, new data indicates

Businesses are willing to flop back and forth as each lab releases new models, volatility that should give both companies' investors pause…

ChatGPT can now send texts for you with new Apple Messages plug-in

Ever wanted someone else to do your texting for you? ChatGPT is being offered up as an automated text scribe via a new Apple Messages integ…

ChatGPTに「おすすめの○○は？」　実は答えが決まっているらしい：893rd Lap

ChatGPTに「おすすめの○○は？」と聞けば、いくつかのブランドや商品を教えてくれる。では、その候補はどうやって選ばれているのだろうか。どうやらChatGPTは、検索を始める前から「この分野ならこれ」と、ある程度の候補を持っているらしい。

「たった14人」の挑戦から7兆円の逆転劇へ　ラピダス小池社長の「TSMCとは戦わない」2ナノ半導体の勝算

世界の半導体市場を台湾TSMCが席巻する中、7兆円規模の国家プロジェクトとして最先端「2ナノ」の量産化に挑むのがラピダスだ。同社はTSMCとの規模の勝負を避け、設計から前後工程を一棟で完結させる「RUMS」による多品種生産で勝負する。「たった14人」の同志でスタートした原点から…

「孫さんはOpenAIだが、僕はAnthropic」　SBI北尾会長が語る「AI投資5億円→増収27億円」の勝算

SBIホールディングスが生成AI「Claude」を開発する米Anthropicとの全社提携を発表した。当面の最優先戦略にAI化を掲げ、社外から専門人材を起用。SBI証券では顧客対応のAIエージェント開発に5億円を投資し、口座再活性化などを通じて年27億円の増収を見込む。「孫正義…

「Gemini Notebook」で利用者10倍　シニア社員をAIヘビーユーザーにした首都高の考え

「生成AIを何に使えばよいかわからない」という理由により、生成AIの活用が停滞してしまう企業は多い。安全を最優先する故に慎重な組織風土であった首都高速道路でも同様の課題を抱えていた。しかし同社では「Google Gemini」を起点としたある工夫により、劇的に活用状況を改善した…

Slack、AIとチームで協働する「Slack Code」を発表　ClaudeやDevinを専用チャネルで操作

Slackは、AIコーディングエージェントと協働するための新機能「Slack Code」を発表した。メンションで専用の「コードチャネル」が自動生成され、計画やコード差分、プレビューを確認しながら指示できる。ClaudeやDevinなど複数社のエージェントに対応し、人間の承認を経…

カルビーが挑むジャガイモ収量の限界――自社開発AIでサプライチェーン最適化

「ポテトチップス」や「じゃがりこ」など、カルビーの主力商品に欠かせないばれいしょには、収穫量の限界がある。後手の意思決定から脱却すべく、同社はAIを活用した全社最適シミュレーター「C-BOSS」を自社開発。いかにして現場定着の壁を越え、データに基づく攻めのサプライチェーンを構築…

OK, can we actually cool data centers with our pee?

Jason Kelce joked that people should cool data centers with their pee, rather than potable water -- but his suggestion is not completely lu…

GoogleはAI競争に負けたのか　「最強のAI」ではなく「AIの“電力網”」を選ぶ賭け

GoogleからAI研究の中心人物が相次いで去った。「Geminiは終わった」という見方に対し、「最先端ではなく、AIを社会全体に行き渡らせる“電力網”で勝つ賭けだ」という別の解釈もある。電気の歴史になぞらえながら整理する。

Google gives publishers a new way to fight AI-driven traffic losses

Google is giving publishers a new button that lets readers make them a preferred source across Search, Discover, and Google News, potential…

Runlayer, Rippling drop lawsuits — but the brouhaha is still a cautionary tale for founders

Runlayer and Rippling have dropped their lawsuits. No money was paid. Rippling celebrated by releasing a competing product.

Linkdaze’s smart calendar is built to run a household, not just track a schedule

Linkdaze's smart digital calendar stands out for not putting its features behind a paywall, including an AI meal planner tool.

Grok keeps sending gibberish responses to users

Affected users told TechCrunch they were using Grok Lite, and noticed the issues as early as Wednesday morning.

A third of web pages published since ChatGPT’s launch show signs of AI authorship, study finds

ChatGPT and other AI models are now authoring and editing much of the new web.

Ramp launches its own AI model router, called Router

Ramp has launched its own AI model routing service, dubbed Router, that lets users and companies use and switch between various large langu…

Meta brings Pocket, an app that lets you vibe-code and share games, to US users

Meta is bringing Pocket, its experimental AI-powered app for creating and sharing interactive games, to users across the U.S. after quietly…

Inertia Enterprises finds a way to make its fusion fuel fast

Fusion power startup Inertia Enterprises reduced the fuel filling process from a week to just a few hours. It's one of 10 hurdles the compa…

Meta AI’s new Mac app wants you to talk to your apps

The company said that the dictation feature works across all apps, just like other tools such as Wispr Flow, Superwhisper, and Monologue.

Binance now lets AI agents trade, but keeping them in check is largely up to users

Binance's Agent OS works with tools such as ChatGPT, Claude Code, and Cursor.

「ロボットのChatGPTモーメントが近づいている」　中国UnitreeのCEO、世界ロボット大会で言及

Unitreeのワン・シンシンCEOは世界ロボット大会で、ロボットの「ChatGPTモーメント」が近づいていると発言した。

日本精工が「国産人型ロボ」開発を後押し　スタートアップのアトムと協力、アクチュエータの検証など

人型ロボットを開発するスタートアップのアトムは、日本精工（NSK）と国産人型ロボットの開発・実装に向け、戦略的パートナーシップに関する基本合意書を締結したと発表した。

Introducing AI Futures

Introducing AI Futures, a new OpenAI blog exploring how transformative AI could reshape power, governance, the economy, and individual free…

エイベックス松浦会長、noteの“バズり記事”を「ほぼAI」で作成　「僕の60年分のデータを入れた」

エイベックスの松浦勝人会長は、8月13日から「note」に投稿している記事について、ほとんどAIで作成していたと自身のXアカウント（@maxmatsuuratwit）で明かした。

Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions

This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and sh…

Position: Profiling Game Worlds by Transition Complexity

Game world modeling (GWM) and reinforcement learning (RL) are often confounded because research papers rarely quantify how difficult the un…

Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational age…

Position: Behavioral Systems Require Behavioral Tests

Artificial agentic systems increasingly operate as behavioral systems by interacting with dynamic environments, pursuing goals, and adaptin…

Position: Current Model Cards Are Insufficient for Downstream Governance of Open-Weight Foundation Models

The growth of open-weight foundation models (OWFMs) has prompted the AI community to re-evaluate strategies for effective downstream govern…

A Metamorphic Artificial Age Score Decision-Support Prototype for Flight-Log-Based Drone Propeller Health Monitoring

Drone propeller faults can create safety and reliability risks when their effects are distributed across multiple flight-log channels rathe…

Position: Multi-Agent Systems Should Prioritize Concurrency Control

LLM-based multi-agent systems (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues…

FinSkillBench: Evaluating AI Agents and Domain Skills for Investment Management

Investment management is a high-stakes domain in which agentic AI systems must do more than generate plausible text. They must retrieve poi…

Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective

Large language model (LLM)-based agents are increasingly becoming self-evolving systems that persist across interactions, maintain memories…

Emergence of Agentic AI: A Review on Evolution, Background, Working Principles, Applications, Adoption Factors, and Future Research Directions

Agentic AI is gaining new insights and advancements in the field of Artificial Intelligence, fostering significant potential to enable rapi…

Solving Is Not Drawing: A Benchmark for Diagrammatic Reasoning in Olympiad Geometry

Foundation models such as GPT and Claude now solve olympiad-level mathematics with remarkable proficiency, so much so that geometry problem…

Position: AI Leaderboards Are Underserving the Global South: A Case Study from India

This position paper argues that AI leaderboards are structurally ill-suited to serving the Global South because they lack independent gover…

Safety Alignment Illusion: The Cross-Lingual Safety Gap in LLMs

Current safety alignment training for Large Language Models (LLMs) are heavily English-centric. When such safety filters fail for non-Engli…

Optimized Fuzzy Logic Approach with the IEEE Key Gas Method for Diagnosing Power Transformer Faults Using Dissolved Gas Analysis

Reliable transformer fault diagnosis is essential for maintaining power system stability. The IEEE Key Gas Method (KGM), a widely utilized…

Improving Rural Medication Safety with AI: A Scoping Review

Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducin…

FraudBench: Stress-Testing Policy-Grounded Banking Agents Against Adaptive Fraud

Conversational agents now act for end users through tools while holding access to customer databases and internal policy documents that a c…

Efficient Adaptation of LLMs for Hate Speech Detection in Low-Resource Languages: A Comparative Study on Roman Urdu

It is challenging to detect hate speech in Low Resource Languages (LRLs) because of the absence of annotated data, the informality of its l…

RDFdL: Integrating RDF with Differential Dynamic Logic

Knowledge graphs modeled in RDF are powerful for describing static knowledge, but they cannot capture or reason about the dynamic behavior…

Adversarial Review: Structured Disagreement for Grounded Agentic Code Review

Early multi-agent LLM systems often used role-separated teams, yet scaling agent count yields diminishing returns on repository-level codin…

Looped Language Models Improve Compositional Tool Calling

Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexp…

On the Triangle Inequality for the Jaccard Distance in Arbitrary Lattices

This paper presents new theoretical results on generalizing the Jaccard distance for lattices and real valuations. We demonstrate that when…

GenEx: A Graph-Based Representational Paradigm for SARS-CoV-2 Variant Detection via Codon Co-occurrence Networks

Genomic analysis on viruses such as SARS-CoV-2 variants: Beta, Gamma, Delta, and Omicron is heavily dominated by classical bioinformatics m…

Redakto - The Incognito Tab for LLMs

Large Language Models (LLMs) are being increasingly used in everyday applications. A major challenge in the context of LLMs or Artificial I…

Cacheable by Design? Training Mixture-of-Experts Routers for Locality Against the Edge Memory-Bandwidth Wall: A Pre-Registered Negative Result with a Systems Measurement Study

Serving a 235B-parameter Mixture-of-Experts (MoE) model on a single 8 GB GPU is bottlenecked not by compute but by memory bandwidth: decode…

Evaluating Structured Information Extraction with Open Models in a High Risk Public Sector Application

The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sect…

The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations

LLM-as-a-Judge, which leverages a large language model to evaluate natural language generated by another AI application or model, has becom…

SESSE: Sketch, Expand, Sort, Summarize, Evaluate -- LLM-as-Judge Evaluation via Structured Decomposition

LLM-as-judge evaluation reduces response quality assessment to a single holistic A/B preference choice, providing no mechanism to isolate w…

ComponentBench: Diagnosing Component-Level Failures in Computer-Use Agents

Current evaluation of computer-use agents is split between long-horizon workflow benchmarks and atomic GUI-grounding tests. This leaves an…

Governance Records as Supervision: Verifier-Selected Self-Training for Structured Workflow Repair

Machine-verifiable workflows produce governance records linking a task contract, model attempt, verifier decision, accepted output, and tar…

Measuring the Partial-Credit Gap: A Strict Benchmark on Vietnam's 2025 Convex Marking Scheme

When evaluating language models on human exams, benchmarks typically score each response as right or wrong and report the overall accuracy.…

A Jagged Frontier: Evaluating Robustness of Code Agents to Semantics-Preserving Transformations

AI code agents are increasingly deployed to resolve real software issues, yet their reliability under superficial code variations remains p…

When Clean Signals Are Not Enough: Detecting Structural Ambiguity for Safe Wearable Stress Classification

Wearable stress classifiers can achieve strong average performance while failing completely for a particular individual. On WESAD, a Random…

Improving Natural-Language Combinatorial-Optimization Accuracy in Resource-Constrained Language Models via Formal Abstractions

Combinatorial scheduling poses a significant challenge for language models, requiring them to identify feasible solutions within exponentia…

FM-Bench: A Benchmark for Long-Horizon Management with Competing Agents

Language model agents now execute bounded tasks reliably. Whether they can sustain effective decision-making over long horizons, where acti…

UMER: Unifying Embedding and Ranking via Pair-Aware Discriminative Reasoning for Universal Multimodal Retrieval

Universal multimodal retrieval aims to support diverse instruction-aware retrieval tasks, demanding both efficient corpus-scale matching an…

Which Negatives Matter? Ask Your Text Encoder: Adaptive Similarity Margins for Dense-Caption Retrieval

Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules i…

Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson

Industrial explainable-recommendation systems built on LLMs incur a substantial serving cost: each request triggers an LLM generation, with…

FinRCA-Bench: Benchmarking Evidence Retrieval and Reasoning for Financial AI Systems

Large language models are increasingly used to support financial operations, but their apparent reasoning performance can depend on whether…

Bridging Search and CRM: Productionizing AI Product Research Agents for Customer Re-Engagement

Modern e-commerce platforms often operate search, recommendation, personalization, and CRM systems independently, limiting opportunities fo…

FACET: Preserving Source Intent and Executable State in Terminal Task Synthesis

Training terminal agents requires scalable executable supervision, yet synthesizing high-quality terminal tasks remains challenging. Each t…

Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference

Uniformly allocating inference reasoning budgets to LLMs is expensive and prone to over-thinking penalties; especially in document tasks wh…

CTIFoundry: An Agent-Native Corpus Scaffold for Cyber Threat Intelligence

Cyber threat intelligence (CTI) is increasingly consumed not by human analysts but by LLM agents that compose multi-step investigations at…

Preference Reasoning under Indeterminacy in Large Language Models

As large language models evolve into decision-making agents, the ability to reason over preferences becomes fundamental to alignment, coord…

Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search

Industrial sensor diagnostics relies on preprocessing, representation, and classification pipelines, making automated pipeline search usefu…

Sanyu Studio: A Multi-Agent System for Art-Historical Narrative Construction

Amid concerns that generative AI may standardize art interpretation, this paper examines whether LLM-based interaction can support plural a…

RTPO: Reverse-Turn Policy Optimization for Stabilizing Agentic RL Training

Training multi-turn agentic workflows with reinforcement learning (RL) enables large language models to perform complex reasoning, use exte…

Competence, Not Accuracy: A Diagnostic for Reference-Free Judge Gates in Skill Optimization

Text-space skill optimization adapts a frozen agent by evolving a natural-language skill document, accepting each candidate through a valid…

A Multi-Agent Platform for Automated Enterprise Analytics and Insight Generation

This paper proposes a multi-agent framework built on CrewAI [1] for conversational business intelligence. Five specialized AI agents operat…

Metrics That Write Themselves: Evolving an Evaluator from Its Own Blind Spots

Agents improve quickly against a reliable automatic metric and stall without one, and the applications that need them most, report generati…

Pairwise Logical Selection of Enthymeme Completions under Semantic-Link Uncertainty

Arguments often omit premises or claims, forming enthymemes. We study pairwise logical selection between two candidates for the omitted com…

Verifiable abstention makes AI leak diagnosis accountable in water distribution networks

Utilities lose a substantial share of treated water to leakage, yet rarely trust artificial-intelligence localizers to dispatch crews: gues…

ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery

Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recen…

SkillGate: Training In-Policy Skill Selection in Long-Horizon Agents

Agent frameworks increasingly package procedural knowledge as skills: instruction files an agent reads on demand, while public libraries no…

DentAgent: Evidence-Centric Multi-Agent Coordination for Multimodal Dental Reasoning

Oral diseases affect billions of people worldwide, underscoring a pressing need for accurate and reliable dental assessment that integrates…

Training-Free Inference-Time Self-Reflection and Cost-Bounded Early Stopping for Large Language Models

Reinforcement-learning training of reasoning LLMs (e.g., GRPO) is expensive and requires a controllable environment, committing every contr…

Syntactic Simplification of OWL Class Expressions

Class expression learning often produces complex OWL class expressions that are difficult to interpret and reason over. However, by followi…

\textsc{TestifAI}: Tomography-Based Testing for Deep Learning Systems

As AI systems are increasingly deployed in safety-critical application domains (e.g., autonomous driving), associated risks increase too. D…

Breaking the weakest link to evade vision language models

Vision Language Models (VLMs) have recently emerged as a critical component of multimodal AI systems, enabling joint reasoning over visual…

A Theory of Post-hoc Debate Judgement

Debates have recently emerged as a useful methodology for agentic AI to improve performance as well as to aid explainability and user engag…

Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models

Accurately extracting nuanced, contextualized data from research articles is laborious and time intensive. Here, we investigate the perform…

Adaptive Memory and Reflection Multi-Agent System for Medical Question Answering

Accurate and responsible medical question answering (QA) is important in healthcare, where complex cases require factual knowledge and nuan…

Eureka: Task-Conditioned Meta-Agent Orchestration for Scientific Discovery

We present Eureka, a task-conditioned Meta-Agent architecture that compiles long-horizon tasks into dynamic obligation graphs with explicit…

What is Missing from AI Post-Training AI: An Empirical Analysis

Large language model (LLM) agents can now post-train an LLM end-to-end. They can write code, launch training, evaluate checkpoints, and imp…

Robust Risk Under Evolving Uncertainty: A Wasserstein Counterpart of the Entropic Value-at-Risk

An agent still learning its environment should be cautious while ignorant and bold once confident. The entropic value-at-risk captures this…

Tuning the Stochastic Machine: A Systems Engineer's Operating Model for Human-AI Engineering

When an expert corrects an LLM assistant's error, the correction usually dies with the session, and the error class returns. I argue this i…

Grouping the Stochastic Machine: Precision, Not Capability, as the Frontier Metric for AI Systems

Frontier language models are compared, marketed, and benchmarked on capability -- what their best or average output can achieve. I argue th…

Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication

Language-model agents can communicate through continuous hidden states that are invisible in public transcripts, creating opportunities for…

SuTRA : Structurally-Unified Tokenization with Root Awareness

Existing subword tokenizers optimize statistical compression but ignore morphological structure, particularly the relationship between root…

Latent Space Refusal Anchoring for Low-Resource African Languages: Mechanistic Safety Recovery Without Retraining

Instruction-tuned models often refuse harmful requests in English but comply with the same requests in Yoruba, Igbo, Igala, and Hausa. This…

Nine Emotion Centroids: A Label-Free Valence Axis That Transfers Across Four Modalities

Inside a modern language model sits a single internal direction that tracks how positive or negative a sentence feels. We show how to find…

Self- and Other-Labels Induce Bidirectional Bias in LLM Judges

As LLM-as-a-judge systems become increasingly widespread, self-preference in LLMs -- the tendency to favor one's own outputs -- raises grow…

Abliteration Mitigation via Refusal Aliases

Abliteration, the removal of refusal capabilities from large language models by projecting weight matrices orthogonal to an extracted refus…

NE-BERT: A Multilingual Language Model for Nine Northeast Indian Languages

Large pretrained language models have demonstrated remarkable capabilities across diverse languages, yet critically underrepresented low-re…

Backdoor Learning in Language Models and Vision-Language Models

Recent advances in deep learning have significantly enhanced the capabilities of Natural Language Processing (NLP) and Vision-Language Mode…

Fractional Decay KV-Cache: Ownership-Aware Memory Management for Improved Inference Relevancy in Dialog Systems

Key-value (KV) caching is essential for efficient autoregressive inference in transformer based dialog systems, yet existing strategies tre…

Computational Orientalism: Measuring Structural Discourse Bias in Large Language Models Using the Middle East Cultural Sensitivity Score (MECSS)

AI systems now shape how hundreds of millions of people learn about cultures other than their own. When someone asks one of these systems a…

DeepTCM1.0: A Multi-Expert AI Agent for Deciphering Mechanisms of Chinese Herbal Formulae Based on General Large Language Models

Background: Mechanistic elucidation of traditional Chinese medicine (TCM) compound formulas remains a central challenge in the modernizatio…

StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data

StocksTalk is a voice-enabled conversational system for transforming spoken financial screening requests into executable and validated stru…

Different Facets of Verbalised Overconfidence: an Interpretability Study

Large language models tend to overconfidence, giving assertive answers when the evidence suggests hedging or abstention. Using controlled r…

Institutional Prestige as Geographic Bias in Large Language Models: Evidence from Three Factorial Experiments with Bootstrap Confidence Intervals

We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity…

Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation

Large language models are being incorporated into sensitive and important decision-making processes across nearly all fields. While prior w…

Accurate Decoding of Natural Sentences from Non-Invasive Brain Recordings

Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial…

Temporal Multi-Signal Fusion for Token-Level Hallucination Detection

Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is conf…

Global Index on Responsible AI 2026 : Conceptual Framework and Methodology

This report presents the methodology of the Global Index on Responsible AI (GIRAI), 2nd Edition. This edition refines the 1st Edition by st…

Language Models for Portuguese: A Systematic Mapping Study

In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of…

The Deontic Gap: Large Language Models and the Modal Language of Obligation

Modal auxiliaries such as must, should, and have to mark necessity and obligation within the contexts of speaker authority and interpersona…

Entropy-Constrained Adaptive Stochastic Quantization

Adaptive stochastic quantization (ASQ) is a recently introduced quantization approach that optimizes the Mean Squared Error (MSE) for a giv…

TokenPowerSandbox: Evidence-Gated CPU-First Screening for Energy-Aware LLM Serving

Energy-aware LLM serving requires comparing configurations under realistic request shapes, yet exhaustive target-GPU profiling is costly an…

How Quantum Is the Advantage? A Fair, Calibration- and Noise-Aware Benchmark and Attribution Audit of Quantum Machine Learning for Network Intrusion Detection

Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most ri…

When Do LLMs Actually Help? Evaluating LLMs as Data Quality Annotators

LLMs have been increasingly used to catch data quality issues automatically, but we know very little about how consistent these judgments a…

Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation

Safety evaluations of large language models (LLMs) predominantly rely on text-based adversarial prompts, potentially overlooking vulnerabil…

What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems

In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainti…

A systematic review of machine learning techniques to address diagnosis and treatment of autism: challenges and opportunities

Autism spectrum disorder (ASD) is a developmental disability characterized by challenges in social interaction and communication. As the ca…

Bound-Aware Per-Organ Recall Risk Control for Multi-Organ CT Segmentation under Clinical Domain Shift

Distribution-free risk control adds organ-specific recall guarantees to frozen segmentation. We calibrate per-organ thresholds for an AMOS-…

GigaBrain-WBC-0.5: A Behavior World Model for Robust Whole-Body Control with Environment Interaction

Whole-body motion tracking policies turn a humanoid into a robust control interface: the teleoperator---or an upstream model---only supplie…

Bidirectional representational alignment between biological and artificial neural networks

Recent work has shown that representational alignment between biological and artificial neural networks is asymmetric: model representation…

Visual-Prompt Guided Wildlife Instance-Level Recognition

Fine-grained wildlife re-identification remains a challenging area in research. Current state-of-the-art approaches apply a detection and r…

How AI Prompts Can Teach Us About the Structure of Human Behavior

We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large la…

SeisEvo: Evolution of Seismic Data Reconstruction Algorithms by Agents

Classical seismic data reconstruction relies on manually designed structural priors and iterative operators, whose coupled design space is…

What Makes Software Issue Resolution Tasks Difficult for Agents?

Background. Advances in agentic systems are simultaneously, and rapidly, saturating benchmarks. Despite this often discussed phenomena, ben…

Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements

An increasing number of scholars use AI to measure variables they subsequently include in downstream analyses. Although AI-measured variabl…

FairGlucose: A CGM Fairness Benchmark Reveals Subgroup Disparities Hidden in Population-Level Validation

As CGM-based AI tools approach clinical deployment, whether their accuracy is equitable across patient demographics remains insufficiently…

FedCoRe: Target-Adaptive Completion for Missing Modalities in Healthcare Federated Learning

Federated multimodal models often assume every site has every modality, although hospitals differ in access to EHRs, chest radiographs, and…

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts du…

Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring

Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is constrained by power, latency…

Coupled-cluster molecular properties across the main group that extrapolate beyond training size

Coupled-cluster theory defines the accuracy standard for molecular electronic-structure properties but scales too steeply for routine appli…

Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents

Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need…

One Gate Is Not Enough: Composing Stateful Pre-Action Controls for Agentic AI

Agentic AI systems take consequential actions governed by more than one pre-action control at once: authority, resource, and evidence gates…

Selection, Recombination, or a Fresh Solve? A Candidate-Free Control for Single-Pass Test-Time Aggregation

When every candidate is wrong, correct-candidate selection is unavailable, yet the aggregation call can still solve the problem afresh. A c…

TTSD-FAR: Test-Time Self-Distillation with Fisher-Anchored Restoration for Missing-Modality Emotion Recognition in LVLMs

Large video-language models (LVLMs) have shown remarkable performance on multimodal tasks like multimodal emotion recognition (ER) in the w…

LEDGER: Claim-to-Evidence Trace Graphs for Auditing LLM Agents

Large language model (LLM) agents can now carry out long-horizon technical workflows involving complex tool use, code execution, file edits…

Vector Symbolic Policy Gradient

We answer this question with Vector-Symbolic Policy Gradient (VSPG), a discrete-action actor that represents each action by a unit-norm hyp…

Mechanistic Interpretability of Structure-Aware Numerical Reasoning in LLaMA 3.1 8B

Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-se…

Pedagogical AI in Mental Health: A Tri-Stream Fine-Tuned LLM Framework for Automated Clinical Supervision and Risk Triage

Modern mental healthcare faces a critical shortage of senior supervisory oversight, leading to a "supervision gap" where novice therapists…

Formal Verification of Romanov's Triplet Logic: A Verified Filter for Sliding-window 3-CNF with Application to Structured Formulas

We present the first mechanised formalisation of Romanov's Triplet Logic (TLS) in the Rocq proof assistant. TLS is a triplet-based combinat…

ERASE: EaRly bAckpropagation SchEdule for Faster Training of Modern Recommendation Systems

Lightweight proxy models enable rapid experimentation without repeatedly training frontier-scale systems, but their small kernels often lea…

Coverage-Driven RTL Assertion Generation with Formal Exploration and Neuro-Symbolic Refinement

Hardware functional verification relies on high-quality assertions to expose design bugs and establish confidence in Register Transfer Leve…

Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models

Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an…

Physics-Unrolled Neural Operator for Wireless Field Modeling

Radio maps are essential for wireless decision-making tasks such as access-point placement, coverage planning, and localization, but their…

Science Done on a Machine by a Machine: AI Agents in Computational Chemistry

We are witnessing an explosion of agentic systems for computational chemistry simulations: from half a dozen in 2024 to a dozen in 2025, an…

OptiModNet: A UNet-Transformer Hybrid with Grouped-Query and Channel Attention for Optic Disc and Cup Segmentation

Precise segmentation of the optic disc and cup is critical for the early detection and diagnosis of glaucoma. However, achieving consistent…

GCNO: Gramian Chebyshev Neural Operator for Physics-Based Compression of Wireless Channels

Large antenna arrays allow wireless systems to serve more users and achieve higher data rates, but they also make channel feedback expensiv…

Prior-Conditioned Gaussian Discriminants for Generalizable AI-generated Image Detection

Diffusion-based generators have made synthetic images ubiquitous, but detectors often fail under simultaneous shifts in generator, prompt/s…

DART-SD: Diamond-topology Aware Retrieval and Tuning for Self-Distillation of Multi-Turn Tool-Calling Agents

Equipping Large Language Models (LLMs) with multi-turn tool-calling capabilities is essential for building autonomous agents. However, prog…

Evaluating and Explaining Prompt Sensitivity of LLMs Using Interactions

The remarkable capabilities of large language models (LLMs) are often undermined by their instability. Even subtle and semantically irrelev…

CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks

Most LLM benchmarks rank models on their ability to automate work tasks. In practice, however, models are often used to assist other (human…

Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments

Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT…

MorphoGP: A Nonparametric Framework for Predicting Equilibrium Beach Profiles Under Tidal Influence

The prediction of equilibrium beach profiles under tidal influence is of fundamental importance for sustainable coastal development, inform…

The Role of Grid Cells in Reducing Spatial Aliasing in Hippocampal Place Representations

Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations, primarily due to environment…

MR-IQA-2: Faithful Image Quality Reflection via Fine-Grained Credit Assignment

Multimodal large language models (MLLMs) have shown strong potential for image quality assessment (IQA) by improving consistency between qu…

From Storage to Access: Verifiable Activation of Parametric Knowledge in LLMs via Explicit Priming and Implicit Reasoning

Although Large Language Models (LLMs) encode rich factual knowledge in their parameters, reliably recalling and verifying such knowledge re…

OmniHandwritingOCR: A Diagnostic Benchmark for Evaluating Multimodal LLMs in Handwritten OCR Scenarios

Multimodal large language models (MLLMs) are increasingly used as OCR systems in document and knowledge-processing pipelines, but their abi…

Denoising-Aware Inversion: Revealing Privacy Risks in Noise-Protected Text Embeddings

Dense text embeddings are widely used in data mining, retrieval, and downstream machine learning systems due to their compact and semantica…

Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation

Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measuremen…

Orienteering Problem with Uncertain Time-Varying Rewards: Framework and Benchmark for Everyday Service Robotics

We present the orienteering problem with uncertain time-varying rewards (OP-UTVR), a novel variant of the orienteering problem (OP). While…

Aslema at NADI 2026: Augmentation through Fewshot for SLU

We present Aslema, our system for NADI 2026 Shared Task 5, which consists of two subtasks: intent recognition and slot filling. We evaluate…

Europe's Climate Ambition Under Scrutiny: Evidence from Deep Learning Emission Projections

The European Union has committed to reducing greenhouse gas emissions 55% below 1990 levels by 2030, but whether current trends are compati…

Composed Historical Image Retrieval by Modeling Temporal Representations

While time evolves linearly, the geometry of neural embedding spaces is inherently multi-dimensional, often chaotic, and difficult to inter…

Impact of Iterative Fine-Tuning on Transcription Accuracy in Complex Historical Sanskrit Manuscripts

Digitizing the text from handwritten historical manuscripts is required to make them easily accessible, preservable, and to enable historic…

MemFuse: Multi-Source Memory Fusion from Fragmented Observations

Long-term memory is essential for agents that operate across extended interactions, yet existing memory systems and benchmarks predominantl…

A Critical Synthesis of Uncertainty Quantification and Foundation Models for Semantic Segmentation

Foundation models are increasingly breaking what seemed to be impossible not long ago by enabling unprecedented accuracy and cross-domain g…

The Impact of CutMix on Reliability and Robustness in Semantic Segmentation

Ensuring not only high accuracy but also reliable and robust predictions is critical for the deployment of semantic segmentation models in…

Budget-First Tariff Recommendation (BFTR): A Complete Algorithmic Framework for Telecom Plan Recommendation without Overcharging

Telecom operators traditionally offer predefined tariff grids, forcing users to choose from a limited set of plans. This paper proposes BFT…

A Few Cases Are All You Need: An Empirical Study of Annotation-Efficient LoRA Fine-Tuning of MedSAM3

Medical image segmentation is essential for clinical workflows such as treatment planning and disease assessment. While specialist tools li…

Flama: a Python framework for development and deployment of production-ready APIs, machine learning, and LLM services

We present Flama, an open-source Python framework for developing and deploying production-ready web APIs, machine learning services, and la…

Epistemic Subordination: Generative AI and the Infrastructure of Knowledge

Generative AI does not merely produce biased outputs. It encodes the majority's way of knowing as the default infrastructure of knowledge i…

Beyond Predictive Fairness: Quantifying Attribution Consistency Across Demographic Groups in Diabetic Retinopathy Screening

Fairness in medical imaging is commonly evaluated through subgroup performance metrics, yet it remains unclear whether models rely on consi…

SIDScope: A Diagnostic Resource for Semantic-ID Interfaces in Generative Recommendation

Semantic-ID mappings are reusable interfaces between item tokenizers and generative recommenders, yet released mappings rarely state whethe…

Decomposing Wrong-Consensus Agreement in LLM Self-Consistency: A GPT-4.1 Case Study

Majority voting over multiple LLM samples is widely used to raise answer accuracy, yet its gain varies erratically: on hard questions it ca…

Forgetting, plasticity, and co-observation: a third facet of continual learning

Efficient continual learning remains a fundamental challenge for deep neural networks. While catastrophic forgetting and loss of plasticity…

A strengthening of the MCFL-ness of $O_2$

In the last years, a number of proofs of the fact that $O_2$ is a multiple context-free grammar (MCFG) were given. Such results can be expl…

Do Large Language Models Hallucinate Electric Fata Morganas?

AI hallucinations - that is, outputs which are made up, cannot be verified, or contradict the source material - are generally regarded as a…

Identifying Implicit Premises for Logical Reconstruction of Argument Graphs

The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., argu…

Understanding Multilingual Medical ASR Adaptation Through Layer-Wise Analysis

Medical automatic speech recognition (MedASR) requires adaptation to specialised terminology, limited annotated clinical data, and multilin…

MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models

Reward function design remains a bottleneck in reinforcement learning. While large language models (LLMs) have enabled automated reward gen…

Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets

Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained…

SMTrap: Cost-Effective DoS Attacks Against Large Reasoning Models via SMT Conflict Guidance

Existing LRM-DoS methods rely heavily on model feedback to synthesize attack queries, requiring either repeated queries to the target model…

Test-Time Scaling in the Wild: Why Exploitation, Not Exploration, Is the Bottleneck

Test-time scaling (TTS) improves language model outputs by spending additional inference compute - generating multiple candidates, searchin…

SkillForge: Self-Distilling Agents for Project-Specific Issue Resolution

Large language model (LLM) based agents have demonstrated remarkable proficiency in automated software issue resolution, yet they often str…

Graphical Design of Interpretable Architectures

Designing, implementing, and comparing interpretable architectures requires a formal language to represent them. The most common representa…

MedUAG: Unified Understanding and Generation for Medical Multimodal Models

Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, e…

Training Chemical Plausibility-Aware Large Language Models for Single-Step Retrosynthesis

Single-step retrosynthesis is a central component of computer-aided synthesis planning, yet its intrinsically one-to-many nature is poorly…

AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL

Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault…

rEDMRec: Distilling Large Language Model Reasoning into an Editable Experience Memory for Recommendation

Large language models can improve recommendation quality by reasoning explicitly over user history and candidate items - for example, extra…

DeepWeaver: Bridging the Evidence Synthesis Gap in Open-Ended Question Answering

Retrieve-then-generate pipelines are commonly used to produce deep-research answers for open-ended questions, but retrieval alone is insuff…

GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery

Visual grounding in Unmanned Aerial Vehicle (UAV) imagery aims to localize a target object in complex bird's-eye-view scenes according to a…

From Threat Intelligence to Detection: Knowledge-driven Enrichment and Template-based Rule Grounding for Automated Sigma Rule Generation

Mechanisms for dynamically converting cyber threat intelligence (CTI) into actionable detection capabilities are necessary due to the rapid…

Harness Continual Learning: Continual Adaptation Beyond Model Parameters

Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern a…

One-Stage Object Detectors in Autonomous Driving

Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, an…

Counterfactual Contrastive Analysis

Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an inpu…

Bernstein-Vazirani Networks: Quantum Machine Learning by Interference

We introduce Bernstein-Vazirani Networks (BVNs), a non-variational quantum machine learning framework that leverages quantum interference f…

GS-VLA: Plug-and-Play Viewpoint Canonicalization for Frozen VLA Policies via Gaussian Splatting

This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in Vision-Language-Action (VLA) pol…

ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models

Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content d…

DA-WAM: Decision-Aligned Future Latents for Driving World Models

Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for deci…

Detecting Backdoors in Object Detection via Pre-NMS Prediction Distribution Shift

Object detection models deployed in safety-critical applications remain vulnerable to backdoor attacks that cause targeted misbehaviors whe…

Open-MOPD: Diagnosing and Fixing Capability Imbalance in Multi-Teacher On-Policy Distillation

Multi-teacher on-policy distillation (M-OPD) has emerged as a promising paradigm for consolidating domain-specialized reinforcement learnin…

Discretizing Continuous Time Series for Imputation with Masked Diffusion Training

Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics…

PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints

Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, mole…

Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers

Astrolinguistics -- communication with minds that categorize reality differently from ours -- has been purely speculative since Freudenthal…

Leaf Values as Coordinates: Exact Contrastive Explanation for Gradient-Boosted Ensembles

A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate resul…

Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets

Modern Intel AI PCs ship capable integrated GPUs and NPUs with 16+ GB of unified memory, and they spend considerable time idle. That is not…

Interpretable AI predicts a 2026 summer dry anomaly in central China

Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability…

Finetuning Strategies for Querying Sounds by Vocal Imitation

This technical report describes our winning submission to the AES AIMLA 2025 Challenge on querying sound effects by vocal imitation. We inv…

Beyond Teacher Likelihood: Group-Calibrated On-Policy Distillation for Long-Context Reasoning

On-policy distillation (OPD) trains a student on its own responses using dense token-level guidance from a stronger teacher. In long-contex…

ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning

We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real…

SPADE: Self-Play in Adaptive Synthetic Executable Environments

Continuous self-improvement requires an ever-expanding pool of self-generated, diverse, adaptive goals. For language agents, existing train…

Hybrid Reinforcement Learning and Search for Flight Trajectory Planning

This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight pa…

Conformal Policy Control

An agent must try new behaviors to explore and improve. In high-stakes environments, an agent that violates safety constraints may cause ha…

SkillNet: Create, Evaluate, and Connect AI Skills

Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systemati…

From Multi-Agent to Single-Agent: When Is Skill Distillation Beneficial?

Multi-agent systems (MAS) for structured data-science tasks externalize analytical control through workflows spanning stages, tools, shared…

Interval POMDP Shielding for Imperfect-Perception Agents

Autonomous systems that rely on learned perception can make unsafe decisions when sensor readings are misclassified. We study shielding for…

When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition

Automatic speech recognition (ASR) systems remain brittle on dysarthric and other atypical speech. Recent audio-language models raise the p…

Event-Causal RAG: A Retrieval-Augmented Generation Framework for Long Video Reasoning in Complex Scenarios

Large vision-language models perform well on short- and medium-length video understanding but still struggle to maintain coherent event mem…

MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance

LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions. To…

RULER: Representation-Level Verification of Machine Unlearning

Machine unlearning aims to remove the influence of specific training records from a deployed model without retraining from scratch. Current…

A Framework for Measuring Appropriate Reliance on Set-Valued AI Advice

Appropriate reliance on AI advice has become a central research theme in human-AI collaboration. Existing frameworks have focused exclusive…

Teaching agentic AI to learn expert reasoning for rare disease diagnosis

Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) ran…

ChainWorld: Composing Long-Horizon Desktop Workloads from Atomic OSWorld Tasks

Computer use agents are evaluated almost exclusively on atomic desktop tasks, but realistic desktop work requires sustaining state across m…

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad search…

ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System

Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automa…

Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations

Natural-language autoencoders score explanations of hidden activations by reconstruction. An explanation is deemed faithful if the activati…

Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting

Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-wo…

Fragility of Value under Imperfect Alignment

As more responsibility is placed upon AI systems, it becomes increasingly important to guarantee that these systems are aligned with humani…

Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks

Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous…

BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding

Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-la…

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may…

Automated Computational Energy Minimization of ML Algorithms using Constrained Bayesian Optimization

Bayesian optimization (BO) is an efficient framework for optimization of black-box objectives when function evaluations are costly and grad…

`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs

As large language models (LLMs) are increasingly integrated into audio-based applications, growing concerns have emerged regarding their vu…

Iterative Flow Matching: Path Correction and Gradual Refinement for Enhanced Generative Modeling

Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for e…

Jailbreaking in the Haystack

Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks…

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for hi…

Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead

Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI),…

Professional Software Developers Don't Vibe, They Control: AI Agent Use for Coding in 2025

The rise of AI agents is transforming how software can be built. The promise of agents is that developers might write code quicker, delegat…

Evaluating Music Context Preservation: A Multi-facet Framework for Music Editing Systems

Music editing plays a vital role in modern music production, with applications in film, broadcasting, and game development. Recent advances…

TrojanGYM: A Detector-in-the-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Hardware Trojans (HTs) remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small…

FiLoRA: Focus-and-Ignore LoRA for Controllable Feature Reliance

Multimodal foundation models integrate heterogeneous signals across modalities, yet it remains unclear whether their predictions can be con…

Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition

Accurate channel state information in wideband MIMO systems is constrained by pilot overhead, a challenge intensifying as bandwidths scale…

Whole-Piece Training for Symbolic Music Language Models via Full-Horizon Compressed Recurrence

For computational efficiency, modern language models are typically trained on independently sampled fixed-length sequences. Symbolic music…

Making Implicit Premises Explicit in Logical Understanding of Enthymemes

Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural langua…

A Framework and Prototype for a Navigable Map of Datasets in Engineering Design and Systems Engineering

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Sy…

Wildfire Suppression: Complexity, Models, and Instances

Wildfires cause major losses worldwide, and the frequency of fire-weather conditions is likely to increase in many regions. We study the al…

When to Call an Apple Red: Humans Follow Introspective Rules, VLMs Don't

Understanding when Vision-Language Models (VLMs) will behave unexpectedly, whether models can reliably predict their own behavior, and if m…

AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

Optimization problems are central to decision-making in manufacturing, logistics, scheduling, and other industrial settings. Translating co…

MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports

Semi-structured information extraction (IE) from OCR-derived clinical reports is crucial for efficiently reconstructing patients' longitudi…

Key Coverage Matters: Semi-Structured Extraction of OCR Clinical Reports

Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sh…

EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding

Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire d…

ICICLE: Expanding Retrieval with In-Context Documents

Generative retrieval (GR) maps queries directly to document identifiers (docids) using parametric knowledge, However, this design makes cor…

DELOS: Contrastive Deep Learning for Low-SNR Blind Transit Searches in Kepler Photometry

We present DEtection in phase-folded Light curves with cOntrastive Scoring (DELOS), a deep-learning framework that uses contrastive scoring…

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis

Language models fine-tuned where the correct completion must outrank a near-synonym competitor often fail silently. The cross-entropy loss…

Sensory Restoration via Brain-Computer Interfaces: A Scoping Review

Brain-computer interfaces (BCIs) can restore sensory and motor function in individuals with severe neurological impairment, but the literat…

Demystifying Training-Time Augmentation for Data-Constrained Language Model Pretraining

As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining…

Horizon-Uniform Sensitivity and Decay of Terminal Reward Perturbations in Discrete-Time Pontryagin Systems

We study local stationary solutions of finite-horizon discrete-time Pontryagin systems near a steady extremal. Suppose that the stationarit…

Hybrid ANN-SNN Pipeline with Local Plasticity

This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs)…

First-Token Broadcasters: Mechanistic Origins of Language Identity and Distributed Robustness in Transformers

Why do multilingual language models sometimes generate in the wrong language, and why is this so hard to fix? We introduce Language Identit…

Mask2Real-WM: Segmentation Masks as a Sim-to-Real Bridge for Controllable Dexterous World Models

Action-conditioned world models allow robots to predict the future consequences of candidate actions without additional physical interactio…

Hierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO)

FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinic…

LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4

We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture de…

RouteCost: A Production-Inspired Multi-Stage Framework for Pre-Order Shipping Cost Estimation in E-Commerce

Accurate pre-order shipping cost estimation is important in e-commerce because it affects price presentation, margin planning, and conversi…

Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting

Existing patching and multi-scale methods advance multivariate time series forecasting but treat learned representations as transient bypro…

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distribu…

Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models

Synthetic tabular data are valued for preserving not just column-wise marginals but inter-column dependency. Yet the most commonly reported…

Cross-Cohort Spectral-Temporal Dissociation in Frozen EEG Foundation-Model Representations

Objective. We tested whether frozen representations from five EEG foundation models support decoding of long-range temporal correlations, m…

Untrainable elements determine what physical learning remembers

Physical learning rules such as equilibrium propagation (EP), coupled learning (CL), and adjoint coupled learning (AL) train resistive netw…

The Epistemic Politics of AI Anthropomorphism

AI anthropomorphism is typically treated as a problem of user misperception requiring institutional correction. Users who engage in sustain…

Approximate Speculative Decoding

Speculative decoding accelerates autoregressive generation by verifying a draft block with a target model in parallel. Under standard greed…

Complete, Scalable, and Robust Prioritized Planning for Multi-Robot Ordered Storage and Retrieval at Maximum Capacity

Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput. While puzzle-…

Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol

AI tools that help people judge online claims are usually evaluated while the tool is present. This paper asks a different question: after…

BrainWAM: Action-Space Coordination of Semantic Priors and Predictive Dynamics for Autonomous Driving

Autonomous driving requires planning under both semantic constraints and predictive dynamics. Existing end-to-end driving approaches, howev…

「AIで生産性向上」日本の従業員は57％、世界平均は81％　仕事の満足度でも大差──アクセンチュア調査

アクセンチュアの世界20カ国調査で、AIによる生産性向上を実感する日本の従業員は57％と、世界平均の81％を大きく下回った。仕事の満足度や成果の実感でも世界との差が開き、人材と組織の変革が課題となっている。

Stripe、AIモデルゲートウェイのOpenRouter買収　400以上のAIモデルを束ねる中立基盤は維持

Stripeは、AIモデルのゲートウェイを手掛けるOpenRouterを買収することで合意したと発表した。報道による買収額は約75億ドル。OpenRouterは単一APIで400超のモデル切り替えを可能にする。Stripeはトークンコスト最適化を強化し、OpenRouterは買…

OpenAI、ZDRを維持したまま悪用検知へ　ログ保持を求めるAnthropicに対抗

OpenAIは、APIデータの非保持設定（ZDR）を維持したまま、複数のやり取りを横断して悪用の兆候を検知する新機構「Private Safety Processing」を発表した。顧客データを自社インフラ外や暗号化で保護しつつ、活動シグナルのみでリスクを判定する。9月に展開を…

PTC、「Onshape」でMCP連携　自然言語でカスタムCAD機能の作成が可能に

PTCは、CAD／PDMプラットフォーム「Onshape」において新機能「FeatureScript MCP Server」の提供を開始した。エンジニアが自然言語とAI（人工知能）を用いて、カスタムCAD機能を作成できるようにする。

Stripe didn’t really buy OpenRouter because of the ‘singularity’

What does a payments giant want with a startup that routes prompts between different AI models? Stripe says it's because of "the singularit…

モデルの利用料金は安くなっているのに、AIの総コスト上昇　「パラドクス」の背景を解説

AIモデルの利用料金の低下が、かえってAIの総コストを押し上げている。Gartnerはワークフロー1件当たりのAI推論コストが2028年までに5倍以上に上昇すると予測する。同社が「推論のパラドックス」と呼ぶ、この逆説の中身とは。また、AIコストが上昇する中でROIを確保するため…

高度なAIのサイバー攻撃、どう対策？　企業が知るべき「スピード格差」の埋め方

米AnthropicのAIモデル「Claude Mythos 5」をはじめとした高度なAIの登場により、サイバーセキュリティの在り方に注目が集まっている。企業に求められる対応を解説する。

Google、大学生向けに「Google AI Plus」を1年間無料提供　「Gemini」アプリに学生向け新機能も

Googleは米国の新学期に合わせ、大学生向けに「Google AI Plus」などを12カ月無料で提供するキャンペーンを発表した。あわせてGeminiアプリに学生向けハブを新設し、授業資料から学習プランを生成する学習ノートブックや、3Dモデルの表示、Gemini Liveでの…

OpenAI seeks to one-up Anthropic with new customer privacy protections

A competition is developing between OpenAI and Anthropic over who can provide the best privacy protections for enterprise customer data.

Cognition CEO denies report that SpaceX tried to acquire the startup

SpaceX was reportedly in talks to buy AI coding startup Cognition. SpaceX has already acquired Cursor as it races to catch up to rivals lik…

Waymoが自動運転AI戦略を解説、「単一AIモデルのE2E方式には2つの問題がある」

米国で自動運転車によるモビリティサービスを展開するWaymo（ウェイモ）が、2009年スタートの「Google Self-Driving Car Project」から開発を積み重ねてきた自動運転技術に基づく同社のAI戦略について説明した。

AI was supposed to win people over by now — it hasn’t

As AI becomes harder to avoid, consumers are growing more wary of the technology — and Silicon Valley is discovering that widespread adopti…

Google packs Search and Gemini with new AI study tools

The launch of the new study features marks Google's latest effort to make Gemini the AI assistant that students turn to when learning and s…

Offering Zero Data Retention for frontier models

OpenAI reaffirms Zero Data Retention for eligible API customers and previews Private Safety Processing for advanced AI safety without compr…

Researchers say OpenAI revoked their access to limited cyber program

The idea behind OpenAI's Trusted Access for Cyber program is to give trusted defenders better models so they can report bugs and vulnerabil…

Meet the startup helping Wall Street put a price on AI compute

The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has be…

TerraPower’s nuclear reactor has a secret weapon for powering AI data centers

TerraPower's nuclear power plant possesses a strategic advantage over competitors, especially when chasing after data center deals.

Amazon makes its AI-powered Alexa+ free on Fire TV, no Prime required

Amazon is making its AI-powered Alexa+ assistant free on all compatible Fire TV devices in the U.S., automatically upgrading users whether…

Calendly throws its hat into meeting note-taker circus

Calendly is also releasing a meeting scheduling assistant called Callie.

AI isn’t close to curing cancer. This startup says it knows what it will take.

It's the data, stupid.

Relativity Networks raises $22 million to bring a faster kind of fiber to data centers

Relativity Networks deals in hollow-core fiber, a rarely deployed technology that allows data to be transmitted 30% faster than conventiona…

ChatGPTの反論で「道徳的判断」の3割超が覆る　高齢者が説得されやすい傾向　神戸大

生成AIの反論によって道徳的な判断の3割超が覆る――神戸大学がこのような研究結果を発表した。米OpenAIのチャットAI「ChatGPT」を利用し、AIの反論が正解のない道徳問題への回答に与える影響を調べた。

Replit expands access to software creation with GPT-5.6 Luna

Replit introduces Free Mode, powered by GPT-5.6 Luna, so anyone can turn ideas into working software without worrying about token costs.

「オープンな国産モデル」に33Bパラメータの新バージョン　国立情報学研究所

国立情報学研究所（NII）が、オープンな国産LLMの新バージョン「LLM-jp-4 33B」を公開。約332億パラメータのDense型モデルで、4種類のベンチマーク全てで従来モデルを上回るスコアを記録したという。

「AIを使える人か、使えない人か」で仕事や評価に差が？　6割のエンジニアが実感した“AI格差”の正体

AIを使うかどうかだけではなく、どの程度使いこなせるかも問われる中、活用スキルの差は業務効率だけではなく、仕事やキャリアにも影響し始めているという。何が起きているのか。ITエンジニア572人の調査から探る。

GxP-Agent: Process-DAG Topology for Reliable Clinical Trial Programming with LLM Agents

Clinical trial programming -- transforming study protocols into analysis-ready datasets under CDISC standards -- is a bottleneck in regulat…

Runtime Governance for Agentic AI: Action-Boundary Control with Trusted Provenance and Fail-Closed Execution

Agentic AI systems request tool actions that can modify files, send messages, launch jobs, or change workflow state. This shifts the safety…

The Price of Thinking: Reasoning Effort as a Model-Specific API Contract

API buyers purchase a dated contract, not a model name alone: the contract includes the requested and served model, reasoning-effort term o…

FedPref: Federated Preference Learning for Structured Radiology Report Extraction

Radiology reports describe findings and locations in free text, but downstream search and analysis require these relations in a fixed schem…

The Problem Is the Problem: Towards Scalable Mathematical Discovery

AI systems are increasingly capable of contributing to mathematical research. In research practice, frontier-model reasoning is a limited r…

SkillEffect: Checked Lowering for Memory-Bounded Agent Tools

Agent Skills can specify procedural and resource obligations for tool use, and language models instantiate them as concrete programs. Howev…

Memory Is Communication: The Frontier Between Remembering and Signaling

A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history…

DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization

As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) co…

KernelArc: A Multi-Agent Framework for GPU Kernel Optimization

We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized a…

A decodability criterion predicts when hidden-state selection beats majority voting in large language models

Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usua…

Toward Personal Intelligence Through Cooperative Observation

A personal AI system needs a model of the user's goals, constraints, and ongoing commitments to plan and act on their behalf, and the quali…

KnowSim: Evaluating Information Calibration in LLM Assistants with User Simulators that Learn

To effectively collaborate with users on knowledge-intensive tasks, Large Language Models (LLMs) must perform information calibration: matc…

Synthesizing Feature Extractors: An Agentic Approach for Algorithm Selection

Algorithm selection for constraint satisfaction problems requires extracting features that capture problem structure. Manually designing fe…

Benchmarking the Benchmarks: Evaluating Automated Safety Benchmarks for Small Language Models

Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures…

Fool's Gold: Defensive Deception Against Safety-Removal Attacks on Open-Weight Models

Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weig…

Explicit State Elicitation Is Not Enough: A Controlled Audit of Memory-Policy Classification

Personalized agents must decide whether retrieved user memory should be used, ignored, updated, or queried before it affects a current task…

Do LLMs Know a Good Hypothesis When They See One? Logit-Based Energy Scoring Outperforms Prompted LLM-as-Judge for Scientific Hypothesis Ranking

Large language models (LLMs) are increasingly used for scientific hypothesis generation. However, evaluating generated hypotheses remains a…

ASI-Bench: At the Dawn of Artificial Superintelligence

Artificial superintelligence (ASI) requires AI to move beyond mastering existing knowledge toward exploring the unknown, creating new knowl…

DeAR: Decentralized Agentic Reasoning via Capability Grounding and Collaborative Thought Navigation

Existing agentic reasoning systems typically rely on centralized protocols. This design introduces routing bottlenecks and static role allo…

PlanPO: Group Planning-Aware Policy Optimization for Multi-Turn Agentic LLMs

Group-relative policy optimization has emerged as a key paradigm for training agentic large language models (LLMs) on multi-turn interactiv…

LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models

Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However,…

SignalReasoner: Assessing the Upper Bound of 3B Models for Signal Mathematical Reasoning

Post-training with supervised chain-of-thought fine-tuning and reinforcement learning from verifiable rewards has substantially improved th…

Wuying-Browser-Agent: Real-World Centric Fundamental Long-Horizon Browser Agents

Browser agents perform well on short, clean demonstrations, but real deployment is fundamentally different: agents must sustain dozens of d…

LLMs for Medical Consultation Are Evaluated Too Late: The Preformulation Gap

Large language models for medical consultation are often evaluated after a clinical problem has already been made clear, although real cons…

TileMix: Tile-Centric Mixed-Precision Attention for LLM Inference Acceleration

Long-context prefill in large language models (LLMs) incurs substantial computation and memory traffic because dense self-attention compute…

LLM-Only PDDL Domain Repair with Open-Weight Models

AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, comm…

Cognitive Graph Intelligence for Adaptive and Robust DDoS Attack Detection in Next Generation Networks

Distributed Denial-of-Service (DDoS) attacks threaten network availability, requiring a cognitive detection process that senses traffic, in…

LEGO-RL: Harness-Native Reinforcement Learning for Coding Agents

Reinforcement learning for coding agents increasingly relies on long-running agent harnesses to manage tool integration, repository context…

Task-Aware Harness Provisioning for LLM Agents in Mission-Critical Infrastructure Operations

LLM agents have been widely adopted to operate mission-critical infrastructure (MCI). These agents normally rely on a harness that determin…

Depth Enables Local Entropy: Quadratic Depth Dependence in Deep Variation-Norm ReLU Regression

We study Gaussian regression over the explicit vector-valued Parhi--Nowak deep-RBV^2 architecture with depth L, width w, layer-sum variatio…

Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence available in KGs, posing a challenge to…

SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution

Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit direct…

When AI Designs AI: Innovation or Imitation?

Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questi…

Towards Better Agents for Multi-Turn User Interaction: The Next User Turn Is More Than Context

User-facing tool agents must coordinate dialogue and tool use as user goals unfold over multiple turns. Yet interactive reinforcement learn…

SGHA: Evidence-Grounded Research Problem Discovery with Local Language Models

Recent efforts toward fully automated AI scientists have demonstrated that language-model agents can generate hypotheses, execute experimen…

Agent Lightning v1.0: Towards Harnessed Agentic RL

Modern agents operate inside agent harnesses that manage tools, context, and control flow, making the harness a critical part of the agent…

When to Review: Spaced Repetition for Continual Pre-Training of Language Models

Continual pre-training of large language models must acquire new information without erasing old knowledge. Existing replay methods often c…

Quantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making

How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), whic…

TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation

Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capa…

MoNe: Modular Neural Memory for Efficient Long Context Inference

We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference wi…

Validated Adaptation for Aerial Crowd Monitoring at Mass Gathering Scale: A Deployment Protocol, a Severity Law, and a Diagnostic for Label-Free Drone Crowd Counting, Toward the FIFA World Cup 2034 (Saudi Arabia)

Saudi Arabia will host the 2034 FIFA World Cup and already operates crowd management at Hajj scale. Drone-based counting must hold accuracy…

Graph Surgery and the Do-Operator: A Precise Correspondence for Acyclic Structural Causal Models

The $\operatorname{do}$-operator is described graphically by deleting arrows into its targets and functionally by replacing their mechanism…

Beyond the Trace: Coupling an Interpretable Reasoning-State Readout to Native MoE Routing

What a reasoning model writes is only a partial record of the process that produces it. We introduce a two-level internal readout for mixtu…

LLM-Derived Preference Judgments Are Not Self-Consistent

Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by askin…

GraphWake: Group Polarization via Memory-Mediated Polarization Cascade in LLM-Agent Communities

LLM-driven agents can autonomously exchange opinions on online platforms and form communities. Such agent-operated social platforms raise a…

Auditing Self-Evolution in Financial Agents: Capability Gains, Security Drift, and Execution-Interface Mismatch

Self-evolving agents turn experience into reusable skills, workflows, or memories, but post-evolution accuracy alone does not show whether…

Mixture-of-Expert Blocks Contain Strong Hallucination Detection Signals

Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false c…

Accuracy and Robustness of Model Cascades Under Data Perturbations

Prediction cascades significantly reduce energy consumption of Artificial Intelligence (AI) models while maintaining high predictive perfor…

Beyond Suspicious Steps: Ontological Trust in Long-Horizon Agents

Long-horizon agents increasingly operate across many steps, tools, and observa- tions. In this setting, the relevant oversight question is…

Evaluating the Diversity of AI-Generated Content with Diversity Profiles

Diversity is a fundamental criterion for evaluating generative artificial intelligence (AI) systems, yet its measurement remains inherently…

Neuro-symbolic learning over OWL 2 DL via consequence-based compilation to differentiable circuits

OWL 2 DL ontologies, grounded in the description logic $\mathcal{SROIQ}$, express large knowledge bases in biomedicine and the Semantic Web…

The Curious Case of Exploding DecPOMDPs: Containing the Fire through Policy Counting

Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision mak…

D$^2$ACCI: A Dual-Loop Diagnostic Protocol for Evidence-Preserving Agent Memory

Memory is a key capability of LLM agents. Persistent memory extends this across sessions---enabling recall, revision, and personalization.…

StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows

Recent advances in Large Language Models(LLMs) and agents have substantially improved the ability of AI systems to execute complex tasks. Y…

ARASH: Adaptive Retrieval And Shot Selection for Tabular Prediction

Tabular prediction is a critical task across numerous applications. The recent success of large language models has sparked various approac…

AutoResearch: Insight In, Hallucination Out

Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the re…

Adaptive Policy Portfolios for Robust Markov Decision Processes

Robust Markov decision processes optimize one policy against a set of plausible transition functions. This can be conservative when the unk…

EvoTS-Agent: A Self-Evolving LLM Agent for Financial Time Series Change Point Detection

Financial time series exhibit non-stationary and heterogeneous statistical properties, making change-point detection challenging because no…

Procedural Content Metageneration via Program Search and Continual Abstraction Discovery

Large language models can generate executable programs, which makes it possible to search directly over procedural content generators rathe…

Towards Zero-Shot Task Transfer with Neurosymbolic World Models

State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent…

Can Large Language Models Explain Flight Safety Events? A Prior-Guided Semantic LLM-based Approach

Improving flight safety with flight data requires not only accurate detection of risk events, but more importantly, clear interpretation of…

StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents

AI agents increasingly perform knowledge work (i.e., produce and modify persistent digital artifacts such as code repositories, documents,…

HLSR: Hybrid Live Forecast Selective Dynamic Vehicle Rerouting for Real-Time Congestion Avoidance

Urban traffic congestion reduces productivity and increases travel cost and emissions. Network-wide live travel-time shortest-path reroutin…

Delegation Asymmetry in Agentic Recommender Systems: Measuring Two-Sided Receptivity in Online Dating

Autonomous LLM agents that converse on a user's behalf are an emerging design pattern in matching platforms, yet their viability depends on…

On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification

Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory b…

Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis

The rise of ChatGPT has brought a notable shift to the AI sector, with its exceptional conversational skills and deep grasp of language. Re…

Intent-Driven Dynamic Chunking: Segmenting Documents to Reflect Predicted Information Needs

Breaking long documents into smaller segments is a fundamental challenge in information retrieval. Whether for search engines, question-ans…

The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks

Repository-scale coding requires an agent to keep tests, imports, configuration, and migration rules consistent within a bounded context wi…

A Framework for Using and Evaluating LLMs as Surrogate Experts in Security Surveys: Reliability, Bias, and Implications

Expert surveys are widely used in security research to study practitioner workows and decision-making, yet recruiting domain experts - espe…

What If AI Carried Her Imagination? Black Girls as Creators in an AI Storytelling Weekend Program

This paper presents the design and outcomes of a seven-weekend AI storytelling program developed for Black girls aged 10-12. Grounded in Af…

CityReal: Human-Aligned Urban Behavior and City Dynamics Simulation with Large-Scale LLM Agents

Large-scale urban simulation plays a pivotal role in social science, traffic safety, and transportation policy. Recent work has shown that…

QuantumNovelty: A Skill-Orchestrating Language Agent for Referee-Style Review and Patentability Screening of Quantum Papers and Patents

Language-model agents increasingly produce quantum-science results; we ask whether the same agentic paradigm can also scrutinize them in an…

AI, Brain Death Detection, and Islamic Law

The deployment of machine learning systems capable of detecting covert consciousness in neurologically injured patients creates a profound…

ComNetX: Local Hierarchical Adaptation for Dynamic Community Detection

Dynamic community detection is commonly addressed either by full-snapshot recomputation or by solver-specific dynamic procedures. Full reco…

Effective Personalized AI Tutors via LLM-Guided Reinforcement Learning

Generative AI (GenAI) is rapidly reshaping education by unlocking the potential for personalized tutoring. Yet, emerging platforms largely…

When Personalization Becomes Bias: Structural and Discursive Religious Framing in AI-Generated Financial Advice

Large language models (LLMs) are increasingly integrated into financial advisory systems, yet their role in reproducing religious bias rema…

Education-centered critical policy analysis of AI: Ghana's AI strategy as a case

National AI strategies increasingly guide governance, workforce development, innovation, and competitiveness, but less is known about how t…

Average Distance Approximation for Static Large Graphs

Calculating average distances in large-scale networks is computationally intensive and constrained by limited main memory, posing a signifi…

Sparse Coverage: Semantic Center Representations for Patent Prior-Art Retrieval

Patent prior-art retrieval is a recall-oriented search task over long and highly structured technical documents. Dense retrieval improves s…

CARA: Cognitive Adaptive Recommendation Agent

Recent advances in large language models and agent-based recommendation frameworks have introduced new opportunities for more flexible and…

WIP: LLM Odyssey: A Game-Based Platform for Teaching LLM Engineering Concepts

This work-in-progress (WIP) innovative practice category paper presents LLM Odyssey, an open source, browser-based serious gaming platform…

EMAN: Optimization-Driven Capacity Growth through Path Emergence in Multi-Task Learning

Existing multi-task learning methods rely on hard sharing, multiple paths or experts, adaptive sharing, and dynamic expansion. However, the…

Probing the Prefill: Detecting Code Vulnerabilities via Latent Activations

LLM-based code generation is now embedded in mission-critical pipelines, but defenses against vulnerable output remain post-hoc -- static a…

Position: Fairness Failure in Generative Models is an Evaluation Problem

Despite groundbreaking advancements in generative models during the last decade, concerns about their lack of fairness, reinforcing societa…

PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation

Recent monocular depth estimators achieve strong zero-shot generalization, yet often struggle to preserve fine-grained structures and objec…

Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media

The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and t…

YILDIZ-VPR: A Novel Dataset with Dense Coverage Under Diverse Environmental Conditions for Visual Place Recognition

Visual Place Recognition (VPR) aims to recognize the location of a query image by comparing it with a set of geo-referenced images. Althoug…

The 10th AI City Challenge

The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and…

Cross-Model Memory Transfer via Target-Side Reader Adaptation

Methods for improving knowledge use in large language models typically fall into two regimes. Non-parametric retrieval offers flexible acce…

Institution-Specific LLM Prompting Recovers PHI That De-identification Systems and Their Gold Standards Both Miss

Secondary use of electronic health records requires de-identification, yet existing systems miss \emph{institutionally situated} protected…

Foundation Agents Meet Agentic Deep Research: Evidence-Grounded Clinical Code Forecasting

Next-encounter ICD forecasting predicts which standardized diagnosis codes will be documented at a future visit from the longitudinal recor…

Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection

Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induc…

From Abductive Explanations to Global Logical Rules for Node Classification in SGCs

Graph Neural Networks (GNNs) have achieved remarkable performance in node classification tasks, motivating growing interest in methods capa…

Iterative tensor network transformations for element-wise evaluation of elementary and filtering functions

Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limit…

Authorization Before Context: A Model-Neutral Audience Boundary Against Cross-Audience Memory Leakage in Agentic Systems

A personal language agent learns a fact from one audience and may later place it in the prompt it assembles for another. This memory-to-con…

Q-Learning With World Models

Off-policy reinforcement learning (RL) has become increasingly sample-efficient, enabling applications such as RL fine-tuning of Vision-Lan…

Expected free energy as an information constraint on the Bethe Lagrangian

Active inference selects actions by minimising an expected free energy functional over predicted futures. However, adding an expectation ov…

Can LLMs Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases

Reasoning has become a standard technique and feature for contemporary LLMs; however, its application and quality in the context of demandi…

The Acknowledgment Point Is the System: Durable Policy-Decision Receipts for AI Audit Evidence

An AI audit record is useful only if its durability and trust boundary are explicit. Returning a guarded decision before any durable write…

Task Specialization Fine-Tuning for Contextual Reinforcement Learning

Contextual Reinforcement Learning (CRL) seeks to generalize classical RL by maximizing task coverage across a context space of related task…

Token Optimization and Context Window Management in Multi-Agent AI Workflows

Multi-agent AI workflows are limited not only by model quality but by token cost, latency, and context-window quality. This paper presents…

Graphectory Viewer: A Tool for Process-Centric Analysis of Agentic Software Trajectories

We present Graphectory Viewer, a web-based tool for interactive, process-centric analysis of software-agent trajectories. Building on the G…

Teach and Grow: An Agent-Centered Architecture for General Robot Learning

End-to-end vision-language-action (VLA) and world-action models offer an elegant route to general-purpose robotics, but their reliability i…

PACE: Policy-Attested Contract Execution for Safe AI Agents in Decentralized Finance

Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield manag…

Delta2Gamma: Band-Wise Adaptive Contrastive Learning of EEG for Alzheimer's Disease Detection

Low-cost, scalable screening for dementia remains an open problem. Imaging-based diagnosis is costly and hard to deploy widely. Electroence…

COMIC: Reference-Aware Safety Gating for Multimodal Large Language Models

Multimodal large language models (MLLMs) are increasingly used to interact with screenshots, scanned documents, diagrams, and other visuall…

Structural Plan-to-Model Conversion with Deterministic Geometry and Guarded Agentic Vision-Language Refinement

Converting structural framing plans into editable finite-element model drafts remains labor-intensive and prone to transcription error. Exi…

Maximum Tsallis Entropy Distributions for Robust and Efficient Sparse Learning from Correlated Data

This paper addresses the limitations of Gaussian distribution assumptions in statistical sparse learning, particularly in modeling correlat…

Adaptive surrogate modeling for high-dimensional spatio-temporal output

This paper develops an adaptive surrogate modeling method for problems with very high-dimensional spatio-temporal outputs. The analysis of…

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

Reinforcement learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its stro…

Learning Where and What to Lift for Bi-planar X-ray-to-CT Reconstruction

X-ray imaging can be approximately modeled as the projection of an underlying volumetric attenuation field, with each measurement recording…

Nonadaptive Learning in Robust Nonlinear Output Regulation

This paper considers robust nonadaptive regulation for general nonlinear systems in an output-feedback setting with arbitrarily high relati…

Understanding Curriculum Learning in Large Language Models via Cross-Difficulty Optimization Dynamics

Curriculum learning has been widely adopted in the post-training of large language models by organizing training data from easy to hard. Ho…

When Agents Act on Web3: An Attack-Surface Survey of MCP, Skills, and Tool Calling

AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that mod…

Rethinking Irregular Time Series Forecasting from the Perspective of Basis Functions

Irregular time series forecasting is crucial in many domains, such as healthcare and meteorological observation. However, due to the inhere…

Beyond MSE: Rethinking the Evaluation Metric and Benchmarking for Irregular Time Series Forecasting

Existing research on irregular time-series forecasting has primarily focused on model design, while evaluation metrics remain insufficientl…

NeuroAbs: A Neuro-Symbolic RTL Abstraction Framework for Property Checking Acceleration

Formal verification is a crucial technique for ensuring the functional correctness of hardware designs. In the context of property checking…

Learning What Not to Learn: Adversarial Disentangled Prompt Tuning for Robust Vision-Language Models

While adversarial prompt tuning can enhance robustness of vision-language models efficiently, we find that existing methods aggravate robus…

ORPA: Online Residual Policy Adaptation for Robot Manipulation Control with Human Feedback

Robotic manipulation policies trained via imitation learning, such as Action Chunking with Transformers (ACT), can achieve strong performan…

SPACE: Sample-cloud Predictive Adaptive Conformal Ellipsoids for Multivariate Time-Series Forecasting

Modern probabilistic time-series forecasters often express uncertainty through forecast samples. While typically converted into nominal pre…

MoFE: A Novel Mixture-of-Experts Framework with Fourier Neural Operators for Cryptocurrency Forecasting

Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale st…

Inductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problems

Neural network surrogates are an emerging alternative to traditional electromagnetic wave simulators like finite-difference time-domain (FD…

Fair ASR: Re-Evaluating Black-Box Jailbreaks under Shared Target-Call Budgets

Reliable jailbreak evaluation is essential for assessing LLM safety, but most existing studies rely solely on attack success rate (ASR) wit…

Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning

Sample efficiency is a central challenge in reinforcement learning (RL), particularly in image-based domains where agents must learn from h…

PTXBench: Benchmark and Adapt LLMs for GPU Kernel Optimization with Architecture-specific PTX

We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel…

Leveraging generative hallucination and biophysics-informed modeling for unified biomolecular sequence-structure co-design

Biomolecular design underpins applications from molecular recognition to therapeutics and synthetic biology, yet de novo interaction design…

SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation

We introduce Semantic Task Completion Video Generation, an outcome-oriented video generation task. Under this formulation, success requires…

Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task

Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across…

Explainable AI-Powered Framework for Video-Based Skill Assessment in Cataract Surgery

Persistent shortages in the surgical workforce and inherent limitations of traditional training methods highlight the necessity of automate…

CoAL-RAG: A Complexity-Aware Legal Retrieval-Augmented Generation Method

Legal consultation questions exhibit multi-level complexity. A single retrieval strategy often leads to over-reasoning for simple questions…

No Gaussian Required: Contrastive Inverse Dynamics for JEPA World Models

Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future embeddings, but the objective admits a trivial sol…

Where a New Concept Must Enter: Entry Point Gates Cross-Task Usability in Unified Multimodal Models

Unified multimodal models (UMMs) are motivated by the hope that understanding and generation reinforce each other but controlled ablations…

Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries

Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, th…

DMT-Dens: Density-preserving manifold visualization for biological data

Motivation: Low-dimensional embeddings are widely used to explore cell-state heterogeneity in single-cell and other high-dimensional biolog…

tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots

In this study, we investigate developmental mechanisms that enable small, resource-constrained systems such as cm-sized millirobots to auto…

HarnessRisk: A Lifecycle-Oriented Benchmark for Agent Harness Safety

Large language models are increasingly deployed through agent harnesses that manage tools, extensions, persistent state, permissions, and e…

Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges

Conversational AI is moving beyond isolated text prompts toward sustained, multimodal interaction. In real conversations, users clarify goa…

From Student Risk Prediction to SC2R: Semantics-Constrained Counterfactual Recourse for Educational Decision Support

Learning analytics models can identify students at risk of poor performance, but they do not directly indicate which interventions are feas…

Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision

Developing robots capable of understanding and manipulating objects requires compact, interpretable, and generalizable representations. Thi…

DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval

Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model fo…

MobileWorldSafety: Benchmarking GUI Agent Safety Against Environmental Injection Attacks in Android Apps

LLM-powered GUI agents that autonomously operate smartphones are rapidly transitioning from research prototypes to early real-world deploym…

Benchmarking Automated Security Patch Backporting: How Far Are We?

Automated security patch backporting is critical for mitigating N-day vulnerabilities. Recent tools report success rates above 80% on their…

GADR: Gathering Architecture Decision Records from Meeting Transcriptions

Existing LLM-based approaches to Architecture Decision Record (ADR) generation share a critical and largely unexamined assumption: that inp…

Dijkstra as an Oracle for Online Stochastic Shortest Path Navigation with Provable Guarantees

Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown tr…

Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although m…

What Aggregate Scores Miss: Measuring Item-Level Regressions in Commercial LLM API Migrations

Context: Software systems that depend on commercial large language model APIs must migrate to successor versions when vendors deprecate old…

Learnware for CSI Feedback: Scene-specific Small Models Can Do Big

Intelligent channel state information (CSI) feedback is essential for realizing the high capacity and spectral efficiency goals of future 6…

Training with synthetic data for drone detection in thermal imagery

Ground-to-Air (G2A) drone detection in medium- and long-wave infrared (MWIR/LWIR) imagery is challenging due to reduced texture information…

Interpretable Humans, Alien LLMs: Expert Analysis of Latent Structures in Assessment Responses

The evaluation of large language models (LLMs) relies heavily on human-designed assessments, implicitly assuming that AI and humans employ…

MotoSafety: Edge-AI with Learned Temporal Importance for Two-Wheeler Collision Risk Assessment Under Time Pressure

Powered two-wheeler riders face critical safety challenges in low- and middle-income countries, yet limited studies exist on how cognitive…

The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges

LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. Howe…

AdaLens: Interactive Storyline for Monitoring and Steering Long-Running Agentic Data Analysis

Large language models are pushing data science toward increasingly autonomous and agentic workflows, with recent systems already supporting…

Encoded but Not Actionable: Auditing the Decode-Generate-Steer Gap in Frozen LLMs for Geometric Constraints

Large language models (LLMs) have demonstrated strong performance on structured reasoning tasks, but what they encode and whether it inform…

BEAR-Bench: A Bilingual Enterprise and Academic Reasoning Benchmark for Multimodal Models

While Multimodal Large Language Models (MLLMs) have made significant strides in visual comprehension, their ability to reason about text-de…

Comparative Study of Out-of-the-Box Technology for Automatic Target Detection and Recognition

Automatic Target Detection and Recognition (ATD/R) is critical for military decision support and (semi-)autonomous operations. Recent advan…

Analysis of Types of Inquiries in Student-AI Interaction: A case study of two CS2 tasks

Background and Context: Question and inquiry are integral parts of knowledge seeking and learning. Despite their importance, students tend…

A Theoretical Framework for Parallel Lifelong MAPF Using Group Decentralized Planning

In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obsta…

Collective Counterfactual Planning: Coordination, Consent, and Verification under Representational Constraints

Groups routinely complete projects that no single member can plan, execute, or verify alone. We propose a formal model of this phenomenon,…

Grading Needs a Rubric, Not Intelligence

Small language models can grade open-ended examination answers as reliably as substantially more expensive models when they grade against a…

Efficient RLVR Scheduling via Graph-Structured Online Difficulty Estimation

Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models but relies on costly rol…

SIGMA: SHAP-Guided Implicit-Trajectory Generation for Metadata-Free LLM-Based AutoFE

Recent research has leveraged Large Language Models (LLMs) to enhance Automated Feature Engineering (AutoFE) through semantic descriptions…

An Omitted Mode Is a Rare Rule: The Sampling-Verification Danger Law in Continuous Code World Models

In the Code World Model paradigm an LLM synthesizes an executable world model that a classical planner searches, and the model is accepted…

Too Sure to Be Safe: Model Calibration for Reliable Log Anomaly Detection

Online log anomaly detection is critical for maintaining the reliability of large-scale computing systems. Although recent language model-b…

Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity

Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability,…

Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media

The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideol…

Traceable Trust for action-ready artificial intelligence in bioscience

Artificial intelligence (AI) is becoming part of the working infrastructure of the biosciences. AI models can predict biomolecular structur…

Policy-Invariant Reward Shaping from LLM Feedback: A Framework for Hybrid RL Agents

Combining large language models with reinforcement learning is increasingly explored, yet the theoretical status of LLM-derived reward sign…

Why GPT-Style Models Do Not Directly Transfer to Symbolic Music: Compression in the Wrong Coordinate System

GPT-style models achieve strong performance by representing language with finite vocabularies of reusable discrete tokens. This success has…

Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors

MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse pha…

From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation

Large-scale image generation has benefited from advances in data scale, quality, rebalancing, and recaptioning, yet conventional pipelines…

HA-VLN 2.0: An Open Benchmark and Leaderboard for Human-Aware Navigation in Discrete and Continuous Environments with Dynamic Multi-Human Interactions

Vision-and-Language Navigation (VLN) has been studied mainly in either discrete or continuous spaces, with little attention to dynamic, cro…

Efficient Dynamic Shielding for Parametric Safety Specifications

Shielding has emerged as a promising approach for ensuring safety of AI-controlled autonomous systems. The algorithmic goal is to compute a…

MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale

Representation learning of geospatial locations remains a core challenge in achieving general geospatial intelligence, with increasingly di…

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering

When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model…

Towards Unified World Models for Visual Navigation via Memory-Augmented Planning and Foresight

Enabling embodied agents to imagine future states is essential for robust and generalizable visual navigation. Yet, state-of-the-art system…

Planning under Distribution Shifts with Causal POMDPs

In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of condi…

Does Unification Come at a Cost? Uni-SafeBench: A Safety Benchmark for Unified Multimodal Large Models

Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While unified arc…

TSQueryBench: LLM-as-a-Judge for Time Series Explanations

Natural language explanations of time series data are increasingly produced by foundation models in high stakes domains, making factual cor…

Chronos: The AI Co-Historian

AI is increasingly supporting, accelerating, and automating scientific discovery across subjects. Yet, the adoption of AI in historical res…

Ads in AI Chatbots? An Analysis of How Large Language Models Navigate Conflicts of Interest

Large language models (LLMs) are trained to align with user preferences through methods like reinforcement learning. Yet models are beginni…

Every Picture Tells a Dangerous Story: Memory-Augmented Multi-Agent Jailbreak Attacks on VLMs

Vision-Language Models (VLMs) expand the attack surface of safety-aligned systems by coupling visual perception with text generation. Exist…

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal…

ScreenSearch: Uncertainty-Aware OS Exploration

Desktop GUI agents operate under partial observability: visually similar screens can correspond to different underlying workflow states, so…

Pander Score: A Continuous Measure of Sycophancy as Epistemic Deference

Current AI models frequently exhibit epistemic sycophancy, endorsing claims to agree with a user. Existing evaluations typically measure th…

A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning

Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificia…

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating divers…

Thinking Before Retrieving: Robust Zero-Shot Composed Image Retrieval via Strategic Planning and Self-Criticism

Composed image retrieval requires identifying a target image from a gallery by integrating a reference image with a textual modification in…

The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents

A self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retiremen…

FormalAnalyticGeo: A Neural-Symbolic Based Framework for Multimodal Analytic Geometry Problem Generation

Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic g…

JUMP: Single-Pass Membership Inference on Fine-Tuned Diffusion Language Models

Public open-weight language models are often fine-tuned on private or domain-specific data before deployment, creating a need to audit whet…

OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding

Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limit…

G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution

Deep search has become a fundamental capability of large language models (LLMs) for solving open-domain complex tasks. However, existing ap…

Agents Catching Agents: Shortcut Cascades and Benchmark Gaming in Clinical Multi-Agent Systems

Clinical decision support is moving toward committees of language-model agents deliberating on a shared workspace. We ask whether such comm…

VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledge

Retrieval-Augmented Generation (RAG) is essential for enterprise knowledge question answering (QA), particularly in domains with complex pr…

Decided Upstream, Written Late: Locating and Pricing the Cross-Lingual Refusal Circuit of a Multilingual MoE

Safety alignment in multilingual models is uneven: a model that reliably refuses a harmful request in English will often comply with the sa…

Rationale-Guided Learning for Multimodal Emotion Recognition

Multimodal emotion recognition in conversation (MERC) requires understanding complex interactions between verbal and non-verbal cues. Howev…

Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration

Bayesian calibration of process-based models requires a prior distribution for each model parameter. Despite decades of methodological work…

LLM-Guided Graph Generation for Structure-Based Local Improvement Methods

Large neighborhood search normally selects a random subset of decision variables for iterative optimization. To efficiently solve various p…

The Authenticity Gap in Human Evaluation

Human ratings are the gold standard in NLG evaluation. The standard protocol is to collect ratings of generated text, average across annota…

Comprehensive framework for evaluation of deep neural networks in detection and quantification of lymphoma from PET/CT images: clinical insights, pitfalls, and observer agreement analyses

This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing l…

ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation

Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent…

Optimizing Container Loading and Unloading through Dual-Cycling and Dockyard Rehandle Reduction Using a Hybrid Genetic Algorithm

This paper addresses the NP-hard problem of optimizing container handling at ports by integrating Quay Crane Dual-Cycling (QCDC) and dockya…

LSem2Vec: A Simple yet Effective Two-Stage Approach for Source Code Embedding

The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code emb…

M3TR: Temporal Retrieval Enhanced Multi-Modal Micro-video Popularity Prediction

Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' en…

Diffusion Models for Smarter UAVs: Decision-Making and Modeling

Uncrewed Aerial Vehicles (UAVs) are increasingly used in modern communication networks. However, challenges in decision-making and digital…

Gradient Heterogeneity Complements Hessian Heterogeneity in Transformer Optimization

Transformers are difficult to optimize with stochastic gradient descent (SGD) and largely rely on adaptive optimizers such as Adam. Despite…

MCTS-KBQA: Monte Carlo Tree Search with Information Gain Rewards for Knowledge Base Question Answering

This work investigates how to improve large language model (LLM)-based reasoning for knowledge base question answering (KBQA) via Monte Car…

Beyond BFI: The CSI for Enhanced Reliability and Validity in Evaluating LLM Personality Traits

As large language models (LLMs) increasingly function as human-like assistants exhibiting human-like personality traits, understanding thei…

LZ Penalty: An information-theoretic repetition penalty for autoregressive language models

We introduce the LZ penalty, a penalty specialized for reducing degenerate repetitions in autoregressive language models without loss of ca…

TabularQGAN: A quantum generative model for tabular data synthesis

In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where r…

Multi-Scale Spectral Attention Module-based Hyperspectral Segmentation in Autonomous Driving Scenarios

Recent advances in autonomous driving (AD) have highlighted the potential of hyperspectral imaging (HSI) for enhanced environmental percept…

Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review

In July 2025, 18 academic manuscripts on arXiv contained hidden instructions that manipulated AI-assisted peer review (indirect prompt inje…

Solving nonconvex Hamilton--Jacobi--Isaacs equations with PINN-based policy iteration

We propose a mesh-free policy iteration framework that combines classical dynamic programming with physics-informed neural networks (PINNs)…

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT

Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object p…

Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing

Open-Vocabulary Remote Sensing Image Segmentation (OVRSIS), an emerging task that adapts Open-Vocabulary Segmentation (OVS) to the remote s…

ChannelFlow-Tools: A Configuration-Driven Pipeline for Generating Machine-Learning-Ready Datasets of 3D Obstructed Channel Flows

Data-driven surrogate models are increasingly used in computational fluid dynamics, and their reliability depends on the quality of the tra…

Future-Back Threat Modeling: A Foresight-Driven Security Framework

Traditional threat modeling remains reactive-focused on known TTPs and past incident data, while threat prediction and forecasting framewor…

Audio Physical Dynamics Inspired Deepfake Detection for Voice Authentication Systems

Voice authentication systems deployed at the network edge face dual threats: a) sophisticated deepfake synthesis attacks and b) control-pla…

Cluster Aggregated GAN (CAG): A Cluster-Based Hybrid Model for Appliance Pattern Generation

Synthetic appliance data are essential for developing non-intrusive load monitoring algorithms and enabling privacy preserving energy resea…

The $\mathbf{P}$-Completeness of Inverted Index Traversal: On the Complexity of Evaluating Boolean Query DAGs

Modern AI agents increasingly rely on search infrastructure to execute complex, neuro-symbolic reasoning workflows. These workflows often c…

Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries

Large Language Model (LLM)-powered Automatic Speech Recognition (ASR) systems achieve strong performance with limited resources by linking…

SCOPE: Selective Conformal Optimized Pairwise LLM Judging

Large language models (LLMs) are increasingly used as scalable judges in pairwise evaluation, but they remain prone to miscalibration and b…

Adversarial Data Modeling in Epidemiology

Epidemiological models increasingly rely on crowdsourced, self-reported behavioral data such as vaccination status, mask usage, and social…

Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation

Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document ques…

Supporting Calibrated Reliance in Human-AI Collaboration: Different Strategies for Different Tasks

As AI systems increasingly support human decision making, a central challenge is determining what information helps people recognize when t…

Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries

Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept…

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning

Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle…

SegWithU: Uncertainty as Perturbation Energy for Single-Forward-Pass Risk-Aware Medical Image Segmentation

Reliable uncertainty estimation is critical for medical image segmentation, where automated contours feed downstream quantification and cli…

Self-Distillation as a Performance Recovery Mechanism for LLMs: Counteracting Compression and Catastrophic Forgetting

Large Language Models (LLMs) have achieved remarkable success, underpinning diverse AI applications. However, they often suffer from perfor…

FairNVT: Fair Classification via Noise Injection in Vision Transformers

This paper presents FairNVT, a lightweight debiasing framework for pretrained transformer-based encoders that improves prediction fairness…

Convergent Evolution: How Different Language Models Learn Similar Number Representations

Language models trained on natural text learn to represent numbers using periodic features with dominant periods at $T=2, 5, 10$. In this p…

Protect the Brain When Treating the Heart: Feasibility of 2.5D U-Net for Real-Time Gaseous Microemboli Detection

Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approa…

Discovering physical mechanisms from experiment-simulation mismatches

Scientific discovery often begins where observation and prediction disagree. As computation and machine learning survey chemical space, exp…

SOD: Step-wise On-policy Distillation for Small Language Model Agents

Tool-integrated reasoning (TIR) is difficult to scale to small language models due to instability in long-horizon tool interactions and lim…

Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum

In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approach…

SurgicalMamba: Dual-Path SSD with State Regramming for Online Surgical Phase Recognition

Online surgical phase recognition must commit to a prediction at every frame of a procedure that runs for hours, from past frames alone and…

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models

The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models…

On the Subgaussianity of Quantized Linear Maps: An AI-Assisted Note

We prove an elementary bounded-differences inequality for functions of non-isotropic Gaussian vectors. Specifically, if $f$ has bounded coo…

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for w…

FVSpec: Real-World Property-Based Tests as Lean Challenges

We present a benchmark for evaluating AI models and agents on real-world formal software verification tasks. We first scrape 11,039 propert…

BRo-JEPA: Learning Modular Transformations in Latent Space

Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns? We study this question using MNIST (…

Agent libOS: A Runtime Substrate for Capability-Controlled Self-Evolving LLM Agents

Large language model (LLM) agents can persist across tasks, acquire memory, activate Skills, synthesize tools, fork child processes, attach…

Planning-aligned Token Compression for Long-Context Autonomous Driving

Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences t…

The Standard Interpretable Model: A general theory of interpretable machine learning to deductively design interpretable methods using Lagrangian mechanics

As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and c…

Physics-Grounded Causal Auditing of End-to-End Driving Planners

End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that me…

How Transparent is DiffusionGemma?

LLM reasoning transparency is a critical affordance for understanding model decisions, mitigating misuse and misalignment, and debugging su…

Has This Checkpoint Been Abliterated? A Two-Signal Audit and Its Failure Map

Can a platform tell, before deployment, whether an open-weight checkpoint has had its refusal mechanism stripped? Runtime guards cannot: th…

Turning Off-Policy Tokens On-Policy: A Plug-in Approach for Improving LLM Alignment

Reinforcement learning (RL) post-training for large language models (LLMs) follows a efficient paradigm of "rollout then update", which ine…

Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference

Vision-Language Models (VLMs) are the perceptual backbone of embodied AI, but their energy footprint on edge hardware remains poorly unders…

From Adoption to Deployment: A Qualitative Study on AI Integration in Software Development Practice

The increasing adoption of Large Language Models (LLMs) as AI components in modern software systems introduces distinct security risks to t…

Constitutional Midtraining: Content Presence Drives Alignment Gains

Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining intervent…

WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA

Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving; however, their efficient deploy…

Eigenius: A Typed Knowledge-Graph DBMS with Epistemic Stratification and Institution-Mediated Reasoning

As "AI Scientists" emerge to drive research via the Model Context Protocol (MCP), systems relying on ephemeral scripts will fail. The sheer…

Guideline-as-Oracle: Zero-Annotation Training of an Ophthalmic Telephone Triage Agent

Scaling supervision for multi-turn medical agents is difficult because expert dialogue annotation is costly and clinical conversations are…

Search-G1: Grounded Search Agents via Representation-Based Intrinsic Rewards

Search-augmented language agents should retrieve external information only when necessary and ground their answers in retrieved evidence. E…

What to Edit Next: Visually Aligned Image-Editing Follow-Up Suggestions in Conversational Systems

Conversational assistants increasingly recommend follow-up edits to help users continue a task. Existing systems primarily target text-only…

FUSE: Frame-Unified Stress Estimation from Facial Video

Automatic stress detection from facial video offers a practical path to non-intrusive affect monitoring, yet existing video-based approache…

A 12-CNOT Double Qubit Excitation Gate

In this work, we presented, to the best of our knowledge, the first reported 12-CNOT decomposition of the double qubit excitation operator.…

AQuA: Recursively Self-Improving Quantitative Trading Research Agents

We study recursive self-improvement at the level of quantitative-investment research: whether an autonomous system can use evidence from ea…

Falsehood and Impossibility Are Different Directions in an AI's Representation of Language

Language can describe states of affairs that are false and states of affairs that could not be the case at all. Whether an AI model interna…

NaviDC-OCR: Navigating Document Parsing Across Digital and Camera-Captured Documents

Document parsing aims to transform unstructured documents into structured and machine-readable representations. Recent advances in Vision-L…

UltraArUco: A Lightweight Multilingual Library And Framework With Low-Latency Real-Time Marker-Based Tracking System For Mobile AR Interaction

UltraArUco is a lightweight multilingual library and framework for low latency, realtime marker-based tracking in mobile augmented reality.…

CLAIR-Fin: An Adversarial Multi-Agent Framework for Claim-Level Verification and Adaptive Debate in Cross-Modal Financial QA

Existing defenses against hallucination in retrieval-augmented and multi-agent pipelines remain partial: evidence is trusted despite modali…

「うちの会社にAIなんて」――謙遜する中小企業に眠る伸びしろ　OpenAIがパートナー網拡充、新規3社が語る

米OpenAIが法人向けパートナーネットワークの日本での拡充を進めている。メディア向け座談会に登壇した新規パートナー3社が語ったのは、「うちの会社にAIなんて」と謙遜する中小企業にこそ眠る“伸びしろ”だった。

なぜNTTは「小型LLM」にこだわるのか　国産AI「tsuzumi 2」に込めた狙い

LLMは大規模であるほどよいのか。NTTが開発する国産LLM「tsuzumi 2」は、小型化と日本語処理、データ主権を重視する。企業が生成AIを実務に組み込む際の現実解として、同モデルは何を目指しているのか。開発担当者の講演から読み解く。

Googleや英政府、AIで飛行機雲を回避する大規模実証開始　航空業界の温暖化影響を減らす狙い

Googleは、英政府や航空管制大手NATSなどと共同で、AIを用いて飛行機雲を回避する実証プログラムを開始すると発表した。北大西洋のシャンウィック洋上空域を対象に、航路をわずかに変更して温暖化影響を抑える。空域規模での協調的回避の実証は世界初となり、Googleは計算基盤など…

Claude Codeの週次制限枠「50％増」、8月31日まで延長　「恒久化したいものの……」

米Anthropicは、AIコーディング支援ツール「Claude Code」の週次利用制限枠を50％増やすキャンペーンを8月31日まで延長すると発表した。

人材育成を邪魔する、「忙しすぎる現場」以外の要因は？　ガートナーが指摘

デジタル人材育成が急務になる中、ガートナーが人材育成に関する課題を調査した。同社が指摘する、「学ぶ時間がない」の先にある課題とは。

冷間鍛造FEMをAIで高速予測するサロゲートモデル「ForgeNet」を発表

ゴーデルブロックは、計算力学の国際会議「WCCM ECCOMAS Munich 2026」で冷間鍛造シミュレーション向けAIサロゲートモデル「ForgeNet」の研究成果を発表した。解析結果を固定オイラー格子へ投影することで、適応的リメッシュに伴う節点対応の課題に対処する。

「AIに原始人っぽく話すとトークン65％削減」は本当か？　JetBrainsが検証してみた

JetBrainsは、AIエージェントの応答を圧縮するスキル「Caveman」の効果を検証したした結果を公式ブログで公開した。Cavemanは、「エージェントの応答を原始人のような簡潔な言葉に変えることで、トークンを65％削減する」と主張しているスキルだ。

話題の職種「FDE」、実際何をやってるの？　OpenAIの現役2人に聞いた

話題の職種「FDE」の実態はどのようなものか。米OpenAIでFDEとして働く2人に聞いた。

OpenAI、「ChatGPT for Teens」発表──宿題の“丸投げ”検知、自傷や摂食障害などの保護も強化

OpenAIは、13?17歳向けの新環境「ChatGPT for Teens」を発表した。年齢推定や申告に基づいて自動適用され、段階的な理解を促す「Study Mode」などの学習機能を提供。自傷行為や摂食障害などの高リスク領域での保護を標準で有効にし、感情的な依存を促す対話を…

Cursor capitalizes on GitHub frustration, launches rival hosting platform

Cursor, known for its AI Code Editor, is launching a new code-hosting platform to rival developers' long preferred favorite, GitHub.

OpenAI、フロンティアAIの強化学習を一部停止　安全対策を強化

OpenAIは、外部侵害インシデントや次期モデルの高いサイバー能力を受け、開発・テスト段階の安全対策を強化すると発表した。一部の大規模モデルの強化学習を一時停止し、研究環境の隔離や内部活動の監視多段化、アラインメント手法の拡張を実施。安全性基準を満たした上で開発を進める姿勢を示…

「害悪すぎる」「バカ迷惑」──嫌われまくる“AI営業電話”、今すぐ取れる自衛策は

AI営業電話に迷惑を被ったという声が少なくない。SNSでは迷惑がる声も……。

ニンテンドーシステムズ開発者が明かす、通信「低遅延・安定運用」のコツ【事例集】

レガシーシステムの解析、通信の遅延、現場ナレッジの活用。IT部門が抱える難題を、先進企業はどう突破したのか。3社の事例から、IT課題解決のための具体的なアプローチを紹介する。

フィジカルAIとヒューマノイドの可能性、PFNの見立てとトヨタのアプローチ

「インテル・ロボティクス・ワークショップ2026」のレポート記事をお送りする。今回の後編では、ヒューマノイドとフィジカルAIをテーマにした、三菱UFJ銀行、Preferred Networks（PFN）、トヨタ自動車 未来創生センターの講演内容を紹介する。

【Pythonで学ぶデータ分析】対応のあるデータの母平均に差があるかどうかをベイズt検定で調べる ～ ホラー映画を観ると握力は上がるのか？

手に汗握るホラー映画を観た後では、観る前よりも握力が強くなったような気がしませんか？ 同じ人の2回の測定値の差を求め、ベイズ統計により検定します。事前分布のパラメーターを変えても結果が安定するかどうかを調べる「感度分析」にも触れます。『社会人1年生から学ぶ、やさしいデータ分析』…

Strengthening democratic oversight in national security

OpenAI launches an initiative to strengthen democratic oversight of AI in national security, supporting government institutions with tools,…

OpenAI institutes new safeguards after Hugging Face breach

The new safeguards include more detailed monitoring of models during the development process, as well as greater emphasis on alignment and…

Etched’s valuation doubles to $21B in a month

Jane Street has installed Etched's first shipped AI cluster system, and was so impressed, it led another massive round, the startup says.

Why Apple’s camera-equipped AirPods may not be the ‘pervert pods’ consumers fear

Apple’s leaked camera-equipped AirPods might avoid the privacy pitfalls of other AI wearables by preventing users from recording photos and…

Warp’s new system is an out-of-the-box software factory for AI development

On Tuesday, Warp introduced Warp Factories, a new infrastructure system designed to make building AI software factories as easy as possible.

OpenAI launches a safer ChatGPT for teens — years after teens started using it

ChatGPT for Teens adds age-appropriate safety measures, parental controls, and learning tools designed to steer teens away from harmful con…

Perplexity’s free AI offer left it with millions more users in India

Perplexity's India revenue rose about 60% after the Airtel offer ended for new users, even as downloads declined.

Partnering with CodeAI to prepare the first AI generation

OpenAI and CodeAI are partnering to help students build AI literacy, think critically about AI, and develop the skills to use and shape it…

Pacing model development in an era of cyber-critical capabilities

OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model de…

Introducing ChatGPT for Teens: Built for learning, backed by protections

ChatGPT for Teens helps teens learn, think critically, and use AI with confidence, with stronger built-in protections, healthy-use features…

無料で読めるAIエージェントの実践ガイド、Googleが公開　基礎から本番実装まで学べる

AIエージェントの基礎から本番実装まで学べる5つのガイドをGoogleが無償公開した。Kaggleと共同で実施した研修プログラムを基にした内容で、開発者の実務に直結する知識を習得できる。各ガイドが扱う内容とは。

Asana cleared 5 years of engineering work in 2 weeks with Codex

Asana used OpenAI Codex to replace an outdated testing system in two weeks, completing work expected to take five years for about $12K.

FLOPs vs Real Work: The Importance of Replication in AI Efficiency Assessment

AI efficiency has recently taken the spotlight in both academy and industry due to massive model scales, high energy demands, and environme…

Large Language Models Show Metacognitive Sensitivity in Medical Reasoning

Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whethe…

The Unwritten Benchmark: A New Challenge for Multimodal Machine Learning in Abstract Perceptual Reasoning

Current multimodal models have demonstrated remarkable proficiency in recognizing static visual and auditory content. However, their capaci…

When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL

Effective communication in multi-agent reinforcement learning requires agents to decide not only \textit{what} to communicate, but when? Ex…

Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review

AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates complian…

Position: AI Lock-In Is in Progress, and We Must Be Prepared

AI safety research has mainly focused on two areas: technical alignment (ensuring AI systems produce human-aligned outputs) and the regulat…

Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picture

Recent work on evaluating the moral competence of large language models (LLMs) has focused primarily on what we call the moral value proble…

From Doyle to AGM: A Survey and an Implementation Roadmap for Belief Change

This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change imp…

Position: AI Governance Needs ISO-like Interoperability Protocols, Not Just Laws

As Artificial Intelligence (AI) systems become deeply integrated into critical global infrastructure, the urgency for robust governance fra…

Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration

Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental l…

Position: Want Better ML Reviews? Stop Asking Nicely and Start Incentivizing with a Credit System

With soaring submission counts, stricter reciprocal review policies, widespread adoption of platforms like OpenReview, and without the offs…

Longitudinal and Graph-Augmented Prediction of Adolescent Substance Use Onset in the ABCD Study

Early identification of adolescent substance-use risk is an important prevention challenge, yet the relative value of baseline characterist…

SKILL: Self-correcting Knowledge-guided Iterative Large Language Model Agent for Logic Optimization

Logic synthesis optimization poses significant challenges due to exponentially growing search spaces, sparse reward signals, and diverse lo…

OGX: An Open-Source, Vendor-Neutral Generative AI Application Server

OGX (Open GenAI Stack) is an open-source AI application server and Python library that implements the APIs of major frontier labs (OpenAI,…

Euclid-Omni : A Unified Neuro-Symbolic Framework for Plane Geometry

Euclidean geometry is a compelling testbed for AI reasoning, as it demands the combination of intuitive diagram understanding, axiomatic de…

An Agentic Framework Using Rules and LLMs for Embedding and Annotating Descriptive Document Layouts: A Plant Science Use Case

Background: Recent advances in information retrieval (IR) leverage both dense and sparse representations, large language models (LLMs), and…

The Hallucination Snowball: Modeling Error Propagation as State Transitions in Multi-Agent LLM Pipelines

Sequential multi-agent LLM pipelines chain specialized agents without verification at handoffs, creating a structural flaw with measurable…

Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement

LLM agents increasingly perform irreversible real-world actions, including database updates, API calls, file operations, and autonomous use…

Position: Medical AI Neglects Real Treatment Outcomes

Medical AI has rapidly improved its ability to perform diagnostic and prognostic tasks that lead to treatment decisions. But understanding…

When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning

Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a cas…

Do LLM Agents Negotiate Rationally? A Mechanism-Design Framework for Verifiable Multi-Agent Interaction over A2A/MCP

Modern LLM-agent frameworks increasingly interoperate through standards such as Anthropic's Model Context Protocol (MCP) for agent-to-tool…

Large Language Models and their Awareness of Mechanics and Spatial Geometry

Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in m…

A Human-Centred Approach to Benchmarking LLMs for Parenting Advice

People are increasingly using large language models (LLMs) to seek advice, including for parenting. Parenting is a critical and socially se…

Learning Agent Execution for KV-Cache Management in Agentic Serving

Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequ…

Accuracy and Reliability of Large Language Models in Cosmetic Chemistry and Skin Health: A Benchmarking Study

As consumers increasingly turn to AI chatbots for skincare advice, the technical accuracy of Large Language Models (LLMs) in cosmetic chemi…

Task- and Session-Level Model Routing: A Common-Interface Hybrid Evaluation of Four Open-Source Routers Across Four Benchmarks

Agentic systems increasingly delegate model selection to a router, yet open-source routers are usually evaluated with different tasks, cand…

Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance

Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the need…

When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation

Large language models for code generation often produce incorrect solutions without reliable indicators of failure. We study whether uncert…

Cross-Domain Industrial Fault Detection by Causal Mechanism Monitoring

Unsupervised fault detection in industrial systems is dominated by reconstruction based methods that monitor individual sensor marginal dis…

Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

Large language model-based agents are increasingly deployed as collaborators in scientific discovery yet most current work focuses on the a…

Beyond Correctness: Toward Automated Novelty Verification with Lean 4

Artificial intelligence systems applied to mathematics verify correctness but not novelty: an automatically generated theorem can compile i…

Auditing an AI-Generated Mathematical Proof: A Correction to a Greedy Conditioning Lemma in Quantum Parallel Repetition

Chapter 6 of OpenAI's *Ten Advances in Mathematics and Theoretical Computer Science* claims an exponential parallel-repetition theorem for…

When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry

Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent mes…

A Comprehensive Survey of Wireless Foundation Models for AI-Native 6G Networks

Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, tra…

Synchronized Logit Steering: Real-world Steganography

Steganography in large language models offers a way to embed hidden messages within natural-sounding text. Existing token and logit-level m…

Semantic Uncertainty-Guided Orchestration in Hierarchical Multi-Agent Systems

As large language model (LLM)-based multi-agent systems become increasingly capable, coordinating agents under uncertainty becomes a fundam…

Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation

AI coding agent benchmarks rank agents with the Chen et al. (2021) pass@k estimator, but current implementations misapply it: they set n to…

Advanced modelling and data analytics in aviation

The aviation industry characterized by its stringent safety standards has seen a growing need for innovative approaches to enhance safety m…

Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs

Data cleaning without a trusted clean reference is challenging because unusual values may represent either genuine errors or valid observat…

From Errors to Proofs: Minimal-Core-Guided Repair for Neuro-Symbolic Constraint Solving

Making language models solve constraint problems reliably often means having them translate the problem into a formal specification and del…

Task-Driven Three-Layer Distributed Scheduling for Emergency Earth Observation in Large Low-Earth-Orbit Constellations

Large low-Earth-orbit (LEO) Earth-observation (EO) constellations offer frequent access to geographically dispersed ground targets, but eme…

CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs

Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday s…

Individual Disempowerment through an Advice Channel: Control Loss when Influence is Endogenous

An AI that can only give advice seems safe: the human is always free to ignore it. That is the premise of the boxing tradition in AI safety…

Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning

Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in…

Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking

When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information…

MINT: Min-Selection Preference Distillation for Balanced Multi-Objective Alignment

Aligning a language agent to several objectives at once is a persistent failure mode of preference-based training: when objectives are comb…

What the Reranker Sees: Multi-Aspect Page Annotation for Long-Document Multimodal Question Answering

Long-document visual question answering (VQA) over documents of tens to hundreds of pages mixing text, tables, charts, and figures typicall…

Discovering High-Quality Chess Puzzles with Offline Reinforcement Learning

Learning and skill mastery require extensive and deliberate practice. In many learning settings, producing high-quality pedagogical materia…

JarvisBench: Always-on Intelligence Between Humans and Agents

Long-horizon agents can execute continuously, but human attention remains intermittent and scarce. This creates a bidirectional coordinatio…

Personalized Auto-Research: Towards a True AI Co-Scientist

AI co-scientists that generate hypotheses, retrieve related work, design experiments, execute code, and draft full papers are beginning to…

Frontier AI Forecasting Has a Measurement Problem: An Audit of Progress Evidence

Quantitative forecasts of frontier artificial intelligence often connect dated targets to trends in benchmark scores, training compute, rel…

LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks

Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when ex…

Small Models Scout Bottleneck Order for Large-Model Data Control

Small proxy models are commonly used to identify data mixtures for larger-scale training. We ask whether their training trajectories reveal…

When Is an Agent Evaluation Over? Outcome Finality and Cross-Unit Separation

Current agent evaluations score models on the state visible at the end of a stopped run which they count as one trial. However, interpretin…

Skill Blocks: How Should an Agent Load Its Skill? A Caching-Correct Comparison of Pre-load, On-Demand Tool-Loading, Progressive Disclosure, and Hybrid

Agent skills are often injected in full on every request, increasing token cost. We compare four content-preserving loading methods: Full,…

Trust Is Not Enough: Influence Calibration for On-Policy Self-Distillation in Agentic RL

On-policy self-distillation (OPSD) gives language agents dense token-level supervision from a privileged self-teacher on the policy's own t…

RETRACE: Resilience-Guided Trait-Conditioned Craving Estimation from Wearable Physiology in Opioid Use Disorder

Detecting opioid craving from wearable physiological signals is critical yet difficult, with the potential to support proactive interventio…

T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework

Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code opt…

Demand-Driven Vertiport Siting and Discrete-Event Fleet Simulation for On-Demand Urban Air Mobility Network Design

This paper presents a demand-driven framework for on-demand Urban Air Mobility (UAM) network design that links vertiport siting, fleet simu…

Does a Tool Result Carry More Authority Than Plain Text? Three Prospective Studies of False-Claim Adoption in a Synthetic Assignment Task with Claude Opus 5

Language-model systems increasingly read from stores they also write to, so a claim that was merely written earlier can return looking retr…

S2-MoE: Enabling Efficient Self-Speculative Decoding for Mixture-of-Experts on Edge Devices

Deploying large language models (LLMs) for inference on edge devices is challenging due to severe memory and bandwidth constraints. While s…

Gathered, Not Admitted: How Attention Brings a Latent Variable into Verbalizable Form

Language models hold latent quantities in a form they can report on, and more of a quantity is present in that form when the task requires…

LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, oper…

SCOPE: Score-Isolated Agentic Optimization for Video World Models

Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time intr…

Andy: A Mathematical Agent for Rigorous Proof and Autonomous Research

Andy is an autonomous mathematical research agent that solves and verifies submitted problems, formulates new research problems, and constr…

TAHB: A Comprehensive Benchmark for Text-Attributed Hypergraph Learning

Hypergraphs effectively model higher-order groupwise relationships beyond pairwise interactions, while pretrained language models (PLMs) an…

GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs,…

LongDocBench: Benchmarking TOC Hierarchy and Contextual Relationship Recovery in Long Documents

Parsing visual documents into machine-readable representations is fundamental to document intelligence. Existing benchmarks focus on page-l…

Funnel of Thoughts: Efficient Test-Time Scaling via Early Voting and Rollout Pruning

Large Reasoning Models produce diverse, sometimes inconsistent answers across repeated queries on the same problem, so multi-sample inferen…

Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents

Learning from experience is critical for developing capable, self-improving large language model (LLM) agents. Existing methods typically e…

Beyond Thresholds: A Quality-Aware Decision Intelligence Framework for Cold Chain IoT Systems

Cold chain logistics has advanced technologically, yet most deployed systems remain reactive monitors, not decision-making agents: threshol…

StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling

Long-horizon agents can fail even when their underlying models can solve the constituent steps. They may lose track of mutable state, fail…

Validation-Frontier Representation Selection under Constrained Observation

AI systems deployed outside clean benchmark settings often rely on observations that are incomplete, unstable, costly, or degraded by monit…

Second-Order Policy Effects as State Transitions: A Source-Linked Benchmark for Policy Simulation

Policy evaluation often estimates direct benefits and costs while treating the institutional environment as fixed. In practice, a policy ch…

Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents

Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can s…

Anatomy of a Quantized Agent: VRAM Stability and Forecasting in Code-Synthesis Agentic Workloads

Analytical models of peak VRAM consumption for LLM inference decompose memory into weight-storage, KV-cache, and activation terms parameter…

Platform Adaptation Under Governance Interventions: Actor Best-Response Modeling and an External Public-Case Benchmark

Digital platforms govern by changing rules: rankings, monetization thresholds, moderation standards, verification systems, disclosure requi…

ReForge: Keeping ABR Algorithms Never Finished with Verified Large Language Model Edits

Designing an ABR algorithm for one network scenario takes an engineer months, and large language models now do this work in hours, matching…

Translating finite-domain integer constraint models to CP/SMT/ILP/PB/SAT solvers with CPMpy

Constraint solving is a declarative approach for solving combinatorial satisfaction and optimization problems. The user specifies their pro…

ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models

Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Te…

Constitutive Priors for Machine Intelligence: A Legitimacy Theory of the Artificial Physical World

Machine intelligence has conquered the symbolic world but stalled at the physical one. The stall is structural: physical AI faces a cold-st…

SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion

Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable proce…

LongRCA Bench: Diagnosing Responsible Roles and Root Causes in Long-Horizon Agent Failures

When a long-horizon agent execution fails, outcome-level evaluation reveals the unsuccessful result but not where the decisive error entere…

Demographic Injection in Medical Language Models under Diversity, Equity, and Inclusion Prompts

Clinical-AI guidance increasingly recommends prompting language models to reason with attention to diversity, equity, and inclusion (DEI).…

Towards Standardized Evaluation in Automated Domain Modeling: Introducing a Benchmark

Domain modeling plays an essential role in domain-driven design, capturing essential entities and their relationships within a specific dom…

Decentralized Federated Learning for Heterogeneous Multi-Task Semantic Communication

Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL). How…

VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?

Constructing an interactive 3D open world from a user query is important. However, existing methods are primarily evaluated on idealized, s…

$D^{2}R^{2}$: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction

Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Exist…

ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning

Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dy…

Divergent-Convergent Reasoning: Scaling Test-Time Compute through Structured Solution Synthesis

Test-time compute can substantially improve Large Language Model (LLM) reasoning performance, yet how and when additional compute helps rem…

Understanding Cognition-Induced Risks in Agentic AI Systems

Frontier agentic systems powered by large language models (LLMs) exhibit human-like patterns of cognition. As these systems become deeply i…

Physiological World Models for Human State Transitions

Continuous multimodal sensing now allows human physiology to be observed throughout daily life rather than only during occasional clinical…

MoE Router-Guided Clustering for Heterogeneous Federated Instruction Tuning

Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring…

Physics-informed VAE-EVT for Tail Aware Radio Map Prediction

Ultra-reliable low-latency communication (URLLC) requires precise identification of spatial regions where the signal-to-noise ratio (SNR) f…

The Benchmark Trap: Structures of Power and Injustice in AI Evaluations

Artificial intelligence (AI) benchmarks are not neutral tools of evaluation but socio-technical artefacts that shape competition, power, an…

A concentration result for multilayer feedforward neural networks

We consider for an arbitrary fixed $\rho$ and for each positive integer $n$ a multilayer feedforward artificial neural network with $\rho$…

Incoherent by Design? On the Moral Self-Consistency of LLMs

LLMs are increasingly used in morally sensitive contexts, yet it is unclear whether they apply ethical principles consistently across situa…

UC-PSRO: Utility-Conditioned Policy-Space Response Oracles with a Communication-Dropout Curriculum for Game-Theoretic Course-of-Action Generation in Adversarial Swarms

We study generating game-theoretically optimized Courses of Action (COAs) for a Blue UAS swarm against an adaptive Red adversary in a commu…

FedPA-LoRA: Product-Aligned Framework for Mitigating Aggregation and Initialization Errors in Heterogeneous Federated LoRA

Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a…

Grounding Healthcare LLMs in a Causal Knowledge Graph: Framework, Metrics, and a Cardiovascular Pilot

Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer ac…

Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL

LLM-based Text-to-SQL progress is reported across heterogeneous benchmarks, backbones, and inference protocols, making cross-system compari…

TwinGridShield: Consequence-Aware Runtime Authorization for LLM Grid-Agent Actions

Large language model (LLM)-assisted energy-management tools can translate natural-language context into structured grid commands, but synta…

Visible Reasoning and Indirect Prompt-Injection Monitorability Across English, Tamil, and Tanglish

Chain-of-thought monitoring is a potentially useful safety signal, but its reliability across languages and behavioral settings remains unc…

Large Language Model Assisted Operational Monitoring for Battery Energy Storage System Integrated Power Distribution Networks

Battery energy storage systems (BESS) are increasingly used in distribution networks for voltage regulation and demand response, which incr…

Implementation of a Metacognition Framework for Self-Awareness and Self-Regulation in Ensembles of LLMs

Large Language Models (LLMs) are notorious for struggling with assessing their own uncertainty, detecting knowledge conflicts, or recognizi…

A survey of AI-generated voices and their detection

The ability of artificial intelligence (AI) models to generate highly realistic human voices has advanced rapidly. These technologies power…

Does the Proof Prove It That Way? Faithful Formalization of Elements Proofs

In formal verification, both the autoformalization of statements and automated proof search have been studied extensively. While automated…

OTel: Building Domain-Specialized Telecom LLM Foundations for Intelligent Networks

Frontier AI models have advanced rapidly, but they still struggle with telecom-specific tasks. We present Open Telco (OTel), an open teleco…

Measuring Reward Hacking and Reasoning-Answer Decoupling Under Position-Confounded Optimization

When a reward is correct on every training example yet consistent with more than one goal, a model can acquire an unintended one, a failure…

Mental Model Management: An Operator-Based Framework for LLM Memory

Large language models process large amounts of information but usually lack an explicit mechanism for maintaining compact and evolving conc…

Dynamic Multi-Byte Prediction With Hierarchical Language Models

Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword…

A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution

Effective public event forecasting is essential for intelligent service systems, enabling proactive risk management, adaptive resource allo…

EcoVLA: Energy-Efficient Device-Edge Co-Inference for Vision-Language-Action Models under Real-Time Constraints

Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant…

Who Leads Now? Token-Level Modality Arbitration for Chart-to-Code Generation

Chart-to-code generation requires a model to read the fine-grained visual details of a chart and write executable code that reproduces it.…

From Contexts to Values: Context-Dependent Defeat in Abstract Argumentation

In value-based argumentation, an audience's ordering of values decides which attacks succeed as defeats. In many settings the deciding fact…

ATLAS: Scaffold-Free Algorithm Synthesis by LLMs via Embedding-Guided Quality-Diversity Search

Most LLM-based automated algorithm design methods optimize a designated component within a human-specified scaffold, fixing overall organiz…

Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling

Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking…

From Generalist to Specialist: A Context-Fusion Framework for Endoscopic Polyp Reporting with a Frozen VLM

Reliable endoscopic polyp reporting requires integrating quantitative lesion sizing, standardized Paris classification, and clinically mean…

Agent Gym: A Framework for Continuous Evaluation and Evolution of LLM Agents Through Human-in-the-Loop Feedback

Large Language Model (LLM) agents deployed in production environments face a fundamental tension: the agent's behavior is frozen at deploym…

When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction

Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades th…

TRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation

Multi-turn jailbreak attacks have emerged as a critical safety threat to LLMs, as harmful objectives are decomposed across a sequence of ap…

VARM-Bench: Benchmarking Verifiable Structured Reasoning in Chinese Abusive Speech Moderation

The widespread circulation of abusive online content has increased the need for reliable moderation of Chinese social-media text. Existing…

Bias-Corrected Ceilings of Emotion Predictability from Human Label Variation Based on Instance-Level Fano Bounds

Emotion recognition from text keeps improving on benchmarks, yet whether an accuracy ceiling has been reached is seldom asked with discipli…

Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts

Human Activity Recognition (HAR) with self-administered wearables, such as at-home rehabilitation and exercise monitoring, often requires r…