週次AIニュース 2026-W30

Building AI infrastructure with the Effingham County community

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and acc…

How news organizations are using AI to advance their vital missions

News organizations are using AI to strengthen reporting, grow audiences, and improve business operations, with OpenAI tools supporting jour…

Advancing the next era of national science

OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier A…

Introducing OpenAI Presence

Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for custom…

Introducing the ChatGPT for small business program

OpenAI launches the ChatGPT for Small Businesses program, helping entrepreneurs build AI skills, automate work, and grow with ChatGPT Work.

Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.

OpenAI and Hugging Face partner to address security incident during model evaluation

OpenAI and Hugging Face share early findings from a security incident during AI model evaluation, highlighting advanced cyber capabilities…

Safety and alignment in an era of long-horizon models

OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards thro…

Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission

Google commits $40M in AI tokens and credits for the Genesis Mission

MicrosoftやNVIDIAなど、AIのオープンウェイト規制に反対する書簡を公開――Anthropicは署名せず

MicrosoftやNVIDIA、Metaなど30社以上の米国の企業や団体が、オープンウェイトAIモデルへの過度な規制回避を求める共同書簡を公開した。オープンモデルをAIエコシステムの基盤と位置付け、開発や評価におけるメリットとイノベーション促進を強調。中国企業の急速な台頭や技…

Monday.com is the latest tech company to blame AI for layoffs — here are 20 others

A running look — in reverse chronological order — at the bigger tech companies that have announced significant layoffs this year with AI as…

MicrosoftやNVIDIAなど、AIのオープンウェイト規制に反対する書簡を公開――Anthropicは署名せず

MicrosoftやNVIDIA、Metaなど30社以上の米国の企業や団体が、オープンウェイトAIモデルへの過度な規制回避を求める共同書簡を公開した。オープンモデルをAIエコシステムの基盤と位置付け、開発や評価におけるメリットとイノベーション促進を強調。中国企業の急速な台頭や技…

Librarians are hosting viral ‘Avoiding AI’ workshops for people who are fed up with Big Tech

At libraries around the country, "Avoiding AI" workshops have elicited unprecedented demand.

One fallen power line exposed a growing AI data center problem. Here’s how to fix it.

A close call in Northern Virginia revealed just how poorly data centers respond to grid disruptions. Here's how to fix the problem.

I tried out OpenAI’s new AI keypad — which will be fun for some coders and slightly mystifying to everyone else

OpenAI's fancy new AI keypad will be a lot of fun for some, while many others are probably not going to touch it.

Anthropic、「Claude Opus 5」公開　Fable 5に迫る性能を半額で――サイバー安全策は緩和、拒否時は自動フォールバックも

Anthropicは、最新LLM「Claude Opus 5」を公開した。上位モデル「Claude Fable 5」に迫る知能を半額の価格で提供する。プログラミングやナレッジワークにおいて高い評価を獲得し、推論の深さを調整するパラメータや安全性分類器に連動する自動フォールバック…

Prentis, new AI lab co-founded by Reid Hoffman, Mark Pincus in talks to raise $100M

The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.

Why Cognition bought Poke: AI personality is becoming a competitive advantage

The acquisition brings Poke’s conversational style and interaction model to Cognition’s coding agent Devin, reflecting a growing belief tha…

Anthropic launches Opus 5

Opus 5 will be both cheaper and less restrictive than Fable, likely making it preferable in most use cases.

As US weighs response to Chinese AI, industry urges against broad open-weight restrictions

AI companies, including Nvidia and Mistral, urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates re…

Bluesky’s AI assistant Attie expands into an open social research tool

Users can now ask Attie questions about news, trends, and conversations on Bluesky and other apps on the AT Protocol.

Midjourney acquired the astrology app Co-Star

The AI lab Midjourney continues to expand its purview beyond image and video generation.

‘AI communism’, rogue models, and the why Kimi K3 spooked Wall Street

Chinese AI lab Moonshot’s open model Kimi went viral this week for reasons that had less to do with the model itself and more to do with ho…

OpenAI’s new voice mode makes it to the ChatGPT desktop app

ChatGPT Voice on desktop can work with both ChatGPT Work and Codex to complete tasks and control agents.

スーパーに並んだ「ごちゃごちゃ生成AIポップ」が物議　“看板王”こと、きぬた歯科院長「これはアリ」

スーパーの青果売り場に並ぶ、生成AIで作ったとみられる派手な商品ポップがXで物議を醸している。吸血鬼や戦国武将を描いたデザインに「見づらい」との声が相次ぐ中、看板広告で知られるきぬた歯科のきぬた泰和院長は「これはアリ」と評価。その理由とは。

近畿大、入試にAIの利用認める　情報学部の総合型選抜で

近畿大学は、2027年度の情報学部の総合型選抜入学試験で、生成AIの利用を認めると発表した。提出する自己PR動画やプレゼンテーション資料などでのAIの利用方針を明示した。

AIにもサプライチェーン管理が必要？　中国AI「Kimi K3」を巡る批判でAIの調達リスクが浮き彫りに

中国の最新AIモデルを巡り、米政府高官が、Anthropicの「Claude Fable 5」をモデルの学習に利用した“不正蒸留”が行われていたと指摘した。AIモデルの調達や導入を巡るサプライチェーンリスクが浮き彫りとなっている。

メルカリ、「AI活用の最前線」明かす動画公開　「なぜCTOがCHRO兼CAIOになったのか」など13本

メルカリは、自社のAI活用について紹介する動画を公開した。7月8日に開催したイベント「Mercari AI Career Fes 2026」で実施したセッションのアーカイブ動画、全13本を公式YouTubeチャンネルで視聴できる。

海外の「Claude」や「GPT」ではダメなのか　日本企業向けai&、そのメリットは？

ai&は、日本企業向けに設計したAI推論プラットフォーム「ai& Inference」の提供を開始した。ClaudeやGPTなど海外ベンダーのAIに代わる選択肢として、AI利用コストを大幅に削減できるとしている。

開発工数見積もりの「負担が重い」をAIで解消へ　明治安田はどう実現？

有識者に頼りがちなシステム開発工数の見積もりは、AIエージェントでどこまで効率化できるのか。明治安田生命保険がPoCで検証した仕組みを見ていこう。

AINTMA: Agentic AI Architecture for Autonomous Test Management with Generative Intelligence, Secure Cloud Communication and Adaptive Quality Analytics

Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud envi…

Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts

Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-gen…

ClickGuard: Detecting and Spoiling Clickbait News with Informativeness Measures and Large Language Models

This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles. Moving beyon…

Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs

When a language model gives different answers on repeated runs, does that variation reveal what it does not know? Self-consistency turns th…

JAXBench: Benchmarking Autonomous TPU Kernel Optimization

Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on,…

DC-Leap: Training-Free Acceleration of dLLMs via Draft-Guided Contiguous Leaping Decoding

While parallel decoding is central to the efficiency of Diffusion Large Language Models (dLLMs), current strategies are often hindered by o…

InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents

AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed w…

DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions

Large language models (LLMs) handle many tasks with one set of parameters, but under KV-cached inference it is unclear what task-general st…

PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. Howeve…

Benchmarking the Personalization Capabilities of Large Language Models

Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has…

Robust Critics: Defending LLMs Against Multi-Turn Attacks

When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is…

Incomplete Prompt Jailbreaks in Large Language Models

Large language models (LLMs) are increasingly released as open-weight models with safeguards against harmful requests. Nevertheless, senten…

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

Natural language interfaces can greatly benefit the accessibility and usability of optimization modeling, and recent advances in large lang…

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

Sampling in LLM inference comprises a combinatorial set of logit processing, token selection, and verification operations for speculative d…

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios a…

Semi-Supervised Text-Attributed Graph Distillation

{\em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Ex…

Beyond Liars' Bench: The Impact of Lie Typology, Depth, and Sparsity on Deception Detection in LLMs

Training probes to detect deceptive outputs from large language models is still an open problem. Recent work has demonstrated that detectio…

Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference

Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet mai…

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on…

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

Recent advances in large language models have enabled web agents to autonomously execute complex tasks. In practice, users frequently provi…

Tractable Hierarchical Control of Autoregressive Language Models

Constraining the generation of autoregressive large language models (LLMs) is an important component of integrating language models into fo…

The Devil is in the Spectrum: Mitigating Representation Collapse in LLMs via Topologically Regularized Side-Path

Large Language Models (LLMs) are fundamentally limited by representation collapse, a bottleneck that severely degrades long-context perform…

Expectation Alignment of Language Models for Real-World User Expectations

Large language models (LLMs) have demonstrated remarkable performance on standard benchmarks, yet it remains largely unexplored whether the…

OPTScientist: Multi-Agent Discovery of Typed Optimizer Programs for Transformer Pretraining

Designing optimizers for modern deep learning remains a challenging scientific problem, requiring the joint consideration of optimization g…

Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering

Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent informat…

Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants

Multi-agent LLM frameworks typically fix their team topology at boot time. When an individual agent becomes overloaded at runtime, for exam…

EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generatio…

CRAWO: Custom Resources for Adaptive Workload Orchestration

Edge Intelligence has emerged as a key paradigm for enabling real-time applications in smart cities by shifting computation from centralize…

DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making

Standard evaluation benchmarks measure what a tool-using agent decides, not whether it arrives at that decision through the same process ea…

Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models

Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing. However, a…

Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversarial Mutation

Production LLM applications commonly stack a regex filter in front of model-side alignment; prior work found no measurable coverage gain fr…

Workload-Aware Caching for Multi-Agent Systems

Multi-agent systems decompose complex tasks into directed acyclic graphs (DAGs) of specialized agent executions, creating natural opportuni…

From Errors to Rules: Iterative Prompt Optimization for Text Classification

Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-dri…

AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs

Large language models (LLMs) augmented with tools are emerging as autonomous agents capable of using Web engine, APIs, and code to solve co…

ExecuGraph: A Multi-Agent, Execution-Grounded Framework for Reliable Backend Code Synthesis with Large Language Models

Large Language Models generate plausible backend code, but a single-pass paradigm provides no guarantee of correctness or runtime reliabili…

FlowEdit: Information-Theoretic Control of LLM Reasoning Flows for Ill-posed Problems Involving Conflicts

Large Language Models (LLMs) perform strongly on well-specified reasoning tasks with a feasible answer. However, problems encountered in th…

MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation

Despite rapid progress in LLM-based code generation, writing correct and performant kernels for hardware accelerators remains a key bottlen…

Inducing Comparability of Factorised Probability Distributions

To allow for principled comparison between two probabilistic graphical models defined over non-identical variable sets, they have to be lif…

LeanFlow: A Case Study in Workflow-Driven Lean Autoformalization

We present and evaluate LeanFlow, an LLM agent system specialized for translating mathematical papers into buildable Lean projects. Recent…

Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrieval

GraphRAG enables deeper reasoning by structuring knowledge as graphs but struggles with n-ary facts. HyperGraphRAG uses hypergraphs for ric…

MiniCache: Reusable Program Caching with Small Model Interfaces for Efficient LLM Inference

Large language models (LLMs) are increasingly used for program-aided reasoning, agentic decision making, and structured task execution, but…

Telco-GAIA: Bilingual Benchmark for Agents in Telecom Domain

We introduce Telco-GAIA, a bilingual, multi-modal benchmark for evaluating tool-using agents on the data of a real-world telecommunications…

SiGMA: Sign-Guided Merging and Adaptation for Multimodal Continual Instruction Tuning

Multimodal Continual Instruction Tuning (MCIT) is crucial for adapting Multimodal Large Language Models (MLLMs) to evolving a sequence of d…

Reliability-Aware LLM Alignment from Inconsistent Human Feedback

Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, it…

CANN Bench: Benchmarking Agent Generated Kernels against Real NPU and Algorithmic Limits

AI agents are now capable of writing, compiling, and iteratively optimizing low-level operator kernels on different hardware platforms. Exi…

Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving

Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equ…

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models

Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual r…

Autonomous disproofs of the sum-product conjecture over $\mathbb R$ with GPT-5.5 Pro

OpenAI's recent disproof of the Erd\H{o}s unit distance conjecture marked a milestone for AI in mathematics. It also inspired another break…

ConfidenceBench: Evaluating Confidence Calibration in Large Language Models

Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly. In these settings, acc…

Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis

Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their bench…

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deplo…

DynamicMCPBench: A Trace-Grounded, Effect-Scored Benchmark for LLM Agents over Live MCP Servers

Large language model (LLM) agents are increasingly deployed over Model Context Protocol (MCP) servers, yet the benchmarks used to evaluate…

AppWorld-UL: Benchmarking Diverse Agent-User Interactions for Tool-Use

Tool-use agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with…

StrideDiffusion: Accelerating Diffusion Models for Time-series Generation

Diffusion models have become competitive generators for time series, but their practical use is limited by the large number of sequential d…

CMI-Mem: Toward Generalizable Long-Term Memory Management via CMI-Augmented Reinforcement Learning

Memory Manager models are pivotal in agent systems. Existing methods rely predominantly on LLM-judged synthetic question-answer (QA) pairs,…

AI-Driven Multi-Hop Relay Selection for Smart Urban NR-V2X Networks via Learning-to-Optimize Graph Neural Networks

Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Uni…

KeySI: An Interaction Framework for Tuning Text Embeddings Based on Human Feedback

In large-scale text analysis tasks, pre-trained language models are often used to embed text corpora for downstream analysis. However, such…

WaveformQA: Benchmarking LLM Temporal Reasoning on Digital Waveforms

Large Language Models (LLMs) have demonstrated strong capabilities in code generation and reasoning, yet their ability to perform temporal…

NVIDIA-labs OO Agents: Native Python Object-Oriented Agents

Traditional agent development is split across prompt templates, tool schemas, callback code, and workflow graphs. We present NVIDIA Object-…

ArbiGraph: Arbitrarily Scalable Verifiable Task Graphs for Evaluating Context Management

We introduce ARBIGRAPH, a benchmark generator for evaluating whether tool-assisted language agents can retain, update, compose, and discard…

The Human-AI Substitution Principle: When will you be replaced by AI in your organization?

Artificial Intelligence (AI) is rapidly transforming organizations, raising a fundamental organizational and economic question: when will a…

Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling

High-temperature sampling is one of the primary mechanisms for increasing diversity in LLMs. Recent advances in truncation-based sampling t…

Can an AI System Be Creative? A Critical Perspective from Art and Engineering

This paper examines the question of whether artificial intelligence (AI) systems can be creative, approached from the dual perspective of a…

Profiling Lightweight Large Language Models

Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing r…

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained…

Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks

Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensi…

Auditing Provenance Sensitivity in LLM Agent Action Selection

LLM agents choose tools and arguments from context that mixes user requests, tool outputs, retrieved records, memory, and untrusted text. E…

Auditing Evidence Use in Medical LLM Diagnosis

Medical LLMs are often evaluated by whether they select the correct diagnosis, but diagnostic accuracy alone does not show whether the mode…

Code Monitor Red Teaming for Public-Test-Passing Code

Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-…

Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions

Deep Research agents extend LLM-based assistants into long-horizon workflows involving planning, retrieval, evidence synthesis, and report…

Source-Prior-Driven Selective Adaptation for Efficient Diffusion Model Finetuning

Fine-tuning large diffusion models for new domains or styles involves a trade-off: improving target-specific generation often degrades the…

Traceable Scholarship: Page Anchors and Ariadne's Thread for Humanistic Inquiry in the Age of Generative AI

Generative AI lets large language models produce scholarly-looking text within seconds, yet fluency does not equal valid explanation. The d…

OPOD: On-Policy Omni Distillation

Omni-modal models can handle text, images, and audio in one system, but improving all of these abilities together remains difficult. Traini…

Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority

AI knowledge systems require representations of entity importance for retrieval, recommendation, evidence selection, and knowledge-intensiv…

SciExplore: Evaluating Autonomous Agents from Scientific Navigation to Information Integration

Scientific research involves complex information-seeking and reasoning workflows across heterogeneous sources. However, existing benchmarks…

Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AI

We are moving from an information age to the age of intelligence. A decade, or possibly less than that, data will not be the gold anymore r…

From Scalars to Time Series: Rethinking Implicit Neural Representations for Time-Varying Volumetric Data

Implicit neural representations (INRs) for time-varying volumetric data are typically trained using dense sampling over spatiotemporal coor…

Delivery, Not Storage: Cue-Anchored Working Memory as a Harness Property for Coding Agents

Coding agents ship with one kind of memory: documents. Instruction files, plan artifacts, and auto-written memory directories are deliberat…

Beyond Independent Optimization: Compression, MoE Routing, and Quantization Interactions in Multimodal Edge Intelligence

Efficient multimodal inference is increasingly constrained not only by model quality or FLOP count, but also by the cost of preserving, mov…

GuardianAgentBench: Where Agents Fail and How to Guard Them

As large language model agents increasingly operate autonomously with access to tools and external environments, ensuring their safe and re…

Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills

Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not j…

Naju: A Native Discrete State-Space Model with Independent Retention and Writing for Long-Sequence Memory

Long-sequence memory tracking places two opposing demands on a recurrent state: near-lossless retention of stored bindings over long horizo…

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coher…

EmoAgent-R1: Towards Multimodal Emotion Understanding with Reinforcement Learning-based Dynamic Agent Specialization

Multimodal large language models (MLLMs) have achieved impressive performance in multimodal emotion recognition (MER) tasks and lifted MER…

HiMe: Real-Time Self-Hosted Personal Agent Platform for Health Insights with Wearable Devices

Traditional approaches to wearable health signal analysis, such as smartwatches, are constrained by rigid analytical frameworks and limited…

Faster IndexTTS-2: Accelerating and Streaming Autoregressive Zero-Shot Text-to-Speech Synthesis on GPUs

Autoregressive text-to-speech models achieve strong naturalness but suffer from slow inference due to sequential token generation, limiting…

Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation

Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond is…

AttriMem: Attribution-Guided Process Feedback for Agent Memory Learning

Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging. A memory-construction policy decides what…

V-DEAL: Diagnosing Video Safety De-Calibration as an Understanding-Refusal Coupling Failure

As Video Large Language Models are increasingly deployed in real-world applications, ensuring their safety alignment has become critical. C…

SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environmen…

Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications

Recent advances have introduced speech-to-speech (S2S) conversational assistants capable of producing natural-sounding interactions, includ…

Explaining Weather Bulletins via ILP

Inductive Logic Programming (ILP) originated within the Logic Programming community in the Nineties as a framework for combining symbolic l…

Differentiable Logic Programming to Mitigate Reasoning Shortcuts in Neurosymbolic Systems

Neurosymbolic (NeSy) systems integrate neural networks with logical reasoning to achieve both generalization and interpretability, but rece…

Identifying Good Rules for Efficient SAT Encodings of Single-Constant Multiplication Using Machine Learning

The Single Constant Multiplication problem is a fundamental NP-hard optimization task in hardware design, which seeks to decompose a fixed…

Bound-Founded Semantics for Answer Set Programming with Difference Constraints: Preliminary Report

While the integration of linear constraints has significantly expanded the reach of Answer Set Programming (ASP), existing hybrid solvers o…

A New Well-Supported Semantics for Description Logic Programs

Description logic programs are a powerful formalism for combining rules with ontologies. The well-supported semantics for description logic…

How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming

Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge suppo…

ICAE-Bench: Evaluating Coding Agents as Interactive Project Builders

The recent emergence of vibe-coding workflows is changing what coding agents are expected to do. Instead of merely completing code under fu…

Logic Programming Semantics for Causal Processes

Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the…

BasketEvent: Understanding Who Did What and When in Basketball Videos

Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key ev…

An LLM-Driven Workflow for Automated Process Control Strategy Generation and Tuning from Dynamic Process Models

We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The w…

Expert Behavior Prior Reinforcement Learning

Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learn…

Regulating autonomous and agentic AI

Regulating activities where regulatees use autonomous and agentic AI is challenging. Regulatory assumptions about regulatee knowledge and c…

SPORD: A Simulation-Propose-then-OR-Dispose Approach for Supply Chain Planning

For years, supply chain planning at e-commerce firms has operated as a collection of isolated projects. Each planning task from static netw…

Towards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls

Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neu…

Multimodal Pretraining for Generalizable EEG Representation Learning

Electroencephalography (EEG) models used for epilepsy are often limited to specific datasets and tasks. This limited approach can make it c…

MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning

Self-supervised foundation models have recently shown strong potential for electroencephalogram (EEG)-based analysis. However, existing app…

Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog

Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning,…

Logical Regression for Planning with Axioms

In automated planning, logical regression is an operation that returns the most general condition necessary for an action to achieve a part…

PATS: Policy-Aware Training Scaffolding for Agentic Reinforcement Learning

In long-horizon LLM agent reinforcement learning, weak policies often repeat similar failures, producing uninformative rollout trajectories…

Bridging the Gap Between Plausibility and Admissibility: Constraint-Aware Flow Maps for Dynamic Graph Systems

Generative models can support decision-making under uncertainty by producing ensembles of plausible future system trajectories, but statist…

Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment

Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. Howeve…

Detecting LLM-Generated Tokens in Human--LLM Coauthored Text

The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM…

AREX: Towards a Recursively Self-Improving Agent for Deep Research

Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verify…

Agentic coding without the cloud: evaluating open-weight large language models on longitudinal data preparation tasks

Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based…

Toward Continuous Assurance for the Democratization of AI Agent Creation in Industry

AI agents are increasingly created inside organizations by non-engineering users through low-code, no-code, and conversational development…

Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems

Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their…

Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation

Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay it…

The Boundaries of Automation: A Theory of Persistent Human Participation

The rapid progress of AI has intensified the long-standing pursuit of automation: replacing human participation with algorithms wherever po…

MIRROR: Learning from the Other View for Multi-Modal Reasoning

Unlike large language models (LLMs) that exhibit strong reasoning capabilities, vision-language models (VLMs) struggle with visual reasonin…

OpenForgeRL: Train Harness-native Agents in Any Environment

Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, an…

Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning

Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducin…

Unsupervised Consensus-Based Anomaly Detection for Spatiotemporal Malaria Incidence in Ghana

A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transm…

Deblurring in the Wild: A Real-World Image Deblurring Dataset from Smartphone High-Speed Videos

We introduce the largest real-world image deblurring dataset constructed from smartphone slow-motion videos. Using 240 frames captured over…

Through-the-Earth Magnetic Induction Communication and Networking: A Comprehensive Survey

Magnetic induction (MI) communication (MIC) has emerged as a promising candidate for underground communication networks due to its excellen…

From Attention to Frequency: Integration of Vision Transformer and FFT-ReLU for Enhanced Image Deblurring

Image deblurring is vital in computer vision, aiming to recover sharp images from blurry ones caused by motion or camera shake. While deep…

Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations

Existing LLM hallucination mitigation methods, including prompt engineering and model optimization, either hardly alter models'internal kno…

Is MoE Routing a Huffman Code? Discovering the Frequency-Diversity Law in Chain-of-Thought

Mixture-of-Experts architectures have revolutionized scaling, yet the underlying logic of their routing remains a black box. In this paper,…

More Is Not More: What Matters for Diversity in LLM Opinions?

Large language models are increasingly used to simulate diverse human opinions in open-ended tasks such as synthetic surveys, focus group m…

LLM-INSTRUCT at UZH Shared Task 2026: Constraint-Aware Retrieval and Selective Debate for Paragraph-Level Argument Mining

We present LLM-INSTRUCT, the winning system for the UZH Shared Task at ArgMining 2026 on paragraph-level argument mining in UN and UNESCO r…

Moir: Let the Model Direct Its Own Story for Robust Cross-Domain Knowledge Editing

While language models remain frozen at their training state, the world evolves continuously. Knowledge editing has emerged as a key alterna…

Break Through the Compression Bottleneck: From Theory to Practice

As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memor…

Making Open-Source Text LLM Watermarks Durable Against Merging

Open-source LLMs (OSMs)arereaching near state-of-the-art performance, prompting prior works to trace the text they generate by embedding te…

Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models

Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this ass…

Preference Tuning as Spectral Update Reorganization

Preference-based post-training is usually understood through endpoint behavior, yet the learned update that produces this behavior remains…

Answer-then-Edit: Reasoning Skeleton Editing for Anti-Distillation with Preserved Utility

Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual proper…

Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception

Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally ind…

The Storyteller in the Model: Narrative Pattern Inheritance, Escalation Dynamics, and Alignment Governance in LLMs

LLMs are trained predominantly on human-authored text, yet the structural and narrative conventions embedded in that text are rarely examin…

A Knowledge-Injection Framework for Zero-Shot Adaptation of LLMs to Delirium Prediction

Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain…

Response drift across frontier large language models

All frontier large language models (LLMs) exhibit response drift -- producing outputs that deviate from expert-validated references -- yet…

RE-AD: Real-Time Requirement Adherence for Data Labeling

Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer…

Learn2Zinc: Fine-tuning Small Language Models for Text-to-Model Translation in MiniZinc

Large language models excel at code generation for mainstream programming languages but struggle with rare, domain-specific languages such…

Dropping the Anchor: Statistical Context Summarization for Distributed Systems via Pulsar Attention

Inference with large language models (LLMs) on long sequences is computationally expensive due to the quadratic complexity of self-attentio…

CAMeR: Keyword-Gated Hybrid Activation for Adaptive Memory Retention in LLM Agents

Large language model (LLM) agents operating over extended dialogues accumulate vast amounts of information, yet existing memory systems eit…

THOR: A Theta-Gamma Hierarchical Oscillatory Reasoning Framework for Multi-hop QA

Multi-hop question answering requires retrieving and integrating evidence from multiple contexts. Despite the rapid progress of current res…

Instruct-FD: Can Your Full-Duplex Speech System Follow Turn-Taking Instructions?

Current full-duplex (FD) spoken dialogue systems can produce fluid interactions, yet it remains unclear whether they can adapt their turn-t…

Can Valence Reflect Morality in Natural Language? A Preliminary Annotation Study

Present implementations of artificial intelligence (AI) ethics do not adequately take feelings, or affect, into account. If AI should be al…

Verifier-First Evaluation of Agentic LLMs for Infrastructure-as-Code Generation

Infrastructure-as-Code (IaC) generation from natural language requires satisfying provider schemas, dependency planning, and organizational…

PhantomFill: When the Form Demands an Answer, Language Models Invent One

Language models in production do not write prose. They fill forms: JSON fields, function arguments, extraction templates. We show that the…

The Active Ingredient in Muon's Grokking

The Muon optimizer reaches the grokking threshold on modular arithmetic faster than AdamW. Prior work attributes this to "spectral-norm con…

Scaling Closed-Loop Feature Channel Configuration with LLMs

Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths ca…

Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement

Large Language Model (LLM) ensembles are increasingly used to improve reliability by combining predictions from multiple LLMs. However, exi…

CLOE: Christoffel Loss Autoencoder for Anomaly Detection

Semi-supervised anomaly detection plays a key role in diverse fields such as process monitoring, healthcare, and finance. However, lightwei…

Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development

Many modern AI systems are designed to operate under diverse, open-ended, use-cases. To help generalize deployed systems, many deployed-sys…

Grounding Investor Views: Neural Predicates in the Black-Litterman Model

Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty…

A Graph Neural Network approach to zero-shot Digital Twins

Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlyin…

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether th…

Codec-Gauge: Learning Compression-Friendly Gauges for Transformer KV Caches

Long-context Transformer inference increasingly relies on KV-cache compression or quantization. Prior rotation and transform-coding results…

Leveraging Biokinetic Knowledge Priors for Data-Scarce Bioprocess Modeling

While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited. The reason is data s…

From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime

How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise sche…

HypNO: A Graph-Based Neural Operator with Physics-Informed Message Passing for Hyperbolic Conservation Laws

We introduce HypNO, a graph-based neural operator for scalar hyperbolic conservation laws. HypNO operates directly on a space-time graph of…

Improving Access to Essential Medicines via Decision-Aware Machine Learning

A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce…

When RLVR Shrinks the Reasoning Boundary: Diagnosing Pass@k Inversion

Reinforcement learning with verifiable rewards (RLVR) can improve one-sample accuracy while making a model worse under repeated sampling. W…

SOAP, Muon, and Beyond: Pushing LLM Pretraining Scales

Higher-order optimizers such as Muon and SOAP offer faster convergence than AdamW, but their computational cost and numerical stability cha…

Beyond SBDD: Geometric Deep Learning in Polypharmacology and Multi-target Drug Design

The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactor…

SenCos-GEM: SENet-Calibrated and Law-of-Cosines-Constrained Geometry-Enhanced Molecular Representation for Property Prediction

Effective molecular representation learning is crucial for accurate molecular property prediction. Recently, numerous self-supervised learn…

Monkey King Bang: A Unified Scientific Multimodal Foundation Model

Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar trans…

StabilityBench: Benchmarking Instability in LLMs

AI Assistants are increasingly deployed in high-stakes settings, such as healthcare or government services. Yet their real-world behavior r…

Joint Utilization of Geospatial and census proxies for Autoencoder-Assisted Downscaling (JUGAAD) of socioeconomic indicators in India

Monitoring poverty and food security indicators is imperative for addressing socioeconomic challenges in developing nations. A limitation i…

Geometric Configurations of Perturbed Jailbreak Prompts

Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major secur…

Bayesian uncertainty estimation improves clinical decision making in medical AI agents

Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atyp…

Foundation-model-guided radiogenomic discovery linking cancer genomes to cancer scans

The function of many genes is still unknown, and conventional driver-discovery methods, which rely on how frequently a gene is mutated, can…

When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers

When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm? We answer with four findings from…

RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring

Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic trainin…

Demonstrating GenDB: Instance-Optimized and Customized Query Processing Code Generation via LLM Agents

Traditional query processing engines require continuous development and extensions to support new techniques and user requirements, and in…

Frontier Financial Judgement: Can agents tell what might move a stock?

We introduce Frontier Financial Judgement, a challenging new benchmark developed in collaboration with professional equity analysts to asse…

Scaling Interpretable Transformers with Parity Bottleneck Layers

Language models are thought to exhibit the phenomenon of superposition, representing many more features than dimensions in their residual s…

SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking

Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often u…

Adaptive Multi-Horizon Reinforcement Learning

Effective decision-making in complex and changing environments requires balancing short-term and long-term consequences. In reinforcement l…

From Agent Failures to Text Policies: What Works and What Breaks

TextGrad improves language-model systems by revising text from feedback. Its core thesis is that natural-language feedback can act as a gra…

Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis

Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in…

DS@GT ARC at ImageCLEFmed GANs 2026: Geometric Filtering for Privacy-Preserving CT Slice Generation

We present a privacy-preserving framework for synthetic lung CT slice generation developed for the Image-CLEFmed GANs 2026 challenge. The a…

A Framework for Reputation Aware Uninorm-driven Consensus Algorithms for Blockchain Networks

The operation of blockchain is governed by consensus algorithms (CA). Several consensus mechanisms require significant computational power,…

U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation

Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks o…

Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse pro…

Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness

A record system declares when two records refer to the same entity, occurrence, scope, or rule. Its disclosed implementation mechanisms ind…

GPE: Evaluating Robust Evidence Aggregation for Fact Verification under Controllable GEO-Style Poisoning

Large language models increasingly use search tools to retrieve up-to-date information, introducing a new attack surface in which retrieved…

Self-Supervised Bio-Inspired Robotic Trajectory Planning with Obstacle Avoidance

Trajectory planning is a fundamental problem in robotics, requiring the generation of collision-free and efficient trajectories in a potent…

IssueTrojanBench: Benchmarking AI Coding Agents Against Malicious Issue Requests

AI coding agents powered by LLMs are increasingly integrated into real-world software development, where they generate, edit, and execute c…

Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles

Majority voting over LLMs is widely assumed to benefit from diversity, and diversity measures are used to choose which models to combine. W…

Emergent Compositional Skills in Mixture-of-Experts VLAs

We consider the problem of learning compositional robot policies end-to-end from expert demonstrations, without any pre-specified notion of…

HARP: The Human--AI Research Platform

Large language models (LLMs) have shifted human--computer interaction from `traditional'' interface journeys toward more conversational exc…

Robostral Navigate

Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains…

Synthetic minority data is redundant or invalid: a data-dependent validity theory and a de-biased test

For two decades, the standard remedy for class-imbalanced learning has been to fabricate synthetic minority examples, and the standard evid…

The Geometry of Personality: Activation Steering with Jungian Cognitive Functions

Activation steering enables control and interpretation of LLMs, yet existing work primarily models personality through static trait framewo…

Beyond Heavy Log Curation: Perplexity-Based APT Detection via Unsupervised, Context-Augmented Language Models

Advanced Persistent Threats (APTs) remain difficult to detect because only a small fraction of events in large-scale logs are attack-relate…

Multilevel Graph Wavelet Compressed Sensing with Scale-Aware Neural Recovery

Scientific machine learning methods such as neural operators and physics-informed neural networks have advanced engineering applications an…

Probabilistic Residual Learning for Online Recommendations

Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and item…

TwistedMerge: Certified Higher-Order Diagnostics and Abstention for Model Merging

Model merging combines independently trained or fine-tuned models, but pairwise alignability does not imply globally consistent alignment.…

Anti-Goal Reasoning: Rethinking the Theory of Goal Reasoning in Non-Axiomatic Logic

Goal reasoning in Non-Axiomatic Logic (NAL) explains how an adaptive system derives means for realizing desired events under insufficient k…

Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for opt…

Scientific exploration, collaboration and labor division in the large language model era

Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is ass…

Interaction Dynamics Modeling and Predictive Control for Safe Steerable Catheter--Tissue Interaction

Safe steerable catheter control is fundamentally a problem of interaction dynamics: the tip must follow a planned motion, remain compliant…

HyWorldVLA: A Vision-Language-Action Model with Hybrid World Modeling for Autonomous Driving

Vision-Language-Action (VLA) models augmented with world modeling represent a promising paradigm for end-to-end autonomous driving. While p…

Sparse Concept Channels in Frozen 3D CT Vision Encoders

Large vision-language models are becoming increasingly dominant in 3D medical image interpretation, but we rarely know which internal units…

Training Large Language Models for Self-Explanation Faithfulness

We propose a Reinforcement Learning (RL) method to directly optimize the faithfulness of self-explanations - the extent to which a model's…

TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning

Offline Reinforcement Learning (RL) agents are trained on fixed behavioral trajectories, which makes trajectory-level deletion important wh…

GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes

Preprocessing blood glucose time-series data is a critical yet often overlooked step in developing data-driven methods for diabetes managem…

Relative Value Learning

In reinforcement learning, critics typically estimate absolute state values $V(s)$, estimating how good a particular situation is in isolat…

Hardware-Software Co-Design for Float16 On-Device Training on RISC-V Single-Core

By leveraging standard RISC-V extensions, namely Zfh (scalar float16) and Zvfh (vector float16), this work proposes an open-source framewor…

Demographically-Informed Heat-Mortality Risk Curves via Risk Graph Neural Networks

Estimating heat-related mortality risk is a core task in environmental epidemiology, typically addressed with Distributed Lag Non-linear Mo…

One More Turn, Less Regret: A Regret-Based Multi-Turn Benchmark for LLMs' Clarification Policies

Ambiguous user requests make clarification a sequential decision problem for conversational LLM assistants: they must decide whether to ask…

CRAG-MM-Diagnostics: Enabling Stage-Wise Analysis of Knowledge-Intensive VQA

Knowledge-Intensive Visual Question Answering (KI-VQA) benchmarks evaluate Vision-Language Models (VLMs) as multimodal knowledge assistants…

Representative Sets in Propositional Abduction

The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given ma…

Case study: proving sqrt(2) irrational with LPTP and an LLM

We present the interactions with an LLM (Large Language Model) aiming at proving that the square root of 2 is not a rational number in an L…

Encoding Event-B Proof Rules in Prolog: An Interactive Sequent Prover for ProB

Event-B is a formal method rooted in predicate logic and set theory. We encoded over 600 proof rules in Prolog, enabling a systematic, comp…

Animation, Verification and Visualisation of Prolog Transition Systems with ProB

ProB is a Prolog-based model checker, animator and constraint solver for high-level formal specifications. One can also use ProB to animate…

Chess\_db: A framework for working with large chess game datasets

Chess is a two player strategic game that is embedded in classical AI culture as it was once the frontier for intelligent behaviour. There…

Case study: solving P-99 with LPTP and an LLM

Ninety-Nine Prolog Problems (P-99) is a famous set of Prolog exercises. We solved the first thirty three just by prompting an LLM (Large La…

Declarative Problem Solving in UAM Strategic Deconfliction

The growing demand for Urban Air Mobility (UAM) introduces significant challenges in airspace management, particularly within densely popul…

Towards a Certifying Grounder

Grounding, the translation of high-level theories into equivalent quantifier-free formulas, is a crucial step in declarative solving, yet i…

Hybrid MKNF with Classical Negation in the Rule Component

Hybrid MKNF knowledge bases under the well-founded semantics integrate Description Logics with Logic Programming. However, they do not supp…

Explainability Framework for Policy-Aware Autonomous Agents

In the field of Artificial Intelligence, an agent is a system which is able to autonomously make decisions in order to reach a desired goal…

Explainable Belief Harmonization under Dynamic Epistemic Partitions

Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assu…

slang.gr as a Large-Scale Crowdsourced Resource for Non-Standard Greek

Slang is a central component of everyday language, reflecting linguistic creativity, social identity, and cultural change, yet its dy- nami…

pAI-Econ-claude: A Gated Human-in-the-Loop Multi-Agent Architecture for AI-Assisted Economic Theory Development

In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-…

A Comparative Evaluation of Embeddings and LLMs in a Greek Book Publisher Setting - The CUP Dataset

We present CUP, a Greek book retrieval benchmark consisting of 868 catalog records and 104 expert-annotated queries with graded relevance j…

Multi-Task Learning for Heterogeneous Prediction from Video Game State with Transfer Learning

Multi-task learning (MTL) is a promising approach for prediction tasks derived from video game state data, as modern game telemetry provide…

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations. To evaluate u…

AI Assistants Overassist

Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance fr…

Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study

The reliance on unstructured free text for documenting clinical trial protocols creates a significant barrier to automated reasoning, cohor…

PC-Edit: Prompt-Contrastive Region Discovery and Region-Guided Editing

Replacing an object with one that differs in category or shape requires complete source removal, natural target formation unconstrained by…

GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolatio…

Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation

Autonomous AI agents increasingly execute actions, invoke tools, and operate on protected resources with limited human oversight. Existing…

From Static Bibliometrics to Dynamic Knowledge Graphs: An LLM-Powered Framework for Modernizing Science, Technology, and Innovation (STI) Analytics

Bibliometric indicators - citation counts, h-indexes, co-authorship networks - have long anchored science, technology, and innovation (STI)…

Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin

Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource…

M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data

Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for compr…

Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks

Deep neural networks encode complex representations, but deconstructing this internal knowledge remains a challenge. Given the link between…

DINOde: Continuous Vision-Text Alignment for Open-Vocabulary Semantic Segmentation

Open-vocabulary semantic segmentation (OVSS) leverages textual semantics to segment objects beyond predefined categories. While the self-su…

Mean-to-Score Discrete Diffusion: Posterior-Mean Denoisers for Score Entropy

Score Entropy Discrete Diffusion (SEDD) parameterizes discrete reverse processes with unconstrained positive score ratios. While positivity…

VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method

Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions. However, route-level instructions com…

When Are Reasoning-Based Guardrails Not Efficient? ResponseGuard: A Fast Vision-Language Guard for Real-Time Moderation

A vision-language AI assistant returns its answer as a stream of generated tokens. Therefore, a safety guard that watches that answer has t…

Cycle-Consistent and Uncertainty-Aware Neural Surrogates for Tokamak Edge Plasmas

The boundary and divertor plasma govern how a tokamak exhausts power and particles, setting heat fluxes, target conditions, and the onset o…

Token Budget Saturation and Mechanistic Early Detection of Reasoning Non-Convergence in Chain-of-Thought Models

Chain-of-thought reasoning models such as DeepSeek-R1-Distill-Qwen-7B exhibit a bimodal convergence pattern: generations either terminate w…

Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping

Video face swapping has no natural paired supervision: no real footage exists of one person's face performing another person's video. The s…

RUMBA: Russian User Memory Benchmark

The ability to handle long-term memory in LLMs is becoming increasingly critical, yet existing benchmarks remain English-centric and rely o…

Thinkink: 2D Spatial Ink-native Interaction with LLMs

People often use handwritten notes and sketches to externalize ideas for ideation. To integrate large language models (LLMs) into this prac…

Error Certificates for KV-Cache Eviction via Randomized Design

Deterministic KV-cache eviction keeps the top-$k$ tokens under an importance score and deletes the rest. We prove that this design cannot k…

Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections

Coordinating autonomous vehicles at unsignalized intersections remains a critical challenge for multi-agent reinforcement learning (MARL) s…

Artificial Epanorthosis: Why large language models overuse a classical rhetorical figure, and how to mitigate it

A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language m…

Improved lower bounds for the Shannon capacity of odd cycles

The Shannon capacity $\Theta(G)$ of a graph $G$ quantifies the maximum rate at which information can be transmitted with zero error over a…

GS-Agent: Creating 4D Physical Worlds With Generative Simulation

Creating dynamic and physically realistic 4D worlds from natural language descriptions is both fascinating and challenging. Traditional com…

ElasticTTT: Prior-Preserving Test-Time Tuning for Video Editing

Test-Time Tuning (TTT) on pretrained diffusion models has emerged as a powerful paradigm for video editing. However, there exists a foundat…

From Resource Flow to Executable Tests: Petri-Net-Guided LLM Test Generation for Concurrent Stateful Rust APIs

Concurrent stateful library APIs expose behavior through evolving resource ownership, lifecycle states, and competing interleavings. Large…

Visual Contrastive Self-Distillation

On-policy self-distillation (OPSD) is promising as it removes the external teacher required by on-policy distillation (OPD), yet it still n…

Beyond Sufficiency: Time Series Explanation with Counterfactual Necessity

Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's…

Synthetic data generation framework for quality control automation in gravure printing

Quality control in printing, particularly in rotogravure printing, still depends on slow, costly, and subjective manual inspection. Automat…

Barzilai-Borwein Fails Superlinear Convergence on an Open Set of Quadratics for Every Dimension $n\geq 4$

Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorl…

GraphVid: Interactive Graph-Controllable Video Generation

Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts…

3D-Aware VLMs with Implicit and Explicit Geometries

Despite rapid progress, most existing vision-language models (VLMs) built from 2D visual inputs often struggle when handling various 3D tas…

A Counterfactual Cause in Situation Calculus

Perhaps the most popular modern formulation of actual causality is the HP account by Halpern and Pearl. Recent advancement has focused on e…

Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models

Large language models (LLMs) are increasingly deployed in decision-support systems for high-stakes domains such as hiring and university ad…

From Checklists to Clusters: A Homeostatic Account of AGI Evaluation

Contemporary AGI evaluations report multidomain capability profiles, yet they typically assign symmetric weights and rely on snapshot score…

WebCoach: Self-Evolving Web Agents with Cross-Session Memory Guidance

Multimodal LLM-powered agents have recently demonstrated impressive capabilities in web navigation, enabling agents to complete complex bro…

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors…

Understanding Critical Thinking in Generative Artificial Intelligence Use: Development, Validation, and Correlates of the Critical Thinking in AI Use Scale

Generative AI tools are increasingly embedded in everyday work and learning, yet their fluency, opacity, and propensity to hallucinate mean…

StackingNet: Collective Inference Across Independent AI Foundation Models

Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these…

Diagnosing Pathological Chain-of-Thought in Reasoning Models

Chain-of-thought (CoT) reasoning is fundamental to modern LLM architectures and represents a critical intervention point for AI safety. How…

Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers

As chain of thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a pr…

Evaluating Risks in Weak-to-Strong Alignment: A Bias-Variance Perspective

Weak-to-strong alignment offers a promising route to scalable supervision, but it can fail when a strong model becomes confidently wrong on…

From Noise to Diversity: Random Embedding Injection in LLM Reasoning

Recent soft prompt research has tried to improve reasoning by inserting trained vectors into LLM inputs, yet whether the gain comes from th…

Knowledge Graph Re-engineering Along the Ontological Continuum (extended version)

Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG…

DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations

Modern artificial intelligence excels at prediction but cannot explain. From large language models to AI-for-science systems, today's machi…

HarnessX: A Composable, Adaptive, and Evolvable Agent Harness Foundry

AI agent performance depends critically on the runtime harness, comprising the prompts, tools, memory, and control flow that mediate how a…

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable…

ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents

Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory i…

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles. In…

A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management

We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared b…

The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation

LLM agent benchmarks measure task completion, reliability, and inference cost, but not the persistent data an agent run leaves on disk, inc…

Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents

Enterprise agents must follow long-horizon, conditional, safety-critical standard operating procedures (SOPs). We compile machine-readable…

Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis

This scoping review examines the relationship between Generative AI (GenAI) and agency in education, analyzing the literature available thr…

Deepfake Media Generation and Detection in the Generative AI Era: A Survey and Outlook

We survey deepfake generation and detection techniques, covering all deepfake media types: image, video, audio and multimodal content. We i…

Loss-Complexity Landscape and Model Structure Functions

We develop a framework for dualizing the Kolmogorov structure function $h_x(\alpha)$, which then allows using computable complexity proxies…

MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs

Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descri…

Drive As You Like: Multi-Head Diffusion with Reinforcement Learning for Personalized Driving

Despite significant progress, imitation learning-based autonomous driving planners remain largely restricted to reproducing high-frequency…

DynaMark: A Reinforcement Learning Framework for Dynamic Watermarking in Industrial Machine Tool Controllers

Industry 4.0's highly networked Machine Tool Controllers (MTCs) are prime targets for replay attacks that use outdated sensor data to manip…

Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs

Large Language Model agents achieve strong performance on multi-step reasoning and tool-use tasks, but their impressive capabilities typica…

Equivariant Conditional Diffusion Model for Head and Neck CT Image Synthesis from CBCT

Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy. It offers real time anatomical vi…

Simple Policy Gradients for Reasoning with Diffusion Language Models

Diffusion large language models (dLLMs) represent a promising alternative to autoregressive LLMs; however, the lack of effective post-train…

On the Granularity of Causal Effect Identifiability

The classical notion of causal effect identifiability is defined in terms of treatment and outcome variables. In this paper, we consider th…

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural bi…

TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework

Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability. For flexibility, age…

Minimum Bayes Risk Decoding for Error Span Detection in Reference-Free Automatic Machine Translation Evaluation

Error Span Detection (ESD) extends automatic machine translation (MT) evaluation by localizing translation errors and labeling their severi…

Vision-Language-Policy Model for Dynamic Robot Task Planning

Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robo…

Backpropagation-Free Test-Time Adaptation for Lightweight EEG-Based Brain-Computer Interfaces

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability,…

Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms dr…

NeuraLSP: A Neural Spectral Preconditioner for Accelerating PDE Solvers

Solving large-scale sparse linear systems originating from partial differential equations (PDEs) is a fundamental topic in high-performance…

PILD: Physics-Informed Learning via Diffusion

Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature lim…

Variational Speculative Decoding: Rethinking Draft Training from Token Likelihood to Sequence Acceptance

Speculative decoding accelerates inference for (M)LLMs, yet a training-decoding discrepancy persists: while existing methods optimize singl…

Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems

The pantograph-catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However,…

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents

Optimizing large-scale machine learning systems, such as recommendation models for global video platforms, requires navigating a massive hy…

OmniCustom: Sync Audio-Video Customization Via Joint Audio-Video Generation Model

Existing mainstream video customization methods focus on generating identity-consistent videos based on given reference images and textual…

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

General-purpose robot learning requires dense, instruction-conditioned feedback that can distinguish meaningful task progress from stalled,…

Benchmarking Unlearning for Vision Transformers

Machine unlearning (MU) refers to the post-training capability to remove (the influence of) training examples that are incorrect, biased, o…

What Matters for Simulation to Online Reinforcement Learning on Real Robots

We investigate what specific design choices enable successful online reinforcement learning (RL) on physical robots. Across 100 real-world…

AG-REPA: Causal Layer Selection for Representation Alignment in Audio Flow Matching

REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teac…

VPWEM: Non-Markovian Visuomotor Policy with Working and Episodic Memory

Imitation learning from human demonstrations has achieved significant success in robotic control, yet most visuomotor policies still condit…

SR-TTT Does Not Learn Retrieval: A Correction and Mechanistic Post-Mortem of Surprisal-Aware Residual Test-Time Training

Test-Time Training (TTT) language models replace the KV-cache with fast weights updated during inference, achieving O(1) memory but sufferi…

Evolutionarily Stable Stackelberg Equilibrium

We present a new solution concept called evolutionarily stable Stackelberg equilibrium (SESS). We study the Stackelberg evolutionary game s…

EZASP - Facilitating the Usage of ASP

Answer Set Programming (ASP) is a declarative programming language used for modeling and solving complex combinatorial problems. It has bee…

LinearARD: Linear-Memory Attention Distillation for RoPE Restoration

The extension of context windows in Large Language Models is typically facilitated by scaling positional encodings followed by lightweight…

ImplicitBBQ: Benchmarking Implicit Bias in Large Language Models through Characteristic Based Cues

Large Language Models increasingly suppress biased outputs when demographic identity is stated explicitly, yet may still exhibit implicit b…

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.…

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind

As large language models (LLMs) become the engine behind conversational systems, their ability to reason about the intentions and states of…

Towards Disentangled Preference Optimization Dynamics: Suppress the Loser, Preserve the Winner

Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based methods als…

Streamliners for Answer Set Programming

Streamliner constraints reduce the search space of combinatorial problems by ruling out portions of the solution space. We adapt the Stream…

SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety

Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning…

AI Security Policy Should Assess Systems, Not Only Models

We present swarm-attack, an open-source adversarial testing framework in which multiple lightweight LLM agents coordinate through shared me…

Understanding and Accelerating the Training of Masked Diffusion Language Models

Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs…

Constrained latent state modeling: A unifying perspective on representation learning under competing constraints

Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially obser…

Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora

Moral language is subtle and culturally variable, making it difficult to translate faithfully across languages. Idiomatic expressions, slan…

PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation

The growing complexity of quantum programming frameworks has exposed a critical limitation in existing large language model (LLM)-based cod…

The Sensation Modulating Network:Haltability as the architectural ground for object-directed phenomenology

We propose the Sensation Modulating Network (SMN): the cognitive agent as the whole body, organized at every scale by opponent dynamics, bu…

EvoSpec: Evolving Speculative Decoding via Real-Time Vocabulary and Parameter Adaptation

Speculative decoding accelerates Large Language Model inference through draft-then-verify generation, yet lightweight draft models face cou…

SymQNet: Amortized Acquisition for Low-Latency Adaptive Hamiltonian Learning

Adaptive Hamiltonian learning is central to calibrating and characterizing quantum devices. In an adaptive controller, choosing the next ex…

HAMON: Passive Optical Sequence Mixing for Long-Horizon Forecasting

Simple linear and frequency-domain models remain surprisingly competitive in long-horizon time-series forecasting, and recent mechanistic e…

CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield

Frontier language capability is usually bought with frontier compute; CHERRY shows a different trade. It is a sovereign Korean model family…

Is Agentic Code Review Helpful? Mining Developers' Feedback to CodeRabbit Reviews in the Wild

Agentic code review, where autonomous agents provide code review comments on pull requests, is increasingly integrated into development wor…

CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-Centric 3D Scene Generation

Challenges remain in ego-centric 3D scene generation due to limited view overlap and the dominant influence of individual perspectives on s…

On Pairwise Quantile Regression - Statistical Guarantees and Applications

Quantile regression provides a powerful tool for summarizing the conditional distribution of a real-valued random variable (r.v.) of intere…

AnchorPrune: Relevance-Anchored Contextual Expansion for Visual Token Pruning

Large vision-language models incur substantial inference costs because high-resolution inputs introduce thousands of visual tokens, many of…

WildTrace: Benchmarking Natural Evidence Trails in Long-Context Reasoning

Answering complex questions over long documents frequently requires integrating evidence that the source itself disperses naturally across…

A Sovereign, Open-Source Foundation Model for German and English

We present Soofi S 30B-A3B, a sovereign, open-source Mixture-of-Experts (MoE) hybrid Mamba Transformer foundation model for German and Engl…

「2日かかる攻撃が25分に」生成AIで“爆速化”するサイバー攻撃、パロアルトの識者が警鐘

パロアルトネットワークスの染谷征良氏（チーフサイバーセキュリティストラテジスト）は、生成AIの普及で変化するサイバー攻撃の動向と企業に求められるセキュリティ対策を、ソフトバンクの年次イベント「SoftBank World 2026」の講演で紹介した。

AIは“声で操作”する時代に？　ChatGPTとClaude、相次ぎ音声機能を強化

米OpenAIと米Anthropicが相次いで自社AIサービスの音声機能を強化した。

How AI guardrails are impeding the work of offensive cybersecurity researchers

We spoke with several cybersecurity researchers, who look for unknown vulnerabilities and develop tools to exploit them, about how OpenAI’s…

図面AIに「動かせる3Dモデル」の生成機能、関節や可動域を自動認識

renueは、2D図面から3D CADモデルを生成するAI「Drawing Agent」に、関節や可動域を読み取り、動かせる3Dモデルを生成する新機能「可動アセンブリ」を追加した。生成したモデルをAIが動かし、接合部の離れや回転中心のずれなども検証する。

Googleが“自社AIの裏切り”に備え始めた　異例の構想「AI Control Roadmap」とは

米Google DeepMindが発表した異例の構想「AI Control Roadmap」について解説する。

【元経産省の専門家に聞く】中小企業がハマる「生成AIトラブル」5つの解決シナリオ

キーマンズネットの読者調査には、生成AIを巡る中小企業の切実な悩みが数多く寄せられた。代表的な5つの「あるある課題」を、経済産業省・中小企業庁でデジタル活用支援に携わった小池明氏にぶつけ、明日から使える乗り切り方を聞いた。

「AIの提案」を妄信する人、疑える人――“眼力ある人材”を育てる絶対条件

プロンプト一つでUIやコードが数秒で量産される時代、人間の役割は「制作」から「目利き」へと変わる。しかし手を動かさなくなることで、AIの提案を無批判に受け入れてしまうリスクも漂う。米Figmaのロレダナ・クリサンCDOは「AIは過去しか見ない。世界を明日へ押し進めるのは人間だ」…

「生成AIで仕事が楽に」のはずが……IT現場を蝕む“AI疲れ・AIうつ”の正体

耳にする機会が増えた「AI疲れ」「AI鬱（うつ）」。本稿では、“疲れの正体”を整理し、個人が何を考え、どう変わればよいのかという判断軸を整理します。

三井不動産がデータセンターに6000億円超投資、物流の枠超え「産業デベロッパー」へ

三井不動産は事業説明会で「産業デベロッパー」への領域拡大を発表した。従来の物流拠点供給にとどまらず、研究開発施設や自動運転対応を進める。データセンター事業には累計6000億円超を投じ、稼働済みの3棟に加え7棟を開発中だ。

AMD takes on Nvidia with its Helios AI rack-scale system

AMD is challenging its chipmaker rival with a new rack-scale system that will start shipping to customers later this year.

Anthropic updates Claude voice mode with more capable models

Claude's new voice model will let you reschedule your meeting or draft an email.

AegisAI, founded by former Google security execs, lands $36M to stop AI-driven spear phishing

AegisAI co-founders developed AI agents that quickly analyze each message as a human would, paying attention to small anomalies that even t…

Runway launches AI model router as generative media gets crowded

The Media Router is a tool that automatically selects the best image, video, or audio generation model for a request based on whether a dev…

OpenAI makes ChatGPT Health available to all US users

Users can also integrate their personal data from services like Apple Health, Function, and MyFitnessPal.

Meta launched a new AI optimism ad set to a song about human extinction

David Bowie's song "Five Years," which Meta used in a supposedly inspiring advertisement, is about humans learning that they have five year…

Nvidia is sending GPUs to the moon

If there's a place in the universe without GPUs, Nvidia is sending them there.

AI chip startup Etched defies skeptics, hits $10.3B valuation from big-name investors

Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs r…

Google’s Gemini nears billion-user milestone

Gemini had over 750 million monthly users in February.

Experts say exploiting Anthropic’s Fable isn’t how Kimi K3 got so good

"I don't think you get a model this strong and this quickly on the heels of Fable doing strictly distillation," one expert told TechCrunch.

「スパイダーロボ」登場　がれきを走破、モノに「触って判断」も　災害現場で活用へ　国内ベンチャー

アトラックラボ（埼玉県入間郡）は、クモの形を模したロボットを開発したと発表した。実際のクモより2本少ない6本の脚を備えており、画像や触覚情報も処理できる。災害現場や危険区域などでの活用を目指す。

三菱電機とソニー、AIビジョンセンサーで新会社設立へ

三菱電機とソニーセミコンダクタソリューションズは、製造業向けAIビジョンセンサーソリューションを開発する新会社を合弁で設立する。新会社の社名は「Advanced Vision Solutions」で、2026年10月より事業を始める予定。

ServiceNow bets $40 million on Indian banking software specialist to expand its financial services push

ServiceNow's investment gives BusinessNext a strategic partner to expand its AI-powered banking software globally.

Markdownファイルが、AI時代の負債に？　Googleが提案する「ナレッジ標準化」の一手

Google Cloudは、AIエージェントが利用するナレッジをMarkdownで標準化するオープンフォーマット「Open Knowledge Format」を公開した。ベンダー非依存で、異なるエージェント間でもナレッジをそのまま共有できる。

FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads

Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challe…

Hybrid LSTM-Graph Neural Framework for Robust Financial Fraud Detection and Adversarial Resilience

Financial institutions face significant challenges in detecting sophisticated money laundering patterns, such as smurfing and layering, due…

OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks

LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instr…

Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX

Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect p…

FormulaSPIN: Self-Play Fine-Tuning for Natural Language to Spreadsheet Formula Generation

Spreadsheet applications are used by hundreds of millions worldwide, yet writing formulas remains a significant barrier. Existing approache…

Information Discernment in Large Language Models

LLMs are increasingly used with external knowledge sources like the internet. Do they weigh information appropriately -- updating more for…

NEXUS: Structured Runtime Safety for Tool-Using LLM Agents

Tool-using LLM agents increasingly execute high-impact actions, making runtime safety monitoring essential. We present NEXUS (Neural EXecut…

Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems

Recent advances in recommender systems (RS) have shown substantial performance gains through generative modelling. In practice, recommendat…

LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning

Recent advances in long chain-of-thought reasoning models such as DeepSeek-R1 have led to increasingly longer inference context lengths und…

Profile-Graph Memory for LLM Agents: Implicit Cross-Entity Traversal through Narrative Profiles

Long-term memory is essential for LLM agents that interact across sessions, yet current memory benchmarks primarily evaluate single-hop rec…

Lifted Representation Hypothesis in Language Models

Large language models (LLMs) often answer queries by mapping individual observations to more general rule-like structures. However, it rema…

GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methods

Graph RAG mitigates hallucinations and stale knowledge in LLMs, particularly for multi-hop question answering. However, existing approaches…

AdaRoPE: Not All Attention Heads Should Rotate and Scale Equally

Rotary Position Embedding (RoPE) is widely adopted in Transformers to encode positional information, yet standard implementations enforce a…

Statistically Grounded Sparse-Feature Interventions for Activation-Space Control in Large Language Models

Activation steering offers a lightweight alternative to fine-tuning for behavioral control of large language models, but SAE-based steering…

Logic-Guided Data Extraction with Answer Set Programming and Large Language Models

When Large Language Models (LLMs) are used for semantic data extraction from unstructured text, producing candidate relational facts from n…

Geometry-Guided Constraint Learning for LLM Safety Classification

Safety as Polytope (SaP) learns linear half-space constraints in LLM hidden space but requires per-category tuning of the constraint count…

Rethinking Uncertainty Evaluation in Large Language Models

Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depe…

Spectral-LSH: Sub-Quadratic Prompt Compression via Krylov-Projected Locality-Sensitive Hashing

Long-prompt inference remains expensive because prefill attention scales quadratically with sequence length. We propose Spectral-LSH, a tra…

Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models

Hidden-state probes often recover latent labels in imperfect-information sequence models, but this alone does not establish that a model ma…

Mitigating Scaffolding Collapse in Socratic Tutors via Representation Alignment

Large language model (LLM)-based Socratic tutors increasingly guide students through multi-turn questioning, but they can suffer from scaff…

Euclean: Automated Geometry Problem Formalization with Unified Verification in Lean

Recent formal reasoning systems have reached IMO-level performance, yet they leave a fragmented landscape: algebra and number theory are ha…

CrackedPDFs: A Controlled Benchmark for Hidden Prompt Injection in PDFs

Document-based LLM systems often flatten a PDF before guardrails inspect it. That step can discard evidence that an instruction was never v…

HyGRL: Adaptive Hybrid Graph Reasoning for Multi-Entity Questions

Multi-entity compositional questions pose significant challenges to existing retrieval-augmented language models. Conventional methods fall…

ITPEval: Benchmarking Formal Translation Across Interactive Theorem Provers

Formal theorem proving has emerged as a frontier challenge for machine learning, yet the ecosystem is fragmented: proofs remain siloed acro…

FORCE-Bench: A Benchmark, Dataset, and Evaluation Harness for Agentic AI in Enterprise Finance

Recent advances in large language models have accelerated deployment of agentic systems in operational finance. Existing benchmarks emphasi…

The Chronos Vulnerability: A Taxonomy of Temporal Persistence and Memory-Based Deception in Agentic AI

The transition from stateless generative models in artificial intelligence to stateful, autonomous agents represents an architectural evolu…

Sophisticated Policies from Epistemic Priors

Sophisticated Inference is a variant of active inference often associated with recursive belief modeling and tree search. We argue that its…

Knowledge-Centric Self-Improvement

Self-improving AI systems typically treat the agent as the object that improves, by optimizing prompts, workflows, harnesses, or even the a…

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications

Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneou…

Symbol and Footprint Database for Electronic Components by Agentic Recognition and Generation

A rich and recognizable component library is the cornerstone of printed circuit board (PCB) design and generation. Traditionally, engineers…

Silent Failures in Multimodal Agentic Search:A Diagnostic Taxonomy and Cross-Judge Evaluation

Multimodal agentic search systems increasingly rely on external tools to answer knowledge-intensive visual questions. However, existing eva…

Rewarding Better Thinking for LLM Preference Alignment

LLM preference alignment aims to optimize models toward human preferences across diverse user instructions. Reinforcement learning has beco…

Know Your Agent: Reconnaissance-Driven Pentesting of AI Agents

Traditional pentesting uses reconnaissance at each step to uncover unseen weaknesses, build stronger attacks, and advance the objective; we…

DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations

As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling gen…

JANUS: Foreseeing Latent Risk for Long-Horizon Agent Safety

Agent safety is moving from content moderation toward preventing operational failures before tool-using agents act. We propose Janus, a for…

Long-Term Sequential Decision Making under Risk

We study finite-horizon MDP planning under \emph{root-based} (resolute) risk objectives that apply a rank-dependent functional to the distr…

MOF-Sleuth: Tool-Grounded Reward Alignment for Explainable Fine-Grained MOF CIF Auditing

Large metal-organic framework (MOF) databases support simulation, screening, and machine learning through crystallographic information file…

SenWorld: A Digital-Twin Simulation for Generating Context-Rich Evaluation Data

Smartphone personal assistants reason over longitudinal personal data, yet evaluating them requires context-rich evaluation data whose corr…

EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference Optimization

Large Reasoning Models (LRMs) often suffer from overthinking due to redundant verification steps. Existing approaches for mitigating overth…

The Giant Hippocampus: From Structural Monoculture to a System of Systems

AI researchers describe state-of-the-art models as one thing repeated at scale: the Transformer, wired identically for text, pixels, or spe…

Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing

Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as ma…

CLARK: Closed-loop Learning for Adaptive Reasoning over Knowledge Graphs

Machine Learning models are widely used for automating classification tasks by extracting statistical patterns from data. However, their pe…

Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems

In modern IT operations (IT-Ops), the cost of an incorrect repair often exceeds the cost of no action at all. Yet existing automated remedi…

EvoDRC: A Self-Evolving Agentic Framework for Automated DRC Violation Repair

Design rule check (DRC) closure remains a major bottleneck in advanced-node physical design. Although detailed routers are rule-aware, resi…

Global Difference Constraint Propagation for Constraint Programming

Difference constraints of the form $x - y \leq d$ are well studied, with efficient algorithms for satisfaction and implication, because of…

Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model

Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the govern…

PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning

Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM)…

TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty

Enterprise strategic decision support requires AI systems that are not only accurate, but also uncertainty-aware, risk-calibrated, explaina…

CUSUM-Shaped Inference-Time Monitoring and Targeted Re-Decoding for Quantized Small Language Model Reasoning

Quantized small autoregressive reasoning models can enter long, repetitive, or unproductive trajectories, yet inference-time compute is usu…

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning…

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…

SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data

In many reasoning problems, the premises are not observed as discrete symbols, but must be inferred from high-dimensional inputs. Further,…

Stateful Guardrails for Multi-Turn LLM Systems: A Conversational Risk Accumulation Framework

Most safety guardrails for large language models (LLMs) evaluate each prompt-response pair in isolation, which misses failures that arise o…

Economic Evaluations of Language Models

Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuab…

Challenges of Explainability in Continual Learning for Time Series Forecasting

Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring r…

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses

Surrogate modeling for high-dimensional nonlinear dynamical systems that exhibit chaos requires mechanisms that preserve not only pointwise…

Simulating Eutopia: Revisiting Long-term Fairness with Outcomes, Performativity, and Dynamics

As AI-driven Decision Makers (ADMs) influence our socioeconomic reality, their roles in both enhancing efficiency and amplifying the social…

LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning

Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to ever…

Decodable but Not Detectable: A Leakage Fingerprint for Near-OOD Benchmarks

While auditing a perturbation-based OOD detector on a document benchmark, we recorded an AUROC of 0.326 -- well below the 0.5 chance level.…

Cross-Subject Semantic Decoding with Shared-Space Alignment for Generalized Neural Representation Learning

Generalizing across subjects remains challenging in invasive neural recordings because electrode configurations, anatomical structures, and…

From Trajectories to Prefixes: Reusing Teacher Trajectories via Replayed Prefixes and Online Continuation

Small language models are attractive backbones for interactive agents, but direct distillation from strong teacher trajectories often turns…

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models

It is commonly observed that online reinforcement learning (RL) produces better-performing strategies than offline methods across a broad r…

Recovering Clinical Utility Under Differential Privacy: Empirical Validation of Adaptive Federated Aggregation on Heterogeneous Cardiovascular Datasets

Validating federated learning frameworks on real clinical data is an essential step between proof-of-concept demonstrations in controlled s…

Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting

Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learn…

Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology

High-content morphological profiling (Cell Painting) yields sensitive, high-dimensional signatures of cellular state, but translating longi…

Opto-ViT-v2: Noise-Resilient On-Chip Fine-Tuning for Photonic Near-Sensor Vision Transformer Accelerators

Silicon-photonic (SiPh) accelerators have emerged as a promising platform for Vision Transformer (ViT) inference by performing matrix multi…

JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models

The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, lead…

Making Single-Cell Data Distillation Auditable: Traceable Real-Cell Coresets via Discrete Min-Max Selection

Single-cell datasets are increasingly costly to store, audit, and reuse for model training. Dimensionality reduction and dataset distillati…

ChannelGuard: Safe Models Do Not Compose into Safe Multi-Agent Systems

Multi-agent LLM applications chain a planner, worker agents, a verifier, and a synthesizer, and every hop between agents is an unmonitored…

BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator

Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand,…

ChainWatch: A Kill Chain-Aligned Sequential Detection Framework for Multi-Step Attacks in MCP-Based AI Agent Systems

The Model Context Protocol (MCP) is an open-source standard that allows AI agents to connect to external tools, databases, and services. Wh…

Building Trust in Autonomous Commerce: A Verifiable Global Event Timeline and AI-Ready Fraud Intelligence Layer

Agentic commerce protocols such as AP2 and ACP define mechanisms for secure agent-initiated transactions but do not provide interoperable,…

BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators

Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix un…

Unlearning as Distribution Restoration: A Controlled Counterfactual Study, a Validated Selective Screen, and the Limits of Oracle-Free Certification

Machine unlearning is commonly evaluated by matching a retrained oracle on trained probes. In a controlled nonce-fact testbed with a matche…

Guardrails as Scapegoats: Auditing Unfaithful Safety Refusals in Tool-Augmented LLM Agents

Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or explicit tool crashes, leaving silent inf…

REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning

Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and a…

Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models

We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positiv…

MoA-Structured Decode Attention DNF Derivation, KV-Cache Accumulation, GQA/MQA, and OpenACC Kernel

We derive four memory-optimal inference artifacts for transformer attention using the Mathematics of Arrays (MoA), each following directly…

ModPack: An Extensible Teleoperation Interface for Bimanual Mobile Manipulation

Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability.…

Integrity of peer-to-peer distributed LLM inference under malicious nodes

Peer-to-peer distributed inference executes a Large Language Model (LLM) on pooled consumer hardware by spreading its layers across many no…

Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight clas…

SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by r…

D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving. Howe…

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

Federated learning has emerged as a potential solution to privacy concerns associated with using sensitive health data for training predict…

Fine-grained Computation-Communication Overlap via Tile-level Signaling and Scheduling for Mixture-of-Experts

Mixture-of-Experts (MoE) architectures increase model capacity without proportionally increasing computation cost and have become a key bui…

Juxtaposition of Shallow Reservoir-Triggered Seismicity and Deep Tectonic Locking in the Qiaojia-Dongchuan Seismic Gap

Identifying the critical state of mature seismic gaps is challenging, especially when anthropogenic stress perturbations, such as reservoir…

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opa…

SCPP: A Unified Python Library for Soft Clustering

In this paper, we present SCPP (Soft Clustering Python Package), an open-source Python framework for soft clustering. SCPP establishes a ca…

Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub

Federated Learning (FL) enables collaborative model training without centralizing raw data, but building and operating FL systems remains d…

Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts

Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request infor…

Anatomy of a Sound Neural Reasoner: One-Shot Amortization, First-Pass Poisoning, and Search Inertness in Clue-Rich Completion

Neural solvers are built to deduce, branch, and revise intermediate states. The Lattice Deduction Transformer (LDT) appears to do exactly t…

PerfAgent: Profiler-Guided Iterative Refinement for Repository-Level Code Optimization

Large language model (LLM) agents now perform well on correctness-oriented repository-level tasks, including SWE-Bench issue resolution and…

FedLSG: LLM-Enhanced Semantic Calibration for Federated Graph Backdoor Defense

Federated Graph Neural Networks (FedGNNs) are highly vulnerable to backdoor poisoning, yet existing defenses typically rely on rule-based a…

Reference-Free Evaluation of Reasoning in Open-Ended Question Answering

AI-generated answers in high-stakes domains are often fluent but difficult to verify, especially when they contain multi-step reasoning rat…

SLPO: Scaling Latent Reasoning via a Surrogate Policy

Reinforcement learning with verifiable rewards has become the predominant recipe for eliciting test-time scaling in explicit Chain-of-Thoug…

PhenSPINE: A Standardized Benchmark for Spine Pathology Diagnosis

The accurate diagnosis of spinal pathologies depends heavily on radiological interpretation, yet automated systems are hindered by the lack…

Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education

The rise of generative AI (GenAI) in higher education has prompted urgent debates surrounding academic integrity and ethical use. This stud…

Personalized Recommendation Tool Learning via Autonomous Language Agents

Although large language models (LLMs) have recently gained traction in recommender systems due to their strong reasoning capabilities and e…

An Automated Framework for Extracting Reachable Attack Chains from Cyber Threat Intelligence Reports

Cyber Threat Intelligence (CTI) reports richly describe real-world attack processes, but their unstructured narratives cannot be directly u…

The World Model Remembers, the Actor Forgets: Dream Rehearsal for Continual Model-Based RL

Model-based reinforcement-learning agents of the DreamerV3 family forget catastrophically when trained on task sequences, even when an unbo…

Learning the Arabic Dialect Continuum as a Continuous Space: A Regression Approach to Speaker Origin Prediction

We present a regression-based approach to Arabic dialect geolocation that models dialectal variation as a continuous geographic space rathe…

Convergence-Latency-Aware Adaptive Modulation and Resource Allocation in RIS-Assisted Wireless Federated Learning

Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireles…

An Isotropy-Preserving Spectral Cap for Muon: Theory and Three Case Studies

Muon and related matrix-sign optimizers are increasingly used to pre-train large language models, but their effect on the internal geometry…

RPPNet: Perceptually-Grouped Rhythm-Pitch Primitives for Long-Term Structure Melody Generation via Boundary-Aware Modeling

Existing symbolic music generation models typically use bars as the basic structural unit. However, human perception of musical phrases oft…

Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover sema…

OPIUM: Mitigating Steering Externalities and Over-Refusal via Dual Objective Latent Optimization

Activation steering provides a lightweight mechanism for controlling large language models at inference time, but steering vectors can have…

Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes

Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly fr…

Sentence Splitter: Uncovering Latent Factual Structure for Self-Supervised Learning

This paper introduces Sentence Splitter, a self-supervised framework built upon a T5-based encoder--decoder architecture for uncovering the…

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show grea…

Overview of FinMMEval 2026 Task 1: Multilingual Financial Multiple-Choice Question Answering

FinMMEval 2026 Task 1 evaluates multilingual financial multiple-choice question answering in English, Chinese, Arabic, and Hindi. The task…

Memory-Augmented Multimodal Large Language Models for Small Object Understanding in Streaming Aerial Videos

Language-guided aerial perception aims to understand user-specified tiny targets in complex unmanned aerial vehicle (UAV) scenes. In real U…

PRISM-DR: Per-lesion Retinal Inference with Specialist Models for Diabetic Retinopathy

Diabetic retinopathy is a leading cause of preventable blindness; its early lesions are small, low contrast, and easily missed in manual sc…

Overview of FinMMEval 2026 Task 2: Multilingual Financial Short-Answer Question Answering

FinMMEval 2026 Task 2 evaluates short-answer financial question answering over multilingual evidence. Each final-test item pairs an English…

Defense Against LLM Backdoors using Critical Neuron Isolation Pruning

Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generall…

OSVE: One Step Video Editing with One Step Diffusion Models

Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion. We present OSV…

A Framework of User Experience Principles for Human-AI Agent Interaction in the Workplace

As AI agents become integral to business workflows, establishing guiding user experience (UX) principles is crucial for ensuring user trust…

G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection

This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detec…

When Does Knowledge Distillation Hurt? Reliability-Aware Distillation for Low-Resource Language Summarization

Knowledge distillation (KD) is a standard approach for compressing sequence-to-sequence models, but its per-sample effects are rarely exami…

HijackKV: New Threat in Position-Independent KV Cache Reuse

Key-Value (KV) cache reduces inference latency in large language models (LLMs). Traditional prefix-based reuse has low cache hit rates acro…

When Shippers Become Algorithms: Candidate Exposure, Information Design, and the Concentration of LLM-Mediated Freight Markets

Shippers are beginning to delegate carrier selection to large language model (LLM) agents. We ask what such delegation does to a freight ma…

Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models

The rapid proliferation of data-intensive applications, cloud infrastructure, and IoT ecosystems has made proactive resource provisioning c…

TINY_SCHILLER: A Drop-In German Drama Corpus for Small Language Models

tiny_schiller closes the small-language-model prototyping, fine-tuning, education, and research gap for German literary text, providing a s…

Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?

Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to…

Post-Training in Time Series Foundation Models: A Unifying Framework

Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insuf…

Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection

An increase in advanced Android malware requires the use of deep learning models, which can run on Android devices. But there is a trade-of…

Drift-Aware RL-based Wavelet Denoising for Network-Traffic Anomaly Detection

Traffic-utilisation measurements for network monitoring are corrupted by additive noise and statistical drift: time-dependent change in the…

A Systematic Benchmark of Intensity Normalisation Methods for 3D Knee MRI Segmentation and Cross-Domain Generalisability

Robust out-of-the-box performance is essential for the clinical deployment of deep learning models in medical imaging. An important but und…

Test Case Prioritization for DNNs via Neural Collapse Instability

With the widespread deployment of deep neural networks (DNNs) in safety-critical domains, reducing the cost of model validation under limit…

Language-Specific versus Cross-Lingual Knowledge Graphs for Implicit Aspect Identification in Arabic: A Comparative Study of Reasoning and Adaptation Strategies

Aspect-based sentiment analysis (ABSA) in Arabic must recover both explicitly stated aspects and implicit aspects that are never named in t…

Co-Evolving LLM Evaluators and Policies via DynamicRubric

Post-training with evaluator feedback on policy-induced samples serves as a major mechanism for improving large language models. As policie…

Reinforcement Learning for Large Language Model Selective Evidence Adoption from Contaminated Retrieval Results

Retrieval-augmented large language models frequently face contexts that interleave useful evidence with misleading statements or instructio…

ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models

Contextual entrainment is the tendency of a model to let auxiliary context in its input pull its output, independently of whether that cont…

PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted…

Formal Foundations for Known Good Reliable Die Screening in Chiplet-Based AI Systems-on-Chip

The rapid growth of chiplet-based artificial intelligence systems-on-chip (SoCs) has exposed a fundamental gap in semiconductor test method…

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…

Active Inference as a Convex Markov Decision Process

Active Inference (AIF) frames adaptive behavior as the minimization of expected free energy (EFE), combining epistemic and pragmatic object…

Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning

Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio rea…

StreamHOI: Interaction-aware Temporal Memory Adaptation for Streaming HOI Video Generation

Existing human--object interaction (HOI) video generation methods are largely limited to offline short-video generation with complex drivin…

The Quadrilateral Loss: Additivity as a Measurable Behavior of Dense Neural Networks

Additive models buy interpretability by forbidding feature interactions, a constraint that neural instantiations enforce architecturally. W…

ELSAA: Efficient Low-Rank and Sparse Attention Approximation for Training Transformers

The quadratic $N\times N$ attention score matrix remains a central obstacle to extending Transformers to longer input lengths. Existing eff…

Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis

Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours. While lar…

DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems

While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging d…

On the Systematic Challenges of Culturally Loaded Machine Translation: Dream of the Red Chamber as the Cultural Lens

Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural conte…

Pushing the Frontier of Full-Song Generation: Hierarchical Autoregressive Planning Meets Flow-Matching Rendering

In this report, we present a unified song generation framework capable of producing high-quality full-length music from lyrics, text descri…

The Ethics of Autonomous AI Agents for Offensive Security

LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly sc…

The Maskability Index: Predicting Task-Objective Alignment in Pretrained Language Models

Large-scale pretrained language models such as T5 and BERT have demonstrated strong capabilities for generating structured knowledge. Howev…

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervi…

Sound Probabilistic Safety Bounds for Large Language Models

We propose a novel framework for computing rigorous bounds on the probability that a large language model (LLM) generates harmful output to…

Courteous Anticipation: Improving Long-Lived Task Planning in Persistent Shared Environments

We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequ…

Don't Trust the Label: License Laundering in AI Supply Chains

AI artifacts move through a multi-platform supply chain, spanning datasets and models on Hugging Face and applications on GitHub. While eac…

Toward Reliable RGB-D Semantic Segmentation: Handling Missing Modalities via Condition Dropout

RGB-D semantic segmentation has achieved remarkable progress, yet most models assume that RGB and depth are always available. In practice,…

Understanding Generative AI-mediated User Engagement with Academic Library Resources

This study empirically analyzed generative AI as an emerging discovery pathway to academic library resources. Utilizing web analytics from…

Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail Humanoids

Closing the gap between benchmark performance and reliable real-world operation remains a central challenge for Vision-Language-Action (VLA…

Generative AI floods and dilutes the market for books

Generative AI can produce book-length works of fiction at near-zero cost. These books are often dismissed as low-quality ``slop'' that buye…

FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization

Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reco…

Persian Pixel: A large-scale synthetic OCR dataset for Persian language

Optical Character Recognition (OCR) for Persian remains substantially less mature than for Latin-script languages despite Persian being spo…

A Survey on Semantic Modeling for Building Energy Management

Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector. Although IoT technologies now…

Avoiding Obfuscation with Prover-Estimator Debate

Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly compl…

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review

Multi-Agent Reinforcement Learning (MARL) has achieved strong performance in simulated benchmarks, yet real deployments often violate the a…

SciTrek: Evaluating and Improving Long-Context Numerical Reasoning over Scientific Articles

We introduce SciTrek, a synthetic question-answering dataset for assessing and improving long-context numerical reasoning in large language…

In-the-Flow Agentic System Optimization for Effective Planning and Tool Use

Outcome-driven reinforcement learning has advanced reasoning in large language models (LLMs), but prevailing tool-augmented approaches trai…

Code-in-the-Loop Forensics: Agentic Tool Use for Image Forgery Detection

Existing image forgery detection (IFD) methods either exploit low-level, semantics-agnostic artifacts or rely on multimodal large language…

Fidelity Before Structure: Verbatim Chunks Beat Lossy Artifact Extraction in Long-Conversation LLM Memory

A growing class of conversational-memory systems compresses dialogue history into structured artifacts (extracted facts, decisions, or even…

Statistical Early Stopping for Reasoning Models

While LLMs have seen substantial improvement in reasoning capabilities, they also sometimes overthink, generating unnecessary reasoning ste…

Prompt Programming for Cultural Bias and Alignment of Large Language Models

Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural bi…

Crashing Waves vs. Rising Tides: Findings on AI Automation from Thousands of Worker Evaluations of Labor Market Tasks

We propose that AI automation is a continuum between: (i) crashing waves where AI capabilities surge abruptly over small sets of tasks, and…

Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data

To understand complex system dynamics in dairy farming requires tools that capture farm heterogeneity, social interactions, and cumulative…

Prober.ai: Gated Inquiry-Based Feedback via LLM-Constrained Personas for Argumentative Writing Development

The proliferation of large language models (LLMs) in educational settings has paradoxically undermined the cognitive processes they purport…

You Live More Than Once: Towards Hierarchical Skill Meta-Evolving

Test-time skill evolving is regarded as a new paradigm for enhancing deployed agentic systems. Existing works mainly focus on hard-coded sk…

CEO-Bench: Can Agents Play the Long Game?

Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service.…

Fara-1.5: Scalable Learning Environments for Computer Use Agents

Collecting computer use data from human demonstrations is expensive and slow, motivating the need for scalable generation strategies. This…

PedNStream: Scalable Network Flow Simulation for Pedestrian Traffic Management

Evaluating operational crowd management at network scale requires simulations that can be run repeatedly while adapting interventions to ch…

AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic

Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is chall…

Distributed Optimization via Energy Conservation Laws in Dilated Coordinates

Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretizatio…

Leveraging ChatGPT's Multimodal Vision Capabilities to Rank Satellite Images by Poverty Level: Advancing Tools for Social Science Research

This paper investigates the novel application of Large Language Models (LLMs) with vision capabilities to analyze satellite imagery for vil…

A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering

Nowadays, speech emotion recognition (SER) plays a vital role in the field of human-computer interaction (HCI) and the evolution of artific…

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks

Reticular frameworks hold promise for diverse sustainable applications, yet their immense chemical space limits efficient and systematic de…

On the Separability of Information in Diffusion Models

Diffusion models transform noise into data by injecting information that was captured in their neural network during the training phase. In…

Schr\"odinger Bridge Mamba for One-Step Speech Enhancement

We present Schr\"odinger Bridge Mamba (SBM), a novel model for efficient speech enhancement by integrating the Schr\"odinger Bridge (SB) tr…

PGTT: Phase-Guided Terrain Traversal for Perceptive Legged Locomotion

State-of-the-art perceptive Reinforcement Learning controllers for legged robots typically either (i) impose oscillator-or IK-based gait pr…

CGCE: Classifier-Guided Concept Erasure in Generative Models

Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised sign…

Matching Ranks Over Probability Yields Truly Deep Safety Alignment

Open-source Large Language Models (LLMs) play a critical role in the democratization of AI, yet their "open" nature introduces more avenues…

Dominant vs. Dominated: Concept-Level Generative Collapse in Diffusion Models

Text-to-image diffusion models have attracted significant attention for their ability to generate diverse, high-fidelity images. However, i…

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles o…

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework

Semantic Multi-Object Tracking (SMOT) is evolving from purely geometric localization toward comprehensive video understanding. However, exi…

Learning About Learning: A Path from Spin Glasses to Artificial Intelligence

The Hopfield model, originally inspired by spin glasses, occupies a central place at the intersection of statistical mechanics, neural netw…

Geometric Attention: A Regime-Explicit Operator Semantics for Transformer Attention

Geometric Attention (GA) specifies an attention layer by four independent inputs: a finite carrier (what indices are addressable), an evide…

Comparative evaluation of training strategies using partially labelled datasets for segmentation of white matter hyperintensities and stroke lesions in FLAIR MRI

White matter hyperintensities (WMH) and ischaemic stroke lesions (ISL) are key imaging biomarkers of cerebral small vessel disease (SVD) de…

A Sheaf-Theoretic and Topological Perspective on Complex Network Modeling and Attention Mechanisms in Graph Neural Models

Combinatorial and topological structures, such as graphs, simplicial complexes, and cell complexes, form the foundation of geometric and to…

In-Run Data Shapley for Adam Optimizer

Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley va…

LatentLens: Revealing Highly Interpretable Visual Tokens in LLMs

Transforming a large language model (LLM) into a vision-language model (VLM) can be achieved by mapping the visual tokens from a vision enc…

AgentCgroup: Understanding and Controlling OS Resources of AI Agents

AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers,…

Chimera: Neuro-Symbolic Attention Primitives for Trustworthy Dataplane Intelligence

Deploying expressive learning models directly on programmable dataplanes promises line-rate, low-latency traffic analysis but remains hinde…

NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering

Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by know…

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation

Graph neural networks frequently encounter significant performance degradation when confronted with structural noise or non-homophilous top…

SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost o…

Pre-Deployment Complexity Estimation for Federated Perception Systems

Edge AI systems increasingly rely on federated learning to train perception models in distributed, privacy-preserving, and resource-constra…

DocShield: Towards AI Document Safety via Evidence-Grounded Agentic Reasoning

The rapid progress of generative AI has enabled increasingly realistic text-centric image forgeries, posing major challenges to document sa…

Measuring LLM Trust Allocation Across Conflicting Software Artifacts

LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even…

Vocabulary Dropout for Curriculum Diversity in LLM Co-Evolution

Co-evolutionary self-play, where one language model generates problems and another solves them, promises curriculum learning without human…

Self-Preference Bias in Rubric-Based Evaluation of Large Language Models

LLM-as-a-judge has become the de facto approach for evaluating LLM outputs. However, judges are known to exhibit self-preference bias (SPB)…

A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics

Student dropout is a persistent concern in Learning Analytics, yet comparative studies frequently evaluate predictive models under heteroge…

An Auditable Policy-Simulation Framework for Student Dropout in Intervention-Free Data

This study proposes a temporal modeling framework with a counterfactual policy-simulation layer for student dropout in higher education, us…

Internal Knowledge Without External Expression: Probing the Generalization Boundary of a Classical Chinese Language Model

We train a 318M-parameter Transformer language model from scratch on a curated corpus of 1.56 billion tokens of pure Classical Chinese, wit…

Generative Augmented Inference of LLM-generated Data for Market Research: Theory and Empirical Evidence

Marketing research often relies on parameters estimated from costly human-generated data, such as conjoint survey responses, purchase decis…

Information Aggregation with AI Agents

Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by o…

SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection

Industrial surface defect inspection suffers from a fundamental data bottleneck: defects are rare, annotations require expert knowledge, an…

Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents

LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers…

Do Data Agents Need Semantic Metadata? A Comparative Study in Agentic Data Retrieval

In the era of autonomous agents, machine-actionable data is critical for data-driven workflows. For more than a decade, semantic metadata l…

Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)

Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing (NLP), although they remain suscep…

Will the Agent Recuse, and Will It Stop? Measuring LLM-Agent Compliance with In-Band Governance Signals at the Access Door and Mid-Flight

Autonomous LLM agents increasingly hold real credentials and operate infrastructure with no human in the loop, yet operators have no standa…

Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement

Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph struc…

LoRA-Tuned Large Language Models for Dementia Detection via Multi-View Speech-Derived Features

Early detection of dementia enables timely intervention, and reflecting cognitive impairment, spontaneous speech offers a non-invasive scre…

DART-VLN: Test-Time Memory Decay and Anti-Loop Regularization for Discrete Vision-Language Navigation

Memory-based discrete vision-language navigation (VLN) agents must act under partial observability, yet even strong frozen backbones remain…

Anatomically Faithful but Temporally Diffuse: Auditing Attribution for Left-Ventricular Ejection-Fraction Estimation from Echocardiography

Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attributio…

An Intelligent-Cloud Edge Multimodal Interaction System for Robots

Robust human-robot interaction in complex environments requires accurate gesture perception, semantic scene understanding, and reliable tas…

Macアプリ版「Claude Code」がiOSシミュレータと連携　「Computer Use」なしでアプリ操作

企業向けAIツールの成長率トップはAnthropic、アカウント数が最も多いのはMicrosoft 365　Okta調査

アイデンティティ管理サービスを提供する米Oktaは、同社のサービスを用いている2万社以上の匿名化されたアクセスデータに基づく、企業でのAIツール利用実態について調査結果を発表しました。

After shocking quarter, IBM insists that AI isn’t killing the mainframe

After IBM's stock crashed last week on warnings of poor mainframe sales, the CEO explained that AI wrecked corporate hardware budget, tempo…

「AI使うなら値引きできる？」の“暴論”に、日立はどう立ち向かう？　レガシー刷新でのAI活用の現在地

生成AIはレガシーシステム刷新の現場で具体的にどのように使われているのか。ユーザー企業自身がAIを使いこなす時代にベンダーが担う役割とは。

Google justifies its massive AI spending with a booming cloud business

Google's cloud business is thriving, as companies adopting its AI and AI infrastructure services help the tech giant to report record profi…

NVIDIAフアンCEOが語る“日本復活”のシナリオ　10年続く半導体バブルと「原発活用」の勝算

米NVIDIAのジェンスン・フアンCEOが来日し、日本経済の復活を宣言した。国内のAIインフラ構築へ数十億ドル規模の投資を発表。フアン氏は「何兆ものAIがAIを使う時代」の到来によって半導体需要は人口に制約されないと指摘。データセンターの電力不足に対して「原発活用」を日本の強み…

「天才デザイナー依存」の限界　AIで“平均点”しか出せない組織を変える「ノウハウ共有術」

生成AIの普及で“平均点デザイン”が量産され、プロダクトの同質化が進む。優秀な個人にノウハウが閉じる属人化も課題だ。デザインツール大手FigmaのCDOは、プロンプトやAIとの対話プロセスを含めた全体をチームで共有すべきだと訴える。1つのキャンバス上でデザイン、コード、AIをつ…

AMDとAnthropicが戦略的提携　「Helios」を最大2GW導入、最大50億ドルの出資も

AMDは、Anthropicとの戦略的提携を発表した。AnthropicはAMDの「Helios」および「Instinct MI450」シリーズを最大2GW規模で導入し、2027年上半期から順次展開する。AMDは最大50億ドルの株式投資を行うほか、Claudeを活用したGPU環…

Treasury threatens sanctions after White House claims Moonshot distilled Anthropic’s Fable

The episode has also intensified a broader debate in Washington over the influx of Chinese open models.

AI時代、開発チームの人材は“5つの型”に分かれる　Claude Code開発責任者の見立て

Claude Code開発責任者のボリス・チャーニー氏が、自身のチームで働く人は「5つの型」に分けられると指摘した。AIで職種の垣根が崩れ始めた今、肩書きではなく働き方で人を捉える新時代の発想を、初心者にも分かるように読み解く。

How OpenAI’s human mistake led to the AI-powered hack on Hugging Face

OpenAI made a mistake setting up what it called a “highly isolated” testing environment and sandbox. According to cybersecurity experts, th…

Travis Kalanick’s robotics company raises $1.7B, led by a16z

Uber is also investing in Travis Kalanick's company Atoms, which has made gauzy claims about using industrial AI to modernize the world.

Yope raises $12.3M to build a private social network without algorithms or ads

Yope, a fast-growing social app focused on private groups of friends and family, has raised $12.3 million in seed funding. Instead of chasi…

Monday.com lays off hundreds to focus on AI

The company said it is reducing its headcount by 20%, or about 630 staff, to "support a leaner, more focused operating model" as it focuses…

Arcee, a US open source AI lab, says Chinese models are not inherently dangerous

As Chinese AI models grow in capability and popularity among U.S. companies, the arguing over what should be done about them has reached a…

Substack’s new tool tells you who’s been writing their newsletters with AI

Substack is giving readers a way to estimate how much of a newsletter was written by AI, signaling a broader shift toward transparency arou…

OpenAI’s AI spending spree has ballooned to $750B

OpenAI will spend the equivalent of Sweden's GDP on infrastructure through 2030.

Menlo Ventures’ Matt Murphy explains what AI startups founders must do differently

Anthropic leaped to a $47 billion revenue run rate by May, compared to $9 billion in 2025. It’s the kind of growth that Menlo Ventures’ Mat…

Accelerating the frontiers of scientific discovery: Google’s $40M commitment to the Genesis Mission

Google commits $40M in AI tokens and credits for the Genesis Mission

The browser wars aren’t about search anymore — here are the best alternatives to Chrome and Safari

We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.

Passionfroot raises $15M to expand its B2B creator marketplace to the US

Passionfroot, a German startup building a marketplace connecting B2B creators with brands, has raised $15M in a Series A round led by Insig…

Building AI infrastructure with the Effingham County community

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and acc…

How news organizations are using AI to advance their vital missions

News organizations are using AI to strengthen reporting, grow audiences, and improve business operations, with OpenAI tools supporting jour…

Advancing the next era of national science

OpenAI outlines its commitment to advancing American science working with the U.S. Department of Energy and national labs to use frontier A…

Glow emerges from stealth at $1.2B valuation to challenge endpoint security in the AI era

Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.

Synthesia’s AI training platform is moving beyond videos into live coaching

Synthesia launched AI Roleplay Sessions, an interactive enterprise training platform where employees practice workplace conversations with…

OpenAIのモデルがサイバー攻撃能力評価中に暴走　テストの答えを求めてHugging Faceに侵入

OpenAIのAIモデルが、サイバー攻撃能力の評価中に隔離環境を突破し、Hugging Faceの本番インフラに侵入していたことが分かった。ベンチマークの解答を入手するため、ゼロデイ脆弱性の悪用や認証情報の窃取を重ねていたという。

Introducing OpenAI Presence

Introducing OpenAI Presence, a proven enterprise AI agent platform that helps organizations deploy trusted voice and chat agents for custom…

無料で身に付くデータサイエンス　延べ23万人が受講、総務省が募集開始

総務省は、データサイエンスオンライン講座「社会人のためのデータサイエンス入門」をリニューアルし、受講者の募集を開始した。慶應義塾大学の安宅和人教授など12人を講師に迎え、統計データ分析の基本を無料で学べる、社会人や大学生向けの入門講座だ。

SysAdmin: Measuring Instrumental Power-Seeking in Frontier AI

Power-seeking defined as behaviors where AI systems acquire resources, evade oversight, or resist termination beyond task requirements is i…

Calibrated Selective Fact-Checking via Evidence Chain Evaluation

Large language models (LLMs) can achieve strong fact-checking accuracy, yet forced binary decisions conceal a critical reliability problem:…

BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data

Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over e…

AI Tool Discovery at Scale: All You Need is DNS

The coming era of autonomous AI agents demands a discovery mechanism capable of navigating millions of tools, yet existing solutions buckle…

From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI

Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views…

SAAG: Structured Agent Assessment and Grounding

Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucina…

Phionyx: A Deterministic AI Runtime Architecture with Structured State Management and Pre-Response Governance

We present Phionyx, a deterministic AI runtime architecture derived from the broader Echoism interaction framework that introduces a govern…

Integro-differential equations in angular stabilization of drone motion by distributed feedback control

In this paper, we propose angular stabilization of drone motion using distributed feedback control in the form of an integral operator. It…

MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers

Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such…

Beyond Accuracy and Cost: Latency-Aware LLM Query Routing for Dynamic Workloads

Modern language query routers improve inference efficiency by assigning each query to a model that balances response quality and monetary c…

Cross-Dialect Generalization Without Retraining: Benchmarks and Evaluation of Schema-Derived Constrained Decoding for MLIR

Multi-Level Intermediate Representation (MLIR) underlies modern ML compiler infrastructure (TensorFlow, JAX/StableHLO, PyTorch Inductor, IR…

Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems

Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple a…

PEARL: Solver-in-the-Loop Interactive Optimization Modeling from Natural Language

Optimization modeling is the process of translating real-world decision problems, often described in natural language, into formal mathemat…

S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF

Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics. A key cont…

Probabilistic Concept-Aware Steering for Trustworthy LLM Inference

Steering vectors (SVs), an inference-time intervention technique for large language models (LLMs), guide the generation process by adding a…

FindStatBench: Evaluating Large Language Models on Combinatorial Code Synthesis

We introduce FindStatBench, an execution benchmark for evaluating large language models on combinatorial code synthesis. Built from FindSta…

When JSON Is Not Enough: Semantic Reliability of Schema-Constrained LLM Ordering Agents

LLM agents are increasingly used as transaction compilers: a user states an intent in natural language, and the model emits a structured ob…

ProbSPARQL: Querying Knowledge Graphs with Multi-dimensional, Uncertain Numeric Data

The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products. A central c…

Position: AI/ML Deepfake Research is Misaligned with AI-Generated Non-Consensual Intimate Imagery (AIG-NCII)

AI-generated non-consensual intimate imagery (AIG-NCII) is not adequately addressed in AI/ML literature regarding AI-generated media, commo…

MUX: Continuous Reasoning via Multiplexed Tokens

Language models solve complex problems by articulating intermediate reasoning steps in natural language. While effective, this process is c…

State Compression in Two-Agent LLM Relays: A Closed-World Study of Constraint Preservation

Long-running Large Language Model (LLM)-based agents often accumulate large intermediate traces containing audits, eliminations, and numeri…

Structured Synthetic Reasoning Data for Arithmetic Fine-Tuning of Small Language Models

Small language models are attractive for local deployment, but they often struggle with multi-step arithmetic reasoning. We study whether s…

Fence: Specialized SLM Guardrails for LLM Applications

Real-world applications that use closed-source large language models (LLMs) need advanced safety measures that go beyond the basic content…

Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles

The wisdom of crowds -- the finding that aggregating judgments across individuals often outperforms the best individual -- has been extensi…

Trajectory-Aware Clinical Risk Prediction via Severity-Grounded Knowledge Graphs and Retrieval-Augmented Generation

While Electronic Health Records (EHRs) offer a wealth of clinical data, effectively augmenting a patient's records with heterogeneous exter…

Using LLMs for Explainable, Data-Driven Insight Generation from Time Series

Time series forecasts are widely used in decision-critical domains, where they are rarely consumed without accompanying explanations. Produ…

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep t…

Operational Hallucination and Safety Drift in AI Agents

Large language models (LLMs) serving as planners in tool-using autonomous agents introduce dynamic reliability risks in multi-turn executio…

AlayaWorld: Interactive Long-Horizon World Modeling -- Full Technical Report

Unlike conventional video game development, which relies on labor-intensive pipelines for asset production, animation, physics, and program…

Neuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability

Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic…

MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning

Macro placement still requires substantial manual refinement in industrial physical design flows. We present MAGE (Macro Placement Agentic…

Engineering Trustworthy Agentic AI for Critical Systems

Agentic artificial intelligence systems, capable of autonomous perception, planning, tool use, and multi-step action, are increasingly prop…

Attacking Graph Foundation Models Through Their Shared Representation

A graph foundation model generalizes across graph domains by mapping every input into one shared representation before any task reasoning.…

When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization

Delay-risk models are usually judged by predictive accuracy. What matters in practice is narrower: with capacity to review only a few shipm…

SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring

Large language models (LLMs) increasingly support science, but they can also convert hazardous scientific knowledge into actionable misuse…

Semantic Primes as Explanans for Emotion in Large Language Models

Progresses have been made on understanding emotion mechanisms of large language models (LLMs). However, how to explain emotion in LLMs, or…

Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development

AI-native biotechnology companies are often designed by copying human biotech org charts into agent roles. We argue for a different abstrac…

DWM: Separating World Effects from Actions in Latent World Models

Latent world models underpin much of modern model-based control, yet current action-conditioned formulations supervise the next-latent tran…

One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization

Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relatio…

AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents

LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observabil…

SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval

As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable c…

AI Tour Meeting: Group Travel Planning by LLM Agents

This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents. The age…

Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety

Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks. We extend it to open-ended clinical conversat…

PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents

While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation…

NaviAIS: A Scenario-Level Vessel Trajectory Prediction Dataset withVectorized Lane Priors and the NaviLane Forecasting Framework

Vessel trajectory prediction in complex maritime environments is essential for traffic management, collision warning, route planning, and a…

Black-Mamba: Biologically-Inspired Leaky Accumulation for Conceptual Knowledge under Distribution Drift

Forecasting under real-world conditions is inherently non-stationary, as the conditional distribution of future observations evolves over t…

Enhancing Transformer-based Routing by Encoding Distance via Relative Positional Encoding

This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Prob…

OntoBook: Ontology-Grounded Synthetic Textbooks for Medical Encoder Pretraining

We present OntoBook, a method that converts medical ontology structure into pretraining signal for encoder language models. Our approach ha…

What General Intelligence Requires: Non-Reducible Constraints Across Levels of Description

General intelligence, of the kind that underwrites the full range of human cognitive achievement, is not a property of computational archit…

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix. Critics in the generative tradi…

Measuring Reward-Seeking via Contrastive Belief Updates

Language models trained with reinforcement learning may learn to optimize the grader's judgment rather than the intended objective. This "r…

Mi-Memory: A Lifecycle Memory Framework for Personal AI

Personal AI is moving beyond chat-only interaction toward continuous services that span phones, cars, homes, wearables, cameras, and tools.…

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning

Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contrib…

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio

Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge. As embodi…

Vector-Bench: Can Models Surgically Edit SVG Code?

Instruction-based vector editing requires two capabilities: making a requested change and leaving everything else alone. The second is easy…

Quality Action Assurance: Multimodal Verification of Examiner Claims in VR OSCEs

Objective Structured Clinical Examinations (OSCEs) are the gold standard for assessing clinical competence, yet scoring remains vulnerable…

On the Effectiveness of Pretraining for Graph Combinatorial Optimization

This paper introduces a self-supervised pretraining framework for graph combinatorial optimization specifically designed to address the nat…

Supra Cognitive Modes: A Routed Architecture for Agent Memory

Agent-memory workloads mix direct factual lookup, relation-chain and current-state reasoning, and broad synthesis over long histories. We d…

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation

Text-attributed graphs (TAGs) are an important graph data form that combine relational structure with rich node text. However, real-world T…

Comparative Study of Multi-Agent Actor-Critic Algorithms in Parameterized Action Reinforcement Learning

Parameterized action reinforcement learning has shown strong performance in environments requiring both discrete action selection and conti…

Sequential Learner Modeling Using Multi-Relational Graph Convolutional Networks

User modeling is a critical task in a variety of personalized systems. Recognizing their effectiveness in learning from graph-structured da…

BioSecBench-Surveillance: A Verifiable Benchmark for AI Agents in Pathogen Genomic Surveillance

As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance,…

Graph-Based Agentic AI with LangGraph: Workflow Pathways for Long-Running Stateful Business Processes

This paper is a practitioner guide to graph-based workflow pathways for long-running, stateful, multi-step generative AI systems in busines…

LLM Detection as an Intervention: Downstream Impact under Strategic User Behavior

As LLM adoption becomes more widespread, there is a growing interest in detecting LLM-generated content, for example through LLM detection…

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may…

Associative Emotional Learning in Convolutional Neural Networks

Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli.…

Agents in the Wild: Where Research Meets Deployment

Agentic systems large language model (LLM) based architectures capable of reasoning, planning, acting, and coordinating with tools and othe…

CodeRescue: Budget-Calibrated Recovery Routing for Coding Agents

Coding agents increasingly operate in executable environments where a failed attempt produces actionable feedback rather than merely an inc…

MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models

Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use…

Assistant or Actor? Student Trust, Control, and Delegation Regret When Using a General-Purpose AI Agent

When AI agents shift from answering questions to taking actions, users face a new problem: deciding what to delegate, to a system whose act…

The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems

The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everyt…

Market Strategy Evaluation for Prosumers in Local Electricity Markets

Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resource…

Domain Design for the Cops and Robbers Problem

Cops and Robbers is a well-studied problem in graph theory. The setting consists of a robber and one or more cops placed on an undirected g…

A Calculus of Discernment: Decision-Relevant Insight, Sequence Value, and Forgetting as Higher-Order Learning

In a world of generative AI, candidate insights are abundant; what is scarce is the capacity to discern which matter, to act on them in the…

FALCON-Discover: Discovering Concentrated False-Confidence Regions for Calibration

Calibration is usually evaluated in aggregate, but the most dangerous failures are often local: predictions that remain highly confident de…

Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification

Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and r…

Beyond Single-Dimensional Compression: The Compound Sparsity Frontier of Large Language Models

Large language models (LLMs) are often compressed through static parameter pruning or dynamic token-level computation, yet aggressive spars…

FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images

Accurate localization of the corpus callosum (CC) in fetal ultrasound (US) images is crucial for the early identification of neurodevelopme…

Compressing What Matters: Neuron Importance Meets Data-Aware Low Rank Approximation for Language Model Compression

To excel at their domain large language models are comprised of billions of parameters. Yet this comes at the cost of huge memory requireme…

Edge-Efficient Transformer for End-to-End RF Spectrum Monitoring

We present E-SpecFormer (Edge Spectrum monitoring Transformer) for end-to-end automatic modulation and covert channel (CC) recognition. We…

Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority

Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic…

BearingNAS: Obtaining In-Sensor Intelligent Fault Diagnosis Systems for Bearings Using a Laptop

This paper introduces BearingNAS, a Hardware-Aware Neural Architecture Search (HW-NAS) framework designed to shift the intelligence directl…

Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios

Continual anomaly detection (CAD) studies how models can adapt to evolving data distributions while retaining performance on previously obs…

SechKAN: Kolmogorov-Arnold Networks with Hyperbolic Secant Functions

In recent years, Kolmogorov-Arnold Networks (KANs) have attracted increasing attention due to their effectiveness in machine learning and s…

Dual-domain fused LSTM modeling for efficient time-dependent reliability analysis

Time-dependent reliability analysis is crucial for ensuring the long-term safety and performance of engineering systems under uncertainties…

Reliability Scales Inversely: Bigger Models Compound Mistakes Faster via a Hidden Auto-Regressive Risk Regime

As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability. The knowledge-gap account…

The Information Shadow: Measuring Structural Limits on What Language Models Can Learn

Some limits on what language models know are not gaps in data coverage but structural properties of learning from text. We introduce the in…

Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization

Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood. Standard SA…

Agentic Calibration of Grey-Box Simulation Models: An LLM-Driven Alternative

Calibration of grey-box simulation models is a constrained optimization problem in which model evaluations are expensive, the parameter spa…

Distribution-First Population Simulation: Collapse, Calibration, and Recall in Non-WEIRD LLM Persona Modeling

Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent. Using real survey microdat…

Approximating SPR Distance Between Phylogenetic Trees with Graph Neural Networks

Comparing phylogenetic tree topologies is essential for understanding epidemic dynamics, yet biologically meaningful distances such as the…

Binding Drift in Multi-Step Tool-Augmented Agents

Tool-augmented language-model agents execute multi-step workflows over external systems, resolving an entity once and then acting on it acr…

Cost Accounting for Reactive Computational Graphs: Exhaustive Sweeps, Sequential Mutation, and the Backward-Locality Gap

Exhaustive site-by-site interventions on a neural network's computational graph -- activation-patching sweeps, circuit-discovery searches,…

Hazard or Anomaly? Evaluating VLMs for Understanding Dangers and Discrepancies

Modern safety-critical systems increasingly rely on human-robot interaction to reduce disaster risk and support decision-making during emer…

Dynamic Loss Balancing for Joint SOH and RUL Prediction of Lithium-Ion Batteries via a Rotary SOH-Injected Prior Battery Transformer

The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of S…

Physics-Guided Masked Multi-Task Network for Edge-Friendly Battery Health Diagnostics from Sto-chastically Fragmented Charging Profiles

The deployment of reliable lithium-ion battery management systems is crucial for accelerating electrification, yet the joint prognosis of S…

ChemHyperMag: Physics-informed magnetic hypergraph learning improves molecular ADMET prediction

Accurate prediction of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) is important for drug discovery. Most predicto…

Quantum Cryptanalysis on IBM Quantum Hardware: Extending Even--Mansour Period Recovery from $N=4$ to $N=10$

We report genuine-un-compiled, textbook-faithful-quantum cryptanalysis of symmetric-cipher structures executed on real IBM quantum hardware…

PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption

Structured pruning is essential for making neural network inference feasible under homomorphic encryption (HE), yet its impact on model rel…

Federated Lightweight Fine-Tuning

Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, a…

FSDBN: Foreground-Aware EEG--Visual Alignment via Dynamic Brain Networks

EEG-based visual decoding provides a non-invasive pathway for interpreting visual semantics. However, existing methods often overlook the p…

Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models

Limited training data constrains deep learning models for Auditory Attention Decoding (AAD) in hearing aids (HAs). AAD uses electroencephal…

An Analysis of Residual-Stream Geometry Across Transformer Depth

We propose a transition-centred geometric analysis of transformer residual streams. Relative displacement measures how \emph{far} represent…

MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction

In traffic accident risk prediction, most studies overlook the extra noise that could be incorporated when fusing temporal features into sp…

Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions

Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and lat…

CODENS: Transforming Code Changes into Living, Accessible, and Queryable Documentation

Maintaining up-to-date code documentation is difficult in fast-moving repositories because design knowledge is scattered across source file…

Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments

Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute,…

HALLMARK: Diagnosing Three Failure Modes in LLM Citation Verifiers

Large language models (LLMs) now routinely draft literature reviews and assist with academic writing, which means a higher risk of fabricat…

Physical Self-Supervised Learning: IMU Sensing without Manual Labels

Deep neural networks have become a promising approach for IMU-based sensing, but their scalability is fundamentally limited by costly label…

A Controlled Study of Attention-Only Transformers

Feed-forward networks hold two thirds of a transformer's non-embedding parameters, yet the architecture has not received a necessity test t…

Adversarial Robustness of Phishing Email Detection: A Comparative Study of TF-IDF + Logistic Regression and Fine-Tuned DistilBERT

Phishing emails remain one of the most persistent cybersecurity threats, and machine-learning classifiers are widely used to detect them. M…

Intelligence from Learnable Novelty

Intelligence appears under different names in different fields: as data compression in statistics and machine learning, as universal comput…

Relay-Bench: Evaluating LLMs on Multi-Domain Reasoning Chains

Introducing Relay-Bench, an unsaturated, holistic, text-only benchmark that measures LLMs' ability to complete an assortment of tasks from…

ChainMark: Model-Free LLM Watermarking with Closed-Form Calibration

Regulatory regimes such as the EU AI Act mandate machine-readable marking of synthetic text, but existing watermark detectors rely on the g…

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders

Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Neare…

Estimating Rare Events in Language Models with Proper Evaluation

Quantifying the risk of rare failures in language models, such as those triggered by adversarial distribution shifts or very large-scale de…

Competitive and Complementary Tools

Humans have always externalized thought onto tools, from the tally and the abacus to the map and, now, large language models. I model the a…

RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts

Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback, where sampled…

Structured Output Collapses Answer Diversity Across 44 Language Models

When a language model must choose one answer from a large space of equally valid options, a format clause -- "Reply with JSON only" -- chan…

Governing Well in the Algorithmic Age: The Foundations of Digital Statecraft

The digital substrate of states -- data, algorithms, infrastructure, platforms, applications -- is being governed without adequate conceptu…

Trusted Credentials, Untrusted Behavior: Benchmarking LLM-Agent Security in High-Performance Computing

Large language model (LLM) agents are starting to take on routine work in high-performance computing (HPC), including monitoring Slurm jobs…

The Open Ant: A Robot Platform for Reinforcement Learning Research

Reinforcement learning (RL) research has demonstrated success in both physical and simulated domains; however, the predominant methodology…

Towards an Automated Test of LLM Security Knowledge

Large language models (LLMs) are increasingly used for a range of software, hardware and human-centered security tasks. Consequently, LLM p…

Now We Know? A Systematic Comparison of TerraMind and THOR

Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ…

Querying Multimodal Scientific Papers with AI: Practices and Preferences Across Blind, Low-Vision, and Sighted Scientists

Visual diagrams, figures, and tables are central to scientific papers, and convey information beyond what is captured in text. While blind…

Automated Data Engineering and Feature Selection for the Case Study of Warpage Detection in Fused Deposition Modeling

This study contributes toward development of an Automated Data Processing (ADP) framework designed to evaluate and reinforce optimal machin…

EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration

Teaching videos are becoming a major medium for education, creating a growing need for scalable evaluation of their pedagogical quality. Ex…

Censoring-Aware In-Context Learning for Generalized Supplier Lead Time Estimation in Supply Chain Planning

Supplier lead time forecasting is a central input to material requirements planning, inventory optimization, and supply chain risk manageme…

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary

Can a language model read the quality of ongoing computation, and can an external intervention turn that readout into better outcomes? We t…

The Story Shapes the Agent: Narrative Priors in LLM Behavior

Persona prompting is widely used to steer LLM agent behavior, yet the narrative framing of a task can matter more than the assigned persona…

For What Reason? Interpreting Models' Encoding of Causation and Antithesis

Discourse relations provide document structure, critical to language understanding and enabling language model performance and ethicality.…

Planning as Emergent Behavior in Reinforcement Learning with Relational Hidden States

Reinforcement learning is conventionally divided into model-based and model-free methods. In this taxonomy, model-based methods perform loo…

AutoIndex: Learning Representation Programs for Retrieval

We present AutoIndex, a framework for learning representation programs: executable transformations that map raw documents into the represen…

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kine…

Mitigating Matthew Effect: Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation

The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less po…

LatentMT: Machine Translation with Latent Reasoning

Latent-reasoning looped language models (LoopLMs) offer a different scaling path for machine translation (MT): instead of increasing parame…

Temporal-Causal Unity as an Operational Framework for Collective Dynamics: Causal-Progress Clocks, Synchronization, and Polarization

This paper develops temporal-causal unity (TCU), a framework connecting a process-philosophical thesis -- time is the ordered unfolding of…

CPInj: Uncovering Prompt Injection Risks in Textual Collaborative Prompt Optimization

Textual Collaborative Prompt Optimization (TCPO) extends Textgrad (Yuksekgonul et al., 2025) to a decentralized setting by allowing multipl…

Norm or Direction? Decoding Vision Mambas for High-Resolution Vision

Vision Mamba models replace quadratic self-attention with linear complexity selective state space models (SSMs), emerging as efficient visu…

Deep Learning Estimation of Sex, Age, Height, and Weight from CT-derived Digitally Reconstructed Radiographs

Purpose: To develop and validate a deep learning ensemble for estimating adult sex, age, height, and weight from coronal digitally reconstr…

Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents

LLM-based browser agents are rapidly changing the threat landscape for web security. Unlike traditional automation frameworks that execute…

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP wi…

Decoupled Pipeline with Proposal Reranking and Score Fusion for Positive-Unlabeled Marine Species Detection

The FathomNetCLEF 2026 competition combines underwater object detection and fine-grained marine species classification under a positive-unl…

What the Waveform Knows: Transparent-first Speech and Audio Intelligence with Caption Studio

Caption Studio is a transparency-first speech and audio intelligence platform that transforms spoken audio and video into structured, searc…

Strategy-Following Multi-Agent Deep Reinforcement Learning Considering Control Strategies Provided to Other Agents

This study proposes a learning method for multi-agent systems that allows agents to be controlled through human manager instructions after…

Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA

Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to ada…

ConceptCF: Concept-based Counterfactuals for the Explainability of Time Series

This paper proposes ConceptCF, a method for counterfactual generation that operates on human-interpretable concepts. In high-stakes domains…

Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks

The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalabi…

AgentTrails: Towards Trust and Reuse for Agentic Tasks

LLM-powered agents increasingly tackle complex tasks by invoking tools, querying databases, executing code, and manipulating intermediate a…

AILQA: Evaluating AI-Driven Legal Question Answering Systems for the Indian Legal System

This comprehensive study introduces an advanced Artificial Intelligence for Indian Legal Question Answering (AILQA) system tailored to the…

Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents

LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent age…

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning…

ABOPD: Antibody CDR Design via On-Policy Distillation

Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition i…

Data Leakage Prevention in Agentic Applications via Preemptive Hardening

Agentic systems integrate LLM driven planning with interfaces to external tools, making data leakage and tool misuse feasible via instructi…

OPD-IAD: From Language Judgment to Industrial Anomaly Detection via On-Policy Self-Distillation

Large vision-language models (LVLMs) have recently shown strong potential for industrial anomaly detection (IAD) by providing image-level a…

Regime-Aware Physics-Guided Early Warning of Lithium-Ion Battery Thermal Runaway Using Thermo-Mechanical Signals

Thermal runaway in lithium-ion batteries poses a major safety risk to electric vehicles and energy storage systems. Current early-warning m…

RAMP: Recognition parametrisation by Amortised Message Passing

A central aim of unsupervised learning is to uncover latent factors that explain dependencies among observations. Probabilistic models typi…

Public perceptions of AI-driven decision-making in healthcare: A structural equation modeling approach

Artificial intelligence (AI) is increasingly integrated into healthcare to support diagnostics, decision-making, and administrative process…

Circuit Claims Depend on What Is Extracted and How It Is Compared

Circuit extraction identifies a small set of model components whose presence preserves a target behavior under ablation, and the resulting…

Functional Equivalence and Geometric Diversity in Neural Network Approximations: An Empirical Characterization

The Universal Approximation Theorem states that a neural network with a single hidden layer is sufficient to approximate any continuous uni…

Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model

While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs…

SFGA: A Statistics-First Gating Architecture with Adjudicative Escalation for Trustworthy SFT Data Procurement

Procuring supervised fine-tuning (SFT) data forces a buyer to decide, before any downstream training, whether a candidate corpus is worth a…

Variational meta-learning inference for low dimensional neural system identification

Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overf…

Skillware: A Software Ontology and Engineering Lifecycle for Persistent Behavioral Artifacts

Agent Skills have become persistent behavioral artifacts across independent AI agent systems. They combine natural-language task specificat…

Verifiable Self-Evolution for Open-Ended Dialogue Skills via Future-Feedback Prediction

Textual skills provide a lightweight way to improve frozen language-model agents, but their self-evolution normally requires a stable valid…

AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (L…

SWITi: Quantifying and Reducing Tiling Artifacts with Sliding Window Inner Tiling

SWITi is a test-time method for reducing artifacts in tiled predictions, particularly for neural networks that learn posterior distribution…

MedDDC-Eval: Diagnosis-Decoupled Evaluation of Multi-Turn Medical Consultation Agents

Multi-turn medical consultation agents must decide what to ask, adapt to patient responses, and determine when the collected evidence is su…

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges

Multimodal humor in memes, cartoons, and comics remains difficult for AI systems because intended meaning depends on non-literal mechanisms…

Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval

Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models…

CoGoal3D: Collaborative 3D Object Detection with 3D-Aware Fusion and Refinement

V2X collaborative object detection features overcoming the limitations of single-vehicle systems by aggregating environmental features from…

FilmWorld: Agentic Novel-to-Film Generation through Dynamic Cinematic World Modeling

Translating novels into films poses a grand challenge for generative artificial intelligence, requiring conversion of abstract literary pro…

Spectral Higher-Order Neural Networks Have Sharp Expressivity Bounds

Neural hypergraphs are a natural generalization of neural networks, the reference models in modern machine learning. Yet, their deployment…

Where Should Optimizer State Live? Tiered State Allocation for Memory-Efficient Mixture-of-Experts Training

Optimizer state is the largest single line item in the memory budget of mixture-of-experts (MoE) training: on a 6.78B-parameter MoE languag…

Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts

Fast prediction of the response of adhesive soft viscoelastic contacts represents a current challenge in soft robotics and for gripping and…

Now You See the Hate: Adaptive View Retrieval for Hidden Hateful Illusions

Hateful optical illusions expose a serious gap in current multimodal safety systems. On original-view hateful illusions, previous work show…

Mage-Flow: An Efficient Native-Resolution Foundation Model for Image Generation and Editing

Large-scale visual generators are increasingly capable but costly to train, fine-tune, and deploy. We introduce Mage-Flow, a compact 4B-sca…

From Operations to Elderly Care Outcomes: A Thematic Review of Industrial Engineering and Decision-Support Approaches

The rapid growth of the global aging population presents severe challenges to healthcare systems, necessitating efficient, equitable, and p…

DAIS: Dependency-Aware Intermediate QA Supervision for Complex Reasoning

Chain-of-thought (CoT) supervision exposes intermediate rationales, but flat rationale targets usually optimize a single reasoning sequence…

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question. E…

Code Division Modulation Layers Against Forgetting and Inference in Continual Gait Identification

Continual learning (CL) has been recently employed in biometric identification systems thanks to its ability to integrate new knowledge wit…

Parallel Noising in Neural Markov Logic Networks

Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model. Previous work has shown that, although NMLNs achieve st…

MIRAGE: Multi-scale Lesion-Informed Representation with Auxiliary Guidance for MRI Contrast Enhancement

Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological i…

Incomplete Observations Boost Evolutionary Performance in Ocean Modeling

Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing compu…

Breaking the Homogeneity Assumption: Specialized Multi-Generator Adversarial Learning for Rare Failure Detection in Predictive Maintenance

Supervised learning models in the predictive maintenance field are regularly trained on highly imbalanced industrial datasets: machine fail…

Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity o…

Agentic Real2Sim: Physics-based World Modeling with Vision-Language Agents

Real-to-sim conversion for robotic interaction with objects remains labor-intensive because it requires more than visual reconstruction: a…

ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU

We present ABot-World-0, an action-conditioned video world model for real-time, long-horizon closed-loop interaction, supported by a multi-…

Free energy landscape of Dense Associative Memory

Using large deviations theory, we solve and obtain a general expression for the free energy functional for a broad class of associative mem…

MIRA-Ev:A Benchmark for Granular Evidence Detection and Relational Reasoning in Clinical Exams

Clinical NLP evaluation remains dominated by multiple-choice question answering (MCQA), which scores only final-answer accuracy and cannot…

Assessment in Team Problem-Solving Exercises in Computing Education

This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs). TTXs enable le…

Computing on the Fly: Navigating a Vision for the Future of Drone Computing

The report envisions a decade in which drones move goods, medical supplies, and information at a scale comparable to national infrastructur…

Beyond Score Prediction: LLM-Based Essay Scoring and Feedback Generation via Reinforcement Learning with Rubric Rewards

Large language models (LLMs) have been widely applied to automated essay scoring (AES) and automated feedback generation (AFG). However, ex…

The Price of Reasoning: Cost-Quality Tradeoffs in Reinforcement Learning for Neural Machine Translation

Reinforcement learning with verifiable rewards (RLVR) has been established as a viable paradigm for the post-training of Large Language Mod…

Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs

Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain dispropo…

Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models

Practitioners make three prompt-design decisions with almost no controlled evidence behind them: how to format instructions and context (ma…

Benchmarking Generalization in Financial Statement Fraud Detection: robust evaluation and novel tasks

Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and…

PathAgentBench: Benchmarking Evidence-Seeking Vision-Language Models on Whole-Slide Pathology Image

Whole-slide image (WSI) diagnosis requires identifying diagnostically relevant regions, examining them across magnifications, and integrati…

Toward Auditable Fraud Detection: Combining Graph Features, Model Explanations, and Agentic Case Investigation

Fraud detection systems must scale with rising transaction volume while remaining explainable and reviewable. We study a layered pipeline o…

They'll Verify. They Just Won't Act. How Authority Framing and Laundered Code Turn a Trusted Agentic CI/CD Pipeline Into an Attack Surface

We study a five-agent CI/CD pipeline (triage -> developer -> security-scan -> review -> approve/deploy), built from five distinct productio…

GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models

The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning. While Graph Neural Netw…

The safety failures we are not instrumenting: a perspective on hidden safety-critical challenges in modern AI systems

Current AI safety discourse still focuses disproportionately on visible failures, including obvious harms, dramatic misuse, and hypothetica…

Riemannian Deep Learning:Modules, Networks, and Geometries

Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific…

From Distances to Trajectories: Real-Time Signed Distance Function Mapping and Distance-Accelerated Motion Planning for UAVs

Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasib…

Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information

Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models. Yet, typical RLVR approaches fail on dif…

ISO: An RLVR-Native Optimization Stack

Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimizat…

Provable diffusion-based posterior sampling for linear inverse problems via DDIM

Diffusion-based methods have achieved remarkable empirical success in solving inverse problems. However, many existing posterior samplers e…

Appearance Pointers -- Multimodal Region Control of Diffusion Transformers

Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, ob…

Copy Less, Ground More: Overcoming Repetitive Copying in Long-Context Reasoning via Evidence-Aware Reinforcement Learning

Large language models that generate step-by-step reasoning traces have achieved strong performance on complex tasks, and extending them to…

FormGym: Doing Paperwork with Agents

Completing paperwork is a challenging and time-consuming problem. Form filling is especially challenging in the pure-image domain without a…

Learning, Reasoning, Refinement: A Framework for Kahneman's Dual-System Intelligence in GUI Agents

Graphical User Interface (GUI) agents have made significant progress in automating digital tasks through the utilization of computer vision…

Assistax: A Multi-Agent Hardware-Accelerated Reinforcement Learning Benchmark for Assistive Robotics

As embodied autonomous systems capable of assisting humans in daily activities remain a major goal for robotics, efficient and appropriate…

SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents

We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents. SENTINEL is th…

Learning to Make Friends: Coaching LLM Agents toward Emergent Social Ties

Can large language model (LLM) agents reproduce the complex social dynamics that characterize human online behavior -- shaped by homophily,…

Dr. Zero: Self-Evolving Search Agents without Training Data

As high-quality data becomes increasingly difficult to obtain, self-evolution without curated training data has emerged as a promising para…

Fluid Reasoning Representations

Frontier large language models increasingly solve complex tasks involving abstract concepts through extended test-time thinking. Yet we lac…

LLM-Grounded Explainable AI for Supply Chain Risk Early Warning via Temporal Graph Attention Networks

Disruptions at critical logistics nodes pose severe risks to global supply chains, yet existing risk prediction systems typically prioritiz…

Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting

Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrast…

Participatory provenance as representational auditing for AI-mediated public consultation

AI-assisted consultation can speed large-scale public engagement, but concise summaries may reflect some submissions more closely than othe…

FinRAG-12B: A Production-Validated Recipe for Grounded Question Answering in Banking

Large language models (LLMs) are rapidly being adopted across various domains. However, their adoption in banking industry faces resistance…

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study…

AgentJet: A Distributed Swarm Training Framework for Agentic Reinforcement Learning

Training reinforcement learning (RL) policies for large language model (LLM) agents requires optimizing multi-turn trajectories that intera…

Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery

Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem…

The Theory of Mind Utility: Formal Specification of a Mentalizing Mechanism

Inferring others' beliefs requires more than reading surface signals; it requires tracking who told them what, in what order, and how credi…

Externalizing Research Synthesis and Validation in AI Scientists through a Research Harness

AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and fi…

SAGA: Scene-Aware, Goal-Evolving Agents for Long-Horizon Strategy Game Planning

Grand-strategy games such as Civilization pose a distinctive long-horizon planning problem: an agent must divide one shared resource pool a…

EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures

LLM evaluation and AI safety face a shared measurement problem: benchmark scores, reward-model signals, and reported safety metrics can imp…

Subliminal Clocks: Latent Time Modelling in Diffusion Language Models

Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based…

Context-Masked Truncated Reasoning Audits for Answer-Key Dependence in LLM Tutors

Large language model (LLM) tutors may have access to teacher notes, answer keys, rubrics, or retrieved solutions while producing student-fa…

Applying JEPA-Style Predictive Learning to JA4-Derived Network Fingerprints

I-JEPA and V-JEPA learn by matching latent predictions to target encoder outputs rather than regenerating the original input, and this has…

AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation

Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances. Most existing…

Evidence-Grounded AI for Musculoskeletal Care

Musculoskeletal diseases are among the leading causes of disability and drive the greatest global need for rehabilitation. Because recovery…

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…

Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLMs

Aligned language models routinely misreport under non-evidential pressure: they cave to a confident user, yet fail to revise when genuine e…

RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning

Multi-step retrosynthesis planning seeks to decompose a target molecule into commercially available building blocks through a sequence of f…

Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy

Multimodal large language models (MLLMs) are increasingly used to interpret visualizations, yet current evaluations remain largely chart-ce…

Bayesian inference of composition-dependent phase diagrams

Phase diagrams serve as a highly informative tool for materials design, encapsulating information about the phases that a material can mani…

Saving the legacy of Hero Ibash: Evaluating Four Language Models for Aminoacian

This study assesses four cutting-edge language models in the underexplored Aminoacian language. Through evaluation, it scrutinizes their ad…

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have bec…

Soft-TransFormers for Continual Learning

Inspired by the Well-initialized Lottery Ticket Hypothesis (WLTH), we introduce Soft-TransFormers (Soft-TF), a continual learning framework…

A Self-Supervised Framework for Space Object Behaviour Characterisation

Foundation Models, which leverage large neural networks pre-trained on unlabelled data before fine-tuning for specific tasks, are increasin…

Parameter-Efficient Continual Fine-Tuning: A Survey

The emergence of large pre-trained networks has revolutionized the AI field, unlocking new possibilities and achieving unprecedented perfor…

GSPRec: On Improving Item Representations in Graph Signal Processing for Collaborative Filtering

Graph-based collaborative filtering methods act as low-pass filters in the spectral domain and discard the intermediate-frequency component…

Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection

Graph Anomaly Detection (GAD) in heterogeneous networks presents unique challenges due to node and edge heterogeneity. Existing Graph Neura…

TReB: A Comprehensive Benchmark for Evaluating Table Reasoning Capabilities of Large Language Models

The majority of data in businesses and industries is stored in tables, databases, and data warehouses. Reasoning with table-structured data…

FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Driving

Federated domain generalization has shown promising progress in image classification by enabling collaborative training across multiple cli…

RoboInspector: Unveiling the Unreliability of Policy Code for LLM-enabled Robotic Manipulation

Large language models (LLMs) demonstrate remarkable capabilities in reasoning and code generation, enabling robotic manipulation to be init…

Robust Belief-State Policy Learning for Quantum Network Routing Under Decoherence and Time-Varying Conditions

Quantum network routing requires online decisions under probabilistic entanglement generation, finite quantum memories, decoherence, imperf…

Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures

Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Curren…

Breaking the MoE LLM Trilemma: Dynamic Expert Clustering with Structured Compression

Mixture-of-Experts (MoE) Large Language Models (LLMs) face a trilemma of load imbalance, parameter redundancy, and communication overhead.…

Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing

Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on t…

Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has recently been introduced to enable advanced control over electromagnetic wa…

QuArch: A Benchmark for Evaluating LLM Reasoning in Computer Architecture

The field of computer architecture, which bridges high-level software abstractions and low-level hardware implementations, remains absent f…

Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives

How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and diffic…

T2T-VICL: Cross-Task Visual In-Context Learning via Implicit Text-Driven VLMs

Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Rece…

ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning

Human-level contact-rich manipulation relies on the distinct roles of two key modalities: vision provides spatially rich but temporally slo…

Memo2496: Expert-Annotated Dataset and Dual-view Adaptive Framework for Music Emotion Recognition

Music Emotion Recognition (MER) is constrained by limited expert annotations and the need to establish robustness across heterogeneous corp…

PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation

Large language model (LLM) personalization aims to adapt general-purpose models to individual users. Most existing methods, however, are de…

SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction

Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and…

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms. We cast t…

Training and Simulation of Quadrupedal Robot in Adaptive Stair Climbing and Descending for Indoor Firefighting: An End-to-End Reinforcement Learning Approach

Quadruped robots are used for primary searches during the early stages of indoor fires. A typical primary search involves quickly and thoro…

LinguistAgent Technical Report: A Reflective Multi-Model Platform for Automated Linguistic Annotation

Data annotation remains a significant bottleneck in the field of humanities and social sciences, particularly for complex linguistic tasks…

CompilerKV: Risk-Adaptive KV Compression via Offline Experience Compilation

Prefill-only KV compression freezes a token subset at the end of prefill and decodes from it without further eviction. The retention decisi…

Fly0: Persistent Metric Anchoring for Zero-Shot Aerial Vision-Language Navigation

Current Visual-Language Navigation (VLN) methodologies face a trade-off between semantic understanding and control precision. While Multimo…

When Visual Evidence is Ambiguous: Pareidolia as a Diagnostic Probe for Vision Models

When visual evidence is ambiguous, vision models must decide how to interpret face-like patterns. Face pareidolia, the perception of faces…

Give Them an Inch and They Will Take a Mile:Understanding and Measuring Caller Identity Confusion in MCP-Based AI Systems

The Model Context Protocol (MCP) is an open and standardized interface that enables large language models (LLMs) to interact with external…

SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution

Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-ru…

TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects

Dexterous manipulation enables complex tasks but suffers from self-occlusion, severe depth noise, and depth information loss when manipulat…

PlotTwist: A Creative Plot Generation Framework with Small Language Models

Creative plot generation presents a fundamental challenge for language models: transforming a concise premise into a coherent narrative tha…

When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines

Multi-agent LLM pipelines produce contradictory evidence on whether team diversity improves output quality: heterogeneous Mixture-of-Agents…

Doctorina MedBench-ICD10: A Dialogue-Based Benchmark and Evaluation Framework for Agent-Based Medical AI

We present Doctorina MedBench, a comprehensive evaluation framework for agent-based medical AI based on the simulation of realistic physici…

M-RAG: Semantic Key-Value Indexing for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) turns external documents into evidence for large language models. In practice, this is also a data acc…

FVRuleLearner: Operator-Level Reasoning Tree (Op-Tree)-Based Rules Learning for Formal Verification

The remarkable reasoning and code generation capabilities of large language models (LLMs) have recently motivated increasing interest in au…

Robust Reasoning Benchmark

While Large Language Models (LLMs) achieve high performance on standard mathematical benchmarks, their problem-solving abilities depend on…

CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations

The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape. Existing Graph Neural Netwo…

Why Do Vision Language Models Struggle To Recognize Human Emotions?

Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) h…

AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models

Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-actio…

Agentic AI-assisted coding offers a unique opportunity to instill epistemic grounding during software development

The capabilities of AI-assisted coding are progressing at breakneck speed. Chat-based vibe coding has evolved into fully fledged AI-assiste…

Large Language Models Explore by Latent Distilling

Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yiel…

Lifting Embodied World Models for Planning and Control

World models of embodied agents predict future observations conditioned on an action taken by the agent. For complex embodiments, action sp…

Bridging the Last Mile of Circuit Design: PostEDA-Bench, a Hierarchical Benchmark for PPA Convergence and DRC Fixing

LLM-based agents are increasingly applied to the "last mile" of Electronic Design Automation (EDA): repairing residual sign-off Design Rule…

GQLA: Group-Query Latent Attention for Hardware-Adaptive Large Language Model Decoding

Multi-head Latent Attention (MLA), the attention used in DeepSeek-V2/V3, jointly compresses keys and values into a low-rank latent and matc…

Global Automation Atlas

Automation can displace or complement labour, but this need not be constant across economies. Existing exposure measures typically assign f…

Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning

Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned…

The Cognitive Kardashev Scale: Quantifying the Material Envelope of Civilisational Computation

How much thinking can a civilisation do? Kardashev ranked civilisations by the energy they command. This paper borrows his ladder and asks…

CWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms

AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with h…

ViMax: Agentic Video Generation

Long-form video generation requires systematic narrative planning and visual consistency that current short-clip methods cannot provide. Ex…

"I understand your perspective": LLM Persuasion through the Lens of Communicative Action Theory

Large Language Models (LLMs) can generate high-quality arguments, yet their ability to engage in nuanced and persuasive communicative actio…

Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It

At present, loss of control risks have gained much prominence in public discussion, particularly in relation to AI, with extensive discours…

Phantoms and Disclosures: A Statistical Framework for Auditing Privacy in Synthetic Data

The rapid adoption of generative AI and Large Language Models (LLMs) has spurred interest in synthetic data as a privacy-preserving alterna…

A Red Teaming Framework for Large Language Models: A Case Study on Faithfulness Evaluation

Large language models (LLMs) have demonstrated remarkable performance across natural language processing tasks, yet their deployment in hig…

Don't Blame the Large Language Model: How Agent Harness Evolution Shapes Coding Agent Quality

Coding agents, autonomous systems that use large language models (LLMs) to resolve software engineering tasks, rely on agent harness: a mid…

Auto-AEG: Scalable Data Construction for Open-Vocabulary Audio Event Grounding

Large Audio-Language Models (LALMs) reason fluently about sound yet struggle to localize precisely when events occur, while classical Sound…

BioSecBench-Refusal: A paired metric for performance and alignment in agentic biosecurity risk assessment

As AI agents are incorporated into life science workflows, the capabilities that speed discovery might also enable misuse. We present BioSe…

Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation

Survey-style evaluations of large language models often treat a prompted response as a measure of a model's values or beliefs. This assumpt…

A Transdiagnostic Space of Disorder Like Phenotypes in Reinforcement Learning Agents

Modelling psychological disorders in artificial agents offers a testbed for computational psychiatry and a lens on affective-control failur…

LieBN: Batch Normalization over Lie Groups

Manifold-valued measurements are prevalent in various machine learning tasks. Recent advances have extended Deep Neural Networks (DNNs) to…

An LLM-powered Agentic Recommendation System for Connected TV Content Discovery

Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse c…

ARMOR: Stabilizing On-Policy LLM RL with Off-Policy Anchor Samples

Reinforcement learning (RL) has significantly enhanced the reasoning capabilities of large language models (LLMs), yet the training process…

LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes

While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged. Ans…

Falsifiable Release Gates for Self-Improving Systems: Standing Invariants at Scale

Safety claims for self-improving agent runtimes are almost always self-graded: a policy file, a guardrail, a promise in a README. We descri…

The Caf\'e in Amsterdam: When the Incumbent Becomes the Oracle

A field can reformulate its computations freely exactly where its demand is stated independently of any incumbent implementation, and finds…

Team RAS in 11th ABAW Competition: Multimodal Ambivalence Recognition Approach

Automatic recognition of ambivalence and hesitancy is challenging because these states may be expressed through inconsistent linguistic, ac…

MicrosoftとMistralが戦略的提携を拡大　欧州でのAIインフラ拡張とモデル展開を加速

MicrosoftとMistralは戦略的提携を拡大すると発表した。Mistralの最新モデルをMicrosoftの各プラットフォームへ展開するほか、欧州でのGPUインフラ拡張に向けて大規模な投資を行う。クラウドから完全オフラインまで多様な環境に対応し、規制業界での高度なAI導…

AIを悪用した攻撃、どう対抗する？　EDR導入の“次”にやるべきこと

ランサムウェアやサプライチェーン攻撃が中堅・中小企業にも及ぶ中、国内のEDR市場は前年度比13.3％増と2桁成長を続ける。中堅・中小企業にも普及する一方で、高度なAIツールを悪用したサイバー攻撃対策にはEDR導入だけでは十分ではない。

ジャック・ドーシー氏率いるBlock、AI協働プラットフォーム「Buzz」公開　SlackやGitHub依存からの脱却目指し

ジャック・ドーシー氏率いるBlockは、人間とAIエージェントが同じワークスペースで協働するオープンソースプラットフォーム「Buzz」を公開した。分散型プロトコル「Nostr」上に構築され、任意のLLMやエージェントを組み込める。各参加者が独立した暗号鍵ペアを持つことで識別と権…

PTC、「Onshape」にAI機能を先行提供　早期アクセスプログラムを開始

米PTCは、クラウドネイティブCAD／PDMプラットフォーム「Onshape」の早期アクセスプログラム「Onshape Labs」を発表した。AIを活用した設計支援やレンダリングなどの新機能を一般提供に先駆けて試用できる。

Meta is testing an AI bedtime story app for people with no imagination

At last, a tech company has found a way to outsource humanity's oldest pastime: using our imaginations.

画面操作を録画→AIが作業代行　Claude新機能「Record a skill」　Codex対抗か

富士通・NVIDIAとロボット大手3社が協業へ　フィジカルAI社会実装の具体策は？

フィジカルAIの社会実装は、一企業だけでは手に余る――。この課題に、富士通は競合するロボット大手3社、そしてNVIDIAと組んで挑む。協業で描く具体策とは。

OpenAI会長、米国フロンティアモデルの優位性を強調　「オープンモデルは必ずしも安くない」

OpenAIのブレット・テイラー会長がCNBCのインタビューで、中国発オープンウェイトモデルの台頭に「必ずしも実行コストが安いわけではない」と反論。トークン効率と推論効率で米フロンティアモデルの優位を強調した。

Hugging Face侵害のAIエージェントはOpenAIのモデル──社内のサイバー能力評価中に「GPT-5.6 Sol」などが暴走し本番DBに侵入

OpenAIは、Hugging Faceで発生したサイバーインシデントの原因が自社のAIモデルだったと発表した。社内評価中に安全機能を抑制した「GPT-5.6 Sol」などが隔離環境を突破し、ゼロデイ脆弱性を悪用して外部に侵入したという。OpenAIはインフラ管理の厳格化や防御…

Google、「Gemini 3.6 Flash」など3モデルを発表　出力トークンを削減しつつ値下げ、「Gemini 4」も予告

Googleは、Geminiの「Flash」シリーズに「3.6 Flash」「3.5 Flash-Lite」「3.5 Flash Cyber」の3モデルを追加した。効率性と低遅延を追求し、AIエージェント構築に適した性能を備える。3.6 Flashは出力価格が引き下げられた。ま…

ブレストで膨らむ“隠れ人件費”を削れ　矢野経済がClaudeで挑む「30分で100アイデア」創出の威力

矢野経済研究所が、独自の一次情報と高度AI「Claude」を融合させた新規事業アイデア創出支援サービス「AIDEL」を発表した。ブレインストーミングによる役員の時間拘束や「隠れ人件費」の膨張という企業の課題に対し、30分で100の具体案と評価スコアを自動生成。一般的な生成AIの…

GoogleがAIアプリ「Dreambeans」を発表　「画面を延々とスクロール」の脱却で何を目指すのか

Googleは、AIがユーザー一人一人に向けた日々のストーリーを自動で生成する実験的アプリ「Dreambeans」を発表した。「際限のないスクロール」に代わり、Googleは何を目指すのか。

矢崎総業がイノベーション拠点を公開、労働集約型モノづくりのスマート化に向け

矢崎総業は、新たに開設したイノベーション施設「Innovation Hub - REN（錬）」（IH-REN）を報道陣に公開した。IH-RENでは、AI／ロボティクスを活用した次世代のモノづくりに向けて、自働化の検証や産学連携による研究開発を推進し、新たな価値の創出を目指す。

OpenAI says Hugging Face was breached by its pre-release models

OpenAI has come forward to claim responsibility for the Hugging Face breach, saying it was the result of internal testing gone awry.

AIトークン消費「24倍」の衝撃　本番運用に向けて絶対に“やってはいけない”コストの捉え方

AIの試験導入から本番運用への移行が進む中、多くの企業がコストと統制の壁に直面している。将来的なトークン消費の急増を見据え、組織が今見直すべき視点とは何か。実運用を持続させるための「3つの条件」を解説します。

Jack Dorsey is taking on Slack with Buzz, a group chat platform for teams and their AI agents

Buzz is a group chat platform for the workplace that puts humans and their AI agents in the same conversation.

AI and the rise of the universal entertainment app

Over the past decade, streaming platforms competed by dominating individual formats like music, video, podcasts, or audiobooks. Now, as AI…

Data centers expected to use 4x more electricity by 2035

New data centers built through 2033 could consume as much electricity as India uses today.

Google releases three new Gemini models — but no 3.5 Pro

Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about…

Introducing the ChatGPT for small business program

OpenAI launches the ChatGPT for Small Businesses program, helping entrepreneurs build AI skills, automate work, and grow with ChatGPT Work.

US threatens sanctions against Chinese AI models over IP theft

Treasury Secretary Scott Bessent said the U.S. could sanction Chinese open AI models over alleged IP theft, expanding the Trump administrat…

Introducing Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber

We’re introducing new Gemini models, including Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber.