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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 118
Not All Information Is Worth Asking For: Decision-Informed Information Seeking with LLMs
Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the final answer.Uni-Stream: A Unified Streaming Speech Language Model for Speech Synthesis and Recognition
Unified streaming speech language models aim to support speech recognition and synthesis within a single model, enabling real-time human-computer interaction.Evidence-Grounded SLM Behavioral Prediction Architecture for Future Assistance Use
Users often exhibit changes in interaction behavior before explicitly seeking assistance, enabling systems to anticipate future assistance use.Dual-Adaptive Z-Score PPO: Dynamic Clipping Using Action Confidence and Normalized Advantage Magnitude
Proximal Policy Optimization (PPO) remains a popular on-policy reinforcement learning algorithm owing to its stability and ease of implementation.Principia: Test-Time Hypotheses to Principle Construction for Scientific Discovery
Scientific discovery requires identifying the scientific principles underlying observed phenomena to guide further investigation.SWE-Shift: Evaluating Coding Agents in Evolving Workspaces
Repository-level coding benchmarks largely assume that tasks and environments remain unchanged throughout execution.Watermarking Diffusion Language Models with Learned Foresight and Balanced Bands
Green-red watermarks favor tokens from a green list that a secret key derives from the preceding tokens.Do VLAs Know They Will Fail? Interpretable Success and Failure Patterns in Physical AI Systems
Vision-Language-Action (VLA) models have emerged as powerful general-purpose robot policies, but their successes and failures remain difficult to diagnose and understand.SOPO: Socratic Guided Policy Optimization for Span-Level Hallucination Detection
Large Language Models (LLMs) have gained widespread adoption in various natural language processing tasks, but suffer from hallucination issues where they generate unfaithful or inconsistent content.Head Dimension Matters in Language Models for Long-Context Retrieval
Multi-head attention retrieves and combines context representations through query--key interactions and value aggregation, with head dimension constraining the per-head representation space.PEAR: Adaptive Advantage Reshaping for Perception and Exploration in Large Vision-Language Models
Effective multimodal reasoning requires a model to reliably utilize visual information while sufficiently exploring diverse reasoning paths.RxnExBench: Benchmarking Vision-Language Models for Structured Chemical Reaction Extraction from Scientific Literature
Screen Tables and Substrate Scopes are information-dense figures widely used in synthetic chemistry literature, yet automated extraction of structured reaction data from them remains challenging.Correct Answers, Closed Gates: SolveBind for GUI Agents at the Verification Barrier
Websites and applications deploy CAPTCHAs and anti-automation mechanisms that use behavioral signals such as interaction trajectories and timing, not outcomes alone, to distinguish humans from…Training an MoE LLM in MXFP4: How We Closed the Gap to BF16
Training large language models (LLMs) in sub-8-bit arithmetic promises substantial gains in memory bandwidth and compute throughput, yet FP4 formats introduce quantization error severe enough to…Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading
In modern financial markets, more than 3/4th of the large volume trade is executed by algorithmic trading based on advanced trading strategies to attain higher precision and speed than traditional…Spatial Heuristic Strategy based Diffusion Reinforcement Learning for Online 3D Bin Packing
Traditional heuristic methods have accumulated mature experience in core objectives, such as space utilization and layout compactness of 3D bin packing, relying on artificially designed rule-based…Minimum Description Length Unsupervisedly Separates Law-Grade from Statistical-Grade Knowledge in a Self-Improving Science Agent - and Reveals 86% of Its Own Discoveries Are Statistical
AI systems that discover scientific relations now generate candidate "laws" at scale, but the bottleneck has shifted from generation to vetting: which candidates deserve belief?Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction
Single-image 3D scene construction is a fundamental problem in 3D vision.RSIHub: A Composable Framework for Agent-Evolution Methods
Agent-evolution methods use execution feedback to improve prompts, skills, tools, and orchestration, but their implementations often rebuild the same experimental machinery.Anti-coherence Regularized Absolute Scaling for Out-of-Distribution Detection
Out-of-distribution (OOD) detection is recognized as a critical problem in AI safety.