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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 193
Athanor: An Agent-Native System for Autonomous Post-Training Research
Recursive self-improvement, in which AI systems build stronger AI systems, is one promising route to superintelligence.Bellman-Consistent Trajectory RL for LLM Arithmetic Reasoning
To address sparse terminal rewards in LLM arithmetic reasoning, we propose a Bellman-consistent trajectory RL method.Approve What, Exactly? Effect Binding and Trusted Rendering for Agent Step-Up Authorization
AI agents now pause for approval before they send money, delete files or email outsiders, and that approval is usually treated as a second factor.OmicsBench: a long-horizon multi-turn benchmark of AI agents on research-study-scale bioinformatics
Computational biologists increasingly delegate substantial analyses to AI agents in long-horizon, multi-turn sessions, yet most benchmarks of agents' bioinformatics capabilities pose a single short…Sci$Π$vot: Benchmarking Principal-Investigator Judgment at Pivotal Moments of Scientific AutoResearch Agents
Large language model (LLM) agents can keep scientific experiments running for months with little human supervision, but reliable autonomous research also requires scientific judgment.A Jury, Not an Electorate: Label-Free Reliability Estimation for Ensembles of Language Models
An ensemble of language models is a jury, not an electorate.SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning
We identify a theoretical incompatibility between 3D point cloud masked autoencoding and SE(3)-equivariant networks.Probe-and-Resume: When and How to Evaluate Agent Execution
Test-time search can improve agent performance by exploring alternative actions and using a critic to select promising branches.MemComposer: Learning to Assemble Memories for LLM Agents via Reinforcement Learning
Agent memory enables large language model (LLM) agents to reuse past experiences without updating their parameters.ToolBottleneckBench: Measuring Tool-Agent Performance under Controlled Pressures and Fixed Harness Budgets
The success score of a tool agent reflects both its model and the budgets of the harness that runs it.Planning Commitment of LLM under Partial Observability
LLM-based planning agents commit to plans of different lengths before interacting with a partially observable environment.Kolab: Benchmarking Human-Agent Collaboration in Long-Horizon Workflows
When humans collaborate with AI agents on long-horizon tasks, what happens to the agents' contributions?SSR: Structural Signal Retriever for Scalable Agent Discovery in LLM-Orchestrated Multi-Agent Systems
Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LLM) orchestrator.DST-Net: Decomposed Spatio-Temporal Network for Time Series Forecasting
Series decomposition has emerged as a widely acknowledged paradigm in deep time series forecasting models.When Judges Agree and Are Wrong: Consistency-Accuracy Dissociation as a Blind Spot for Label-Free Evaluation
Label-free evaluation methods, such as self-consistency filtering, majority-vote aggregation, and agreement-based reliability scoring, rely on an implicit assumption that consistency can serve as a…Stronger Judges Do Not See More: Deployment-Integrity Blindness in LLM Evaluation
LLM-agent evaluation reports behavioral scores but leaves the identity of the deployed runtime stack unreported.OnBodyAI - Why the Next AI Needs a Body
The next AI will not win by knowing more about the world.CleanRM: Reward Modeling from Noisy Feedback with a Few Clean Feedback for RLHF
Feedback noise is pervasive in preference data, arising from subjective human judgments, annotation mistakes, and imperfect LLM judges.When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values.CoSTA: Compressed Speech Token Adaptation for Efficient Spoken Dialogue Models
Spoken Dialogue Models (SDMs) typically share a speech-token space for generation and synthesis, leading to long autoregressive sequences and costly inference.