AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
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 102
KAPPA: Checking the Proofs in Statistics Papers with Lean 4 and Language Model Agents
Papers in statistical theory rest on long proofs that few readers check line by line.Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations
Knowledge graphs (KGs) provide reliable external knowledge for multi-hop factual reasoning with large language models (LLMs), yet most KGs in real-world domains such as finance and industry lack…ArchiveLens: A Generative Spatial Knowledge Framework for Semantic Reconstruction of Historical Maps
Historical maps contain dense morphological and semantic information about past urban environments, but many digitization workflows focus primarily on extracting geometry, and do not capture…VaLoP: Value Gradient Lookahead and Pullback for Reward-Guided Diffusion
Reward guidance steers a pretrained diffusion or flow model toward images or actions that a reward model prefers, at sampling time and without retraining the model for each reward.Making Reasoning Visible: Configuration-Aware Diagnostic Modeling for Adaptive Scaffolding in Structured Reasoning Tasks
Modeling the latent, multi-step reasoning processes of learners is a core challenge for adaptive tutoring systems, which often rely on simple right/wrong answers rather than understanding how…From Language to Drives: A Multimodal Framework for Aligning Autonomous Robots with Human Purposes
This work explores a multimodal approach to the alignment of the behavior of open-ended learning autonomous robots with human purposes through the use of large language models as cultural enablers.Adapt for Planning, Not just Prediction: Optimistic Test-Time World Model Adaptation
Latent world models are typically pre-trained offline and kept frozen at test time, so planning with them can degrade under distribution shift.Provably Efficient Q-learning for Robust Reinforcement Learning
Standard reinforcement learning (RL) algorithms based on Markov Decision Processes (MDPs) are vulnerable to sim-to-real gaps.CounterFlow: Target-Conditioned Flow Matching for Visual Counterfactuals
In eXplainable AI, counterfactual explanations have gained significant traction by offering users direct, intuitive, and actionable insights into model decisions.OmniShowcase: Omnidirectional Subject-Consistent Video Generation from Multi-View Images
Generating videos of a specific subject from a few reference images remains challenging when the camera reveals surfaces that are not visible in the input.Adapting LLM-SC to Living Sacred Heritage: Domain-Specific Extraction, Semantic Networks and Human Evaluation on Pashupatinath Temple Reviews
Large language model (LLM) pipelines can turn visitor reviews into structured data, but generic schemes may miss rituals, sacred spaces, cultural restrictions and emotionally mixed experiences at…TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains
Vision Language Models (VLMs) have shown impressive capabilities in high-level reasoning and task planning for robots.EvoRouter: Cost-Efficient Multi-Agent Routing via Insight-Guided On-Policy Distillation
Serving web queries at scale with multi-agent systems requires jointly configuring large language models, collaboration modes, agent counts, interaction rounds, and skills.Towards a Unified Framework of Optimal Input-Side Dimensional Configuration for Deep CTR Prediction
Deep click-through rate (CTR) prediction has achieved substantial success through increasingly expressive feature interaction architectures.MiRL: Error-Targeted Reinforcement Learning for Correcting Terminal Agent Misbehaviors
Error-targeted learning gives a legible way to improve a model: identify specific weaknesses of the model, train the model to fix these weaknesses, and check whether the weaknesses are fixed.Explainable Multimodal AI Approach to Reduce Unnecessary Breast Biopsies
Recently, there has been rise in multimodal vision-language models for computer-aided diagnosis (CAD), yet their application in breast diagnosis, particularly for predicting biopsy outcomes remains…Quantitative Generalization Bounds for Multi-Layer Transformers: A Few Long Strings can Beat Many Short Ones
Transformer-based language models exhibit an astonishing capacity to generalize to longer and more difficult problem instances than those seen in training.Audit Where Hacks Pay: Budgeted Strong-Verifier Audits Against Reward Hacking in GRPO
Reinforcement learning with verifiable rewards trains on cheap checks, such as unit tests, that a policy can learn to game, while a stronger check is too expensive to run on every rollout.Can JEV Support Clinical Trial Criteria Assessment?
Clinical trial prescreening requires distinguishing unmet conditions from insufficient evidence in patient records.The Watchtower Imperative: Lifespan-Calibrated Evaluation for Frontier AI
The gap between AI capability deployment and safety assurance is structural and cannot be closed through internal evaluations alone.