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Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 216
Mitigating Demographic Disparities in Explanation-Human Alignment via Rationale Regularization
We study explanation fairness in hate speech detection.Can LLMs predict the outcomes of machine learning experiments?
Recent advances in AI make Recursive Self-Improvement (RSI) increasingly plausible.At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier
Recent breakthroughs in AI for mathematics---ranging from Olympiad medal performances to resolving long-standing open conjectures---have rapidly expanded what automated systems can achieve.Three LiDARs, Twenty-Five Datasets: Cross-Source Generalization in Tree-Species Recognition
FOR-species20K is the largest public LiDAR benchmark for tree-species classification, pooling 25 independently collected terrestrial, mobile, and airborne laser-scanning datasets across 33 species.StratFL: Countering Covert Model Poisoning Attacks with Strategic Federated Learning
Advanced persistent threats (APTs) are a major concern in federated learning (FL) deployments in critical sectors such as healthcare, and finance.Network-Wide Freeway Speed Nowcasting and Forecasting from Sparse Fixed Sensors with a Probe-Supervised Graph Convolutional Recurrent Network
Traffic agencies need current and near-future speeds on every link of their network, but their fixed sensors cover only part of it.A Plausible Explanation Is Enough to Mislead Multimodal Judges
Multimodal large language models (MLLMs) increasingly serve as judges in training and evaluation pipelines, often assessing answers together with explanations.ToMStories: Interpreting Theory-of-Mind in Synthetic Narratives
Understanding people often requires inferring what they feel or want from how they behave rather than from what they state outright.Measuring Mechanistic Uncertainty in Large Language Models via Causal Robustness
Large language models (LLMs) are increasingly used in critical domains, where their susceptibility to hallucinations makes reliable and interpretable uncertainty quantification essential.Hard Instances on Demand
The conclusions of an experimental study of algorithms are only as good as the instances they are run on.Diverse Reasoning Paths Matter: Symbolic Equivalence Augmentation for Enhancing RL Exploration
Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for improving the reasoning capabilities of LLMs.From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents
Long-term memory enables LLM agents to reuse past experiences, yet retrieving relevant memories does not necessarily provide sufficient evidence for answering a query.Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation
Modern mobile GUI agents can perceive visible screens and execute local actions, but they still lack persistent app-specific navigation knowledge: where functions live, which pages contain them, and…FlexUMM: Flexible Representation Learning for Unified Multimodal Models
Unified multimodal models (UMMs) can integrate understanding and generation within a single backbone.AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents
Forecasting an unresolved event requires finding relevant evidence and deciding how strongly it supports each outcome.Explore Separately, Adopt Selectively: Execution-Guided Tool Use over Large Tool Libraries
Large language model agents must discover relevant tools from large libraries, e.g., thousands of tools, as new needs arise during task execution.Repair, Retry, and the Cost of Hindsight: Verifier Feedback in Formal Theorem Proving
Verifier-guided self-repair conditions a model on feedback from failed attempts before it tries again, without weight updates.NoveltyScope: A Multi-Agent Research Assistant for Evidence-Grounded Novelty Assessment of Scientific Papers
NoveltyScope is a prototype that compares a scientific paper with retrieved prior work and produces a structured report and a five-level novelty score.Brain-Aligned Multimodal LLMs for Social and Emotional Understanding of Cartoon Movies
Multimodal large language models (MLLMs) offer scalable annotations of social and affective content, but their temporal agreement with human judgments and neural predictive value require further…GlobeBench: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations
Text-based world models have recently shown potential to simulate complex RL environment state transitions for a diverse set of use cases.