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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 184
STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases
In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions.Compression Precedes, Content Decides: Emergent Misalignment in Reward Models
Emergent Misalignment - the out of domain generalization of narrow harmful fine-tuning first shown by Betley et al. [2025] has been widely studied in language models but not in reward models that…World Action Critic (WAC) Model: Joint Foresight and Criticism for Policy Learning
World models have shown considerable promise in sequential decision-making by anticipating the consequences of actions and providing predictive foresight for policy learning.The Reliance Hypothesis: Chain-of-Thought Faithfulness Rises as Language Resource Level Falls
Chain-of-Thought (CoT) faithfulness matters for monitoring, explaining and safely deploying large language models, yet it has been studied mostly in English.Sample-Efficient Reinforcement Learning for Policy Improvement via Flow-Reversed Action Anchors
Generalist robot policies offer strong behavioral priors, but achieving deployment-level reliability and throughput remains difficult.Uncertainty-Aware Budget Allocation for Efficient LLM Tree Search
Tree search enhances multi-step reasoning in large language models (LLMs), yet prevailing methods expand every step with a fixed budget, irrespective of the marginal utility of expansion.Evo-B&B: Behavior-Aware LLM-Guided Joint Evolution of Branching and Node Selection Policies for MILP
In branch-and-bound for Mixed-Integer Linear Programming (MILP), branching and node selection are two critical and tightly coupled decisions, yet most existing learning-based approaches focus on…MS-Eval: Benchmarking Sub-Agent Delegation and Orchestration in MCP-Intensive Working Scenarios
LLM agents are increasingly integrated into real-world works, yet they must handle diverse domain-specific tools and complex multi-sub-task problems that sequential tool invocation resolves…ShadowFPT: Backdooring Federated Prompt Tuning via Shadow Triggers
Federated Prompt Tuning (FPT) adapts large vision--language models by freezing the pretrained backbone and optimizing only lightweight prompt parameters across clients.Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting
Time series forecasting models are increasingly deployed in continuously evolving environments.Cooperation Without Adaptation: LLM Agents in a Collective-Risk Dilemma
Language-model agents are increasingly deployed to act on behalf of people in settings where individual decisions shape shared outcomes.ToPA: Block-wise Toeplitz Adaptation for Expressive and Efficient Fine-Tuning
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a prevalent strategy for adapting large pre-trained models.Learning to Be Guided: Shaping Latent Geometry for Classifier-Free Guidance
Classifier-free guidance (CFG) is a standard component of conditional diffusion models, and guided generation performance has become the \textit{de facto} measure of state-of-the-art models.Batched Contextual Reinforcement II: Does RLVR Need Long Rollout Windows?
Long rollout windows make reinforcement learning with verifiable rewards (RLVR) expensive, yet simply shortening them can remove the correctness signal: when every single-problem rollout truncates…Competence-Based Evaluation of Logical Reasoning in Large Language Models
Recent advances in large language models (LLMs) have brought impressive performance across diverse tasks; however, shortcomings remain in logical reasoning, including inconsistencies under…HAPACT: A Benchmark For Human-Centric Physical Impact Localization in Movies
Physical reasoning about humans in videos has largely focused on everyday interactions, where contact, pressure, pose, and motion support embodied perception and contact-aware applications.When to Forget: Risk-Controlled Context Adaptation for Long-Horizon Agents
Long-horizon agents learn from failure, but the value of a failure record can change as execution progresses.COMET: Decoupled Distillation, Routing, and Capacity Control for Task-Agnostic Continual Vision--Language Learning
Vision-language models (VLMs) such as CLIP retain zero-shot recognition ability, but their open-world use often requires task-agnostic continual learning: tasks arrive sequentially, past data are…Reward Engine: Reward-Centric Self-Evolution for Open-Ended Tool Agents
Open-ended tool agents can complete the same objective through different tool paths, and their quality depends jointly on the interaction trace, environment state, and final artifacts, so such tasks…Sep-Latent: Separator Tokens as Natural Compression Anchors for Implicit Reasoning
Large Language Models (LLMs) have demonstrated remarkable success in complex reasoning through explicit Chain-of-Thought (CoT), but the associated computational overhead and token verbosity remain…