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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 107
Verifiable Efficiency Limits of Innate LLM Planning in Sliding Puzzles
Tool-assisted planning success can reflect external search and verification, leaving a model's unaided planning ability unresolved.Search-Guided Reasoning Trace Curation for Verilog Generation
Recent advances in large language models (LLMs) have improved Verilog generation.Continual Learning Exam: Benchmarking Long-Horizon RSI and Experiential Learning in LLM Agents
Continual learning is the ability of LLM agents to improve over time by acquiring, refining, and applying knowledge from experience while retaining useful prior knowledge.Beyond Belief: Evaluating and Mitigating LLM Susceptibility to Misinformation
Tool-using large language models (LLMs) must decide whether external information warrants action.QECGBERT: A Scalable ECG Foundation Model Based on Self-Supervised Language Learning via VQ-VAE Waveform Tokenization
AI foundation models play a pivotal role in advancing heart disease diagnosis from electrocardiogram (ECG) data, particularly for rare diseases where the lack of sufficient labeled data hinders…Hierarchical Cooperative GFlowNets
Generative Flow Networks (GFlowNets) are trained to sample objects from compositional spaces with probability proportional to a reward function, but ineffective exploration can cause learned…Steering the Teacher: Improving On-Policy Self-Distillation for Thinking Models
On-policy self-distillation (OPSD) trains a student on its own reasoning trajectories using token-level supervision from a reference-conditioned teacher initialized from the same model.Fed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems
Conventional federated learners implicitly optimize for global means, creating a "one-size-fits-all" paradigm that inevitably suppresses the minority under cross-site heterogeneity.BriLLM-MetaPred: A Representation-Prediction Unity Language Model with Adaptive Self-Measurement
We present BriLLM-MetaPred, the first engineering implementation of the MetaPred machine-learning framework established by a mathematical theory of AGI, instantiated for language modeling.Deep Scientific Reasoning under Physical Constraints: Structure-Aware Spectrum Prediction
Structured scientific spectra encode rich physical information while obeying hard constraints, such as conservation laws and characteristic spectral geometry.Holding a Steady Course: Knowledge Evolution from Expertise and Lessons for Continual Agent Learning
Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time.A Sign Law for Entropy Drift under Sampler Mismatch in RLVR
In reinforcement learning with verifiable rewards (RLVR), whether entropy rises or falls can depend on choices that matter little under exact sampling, such as whether over-length responses are…Extended LASMP: Language Aided Subset Sampling Based Motion Planner for Drone Navigation
This paper proposes a command-aligned sampling distribution that incorporates navigation commands directly into the exploration process to improve the efficiency of sampling-based motion planning…Identically Trained Language Models Break on Different Jailbreaks
Jailbreak benchmarks test one checkpoint and treat which jailbreaks break it as a property of its pretraining data and safety training.From Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
Large language models (LLMs) generally lack deep causal reasoning capabilities in temporal reasoning.T2D: Target-to-Draft Model Pruning & Adaptation for Efficient Speculative Decoding
Speculative decoding significantly accelerates autoregressive text generation in Large Language Models (LLMs), but its efficacy depends largely on two key factors: the distributional alignment…Can LLMs Trust Appropriately? Evaluating Calibrated Reliance on Imperfect Partners
Large language models increasingly collaborate with humans and peer AI agents whose contributions may be incomplete, inconsistent, or incorrect.HumanoidNDM: Closing the Training-Deployment Gap of Motion Tracking via Humanoid Neural Dynamics Model
Humanoid robots have recently achieved remarkable success in motion tracking.Decoupled Prune-and-Reconstruct: Training on a Compact Core with Double-Layer Sparsification
On million-node graphs, the memory wall arrives before the compute wall: a GNN that never fits in VRAM cannot deliver the all-node inference its accuracy promises.Rethinking Retrieval in the Long-Context Era: Training-Free Semantic Trees for Adaptive Context Construction
Long-context language models can increasingly process entire documents, making full-context prompting a practical alternative to passage retrieval.