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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 82
Context Compaction for World Models
World models let observers explore and interact with generated worlds.Why Speculative Decoding Works Better Than Predicted on Sparse MoE Models
Speculative decoding (SD) accelerates autoregressive inference by leveraging the memory bound nature of decoding, verifying multiple draft tokens in a single forward pass.Benchmarking Graph Neural Networks for AC Power Flow: The Role of Inductive Biases and Physical Constraints
Fast and accurate models for AC power flow (ACPF) are increasingly important as modern electrical grids require more frequent system analyses.Syntactic Segmentation and Collage Generation for Videogame Levels with Perceptual Distance
In procedural videogame level design, many existing techniques combine hand-authored level chunks.Rethinking GFlowNet Training: Negative Reinforce Dynamics
Generative Flow Networks (GFlowNets) are models that learn to sample discrete objects proportional to a target reward.Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics
Sparse-view computed tomography (CT) reduces the number of acquired projections, but conventional sampling strategies are designed primarily for image reconstruction rather than for downstream…Formal Specifications with Data-Driven Signal Analysis
Developing analog and hybrid systems requires substantial domain knowledge that is hard to articulate in a formal specification; without additional effort, this domain knowledge is also inaccessible…Assessing feature scorer results on high-dimensional datasets with t-SNE
While vast literature on high-dimensional data visualization is available, there are not many works regarding the visualization of feature scorers and their results.Implicit Reasoning in Transformers: Bridging the Compositionality Gap in GPT-2-like Models
Compositional generalization remains a persistent limitation of transformer language models: even when a model learns to combine known facts correctly in-distribution (ID), the same compositional…Distilling the Shape of Uncertainty: What On-Policy Distillation Learns and How to Predict It
On-policy distillation (OPD) is increasingly adopted in the post-training of large language models.Representation Complexity Along Learning Trajectories: Trading Width for Precision and Time
What computational role does overparameterisation play during learning?When Should AI Decide, Defer, and Deviate? Responsible Automation for Deceased Donor Kidney Allocation
We ask when an AI trained on historical decisions should be allowed to recommend decisions, defer to human experts, or in other cases, flag concerning patterns in data and deviate from past practice.Reinforcement Learning for the Game of Balatro: A Fast Simulator and Two-Policy Training Approach
Balatro, a 2024 roguelike deckbuilder, presents three difficulties that have historically derailed reinforcement learning methods: a 3,000-action state-dependent action space, horizons of several…When Does Closed-Loop Curriculum Adaptation Help GRPO? An Analysis on Countdown
Reinforcement fine-tuning has become an effective post-training paradigm for improving the reasoning ability of large language models, but it remains sample-inefficient when the training…CausalGrade: Evidence-Guided Causal Graph Scoring with Jev
Evaluating the structural quality of candidate causal graphs when the true causal structure is unknown is an important problem in applying causal discovery results.Repulsive Population Policy Gradients for Behaviorally Diverse Reinforcement Learning
The goal of Quality-Diversity Reinforcement Learning (QD-RL) is to find a population of high-performing policies that solve a decision-making problem in diverse ways.LM-DSB: A Lightweight Multi-agent System for Automated Daily Safety Bulletin Generation in Construction
Construction safety management still struggles to produce daily safety bulletins (DSBs), structured daily communications that identify site-specific hazards, convey safety protocols, and set…Your Agent Acts Differently When You're Around: How User Presence Changes Agent Behavior in Negotiation
LLM agents increasingly interact with other agents on behalf of users, yet how users in the loop affect their agents' behavior--for better or worse--remains poorly understood.RouteDRAG: Learning to Route and Control Agentic Execution for Multimodal Document RAG
Web-based applications increasingly rely on understanding long multimodal documents, where answering questions often requires combining information across pages, tables, and figures.From Solver Trajectories to Teaching Trajectories: Cognition-Aligned Reasoning Distillation
Knowledge distillation from large reasoning models (LRMs) often uses raw teacher trajectories as supervision.