AI & Machine Learning · Computer Science & AI
Machine Learning
The newest papers in Machine Learning 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 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 43
Rapid Mean-Field Formation Transport with a Shared-Representation Neural Fokker-Planck Controller
This paper develops a mean-field formation transport method with low online computational cost for large-scale multi-agent systems.Thinking Fast, Trading Late: System One Decision Models vs the Market
Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns.FlagRot: Selective Rotation for Low-Bit KV Caches
Long-context inference is bound by the KV cache, which 2-bit codes could shrink about sixfold if their error stays in check.WASP-Mem: Let Agent Organize Procedural Memory. Human Spend Effort on Diagnosis
LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture proprietary policies, undocumented behaviors, or state-dependent recovery paths that…Physics-Informed Identification of Tensegrity Prestress across Taut-Slack Transitions
Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service…Decision Transformer with Task-Aware Representations for Offline Meta-Reinforcement Learning
To enable effective generalization in offline meta-RL, it is essential to accurately infer the underlying task through latent task representations, which provide a compact and transferable summary…Language Model Augmented Decision Transformer for Multi-Task Offline Reinforcement Learning
Offline multi-task reinforcement learning (MTRL) aims to learn a unified policy from heterogeneous offline datasets that generalizes across tasks.Testing Processor Contracts in Speculative Decoding Engines
Speculative sampling can accelerate decoding without changing the target distribution.Denoising Is Stein: Moment Access and a Resource Hierarchy for Learning Hidden Structure in Diffusion Models
We study score learning from denoising pairs when the data contain a hidden non-Gaussian component.Forgetting to Improve: Principled Data Removal in Active Learning
The uncertainty of a statistical model is most commonly factorized into an aleatoric and an epistemic part.HybridSpec: Throughput-Aware Speculative Decoding for Hybrid GDN-MoE Models
In speculative decoding, parallel drafters such as DFlash make drafting cheap, so target verification dominates each round.Structured Priors: Towards an algebra of component priors for prior-data fitted networks
Prior-data fitted networks are typically trained using synthetic data generated by a single, manually constructed generator.StreamLTT: Risk Certification for Policy Selection under Stream Dependence
Learn-then-Test (LTT) provides a general framework for certifying deployment policies against a prescribed risk target, but its standard formulation targets a fixed population risk estimated from…TB-Transolver: Parallelized and Coherent Long-Horizon Rollouts via Temporal Bundling for Time-Dependent PDEs
Transolver has recently gained widespread adoption for solving diverse physical problems on unstructured geometric meshes by leveraging its Physics-Attention mechanism, which seamlessly extends…Graph-Localized Coverage for Conformal Prediction on Inductive Graphs
Conformal prediction (CP) provides marginal coverage guarantees, but marginal coverage does not determine where prediction errors occur on a graph.Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies
We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network.Computer-Use Agents Gain Little Benefit from a World Model When Conditioned on Tutorials
Recent work has improved computer-use agents (CUAs) by combining retrieved task-relevant tutorials and look-ahead planning using world models with MLLM judges.The Complexity of ReLU Network Synthesis from a Parameter Multiset
We ask how hard it is to build a ReLU network when its parameter values are given in advance.AlgoPilot: Cross-Paradigm Reasoning in Language Models via Strategy Selection and Guidance
Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optimization, and often require fundamentally different solution paradigms.RAPID: Increasing Efficiency of Internal Distillation via Randomized Methods
Knowledge distillation is a technique used to transfer the expertise of large teacher models into small, efficient student models.