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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Toward an Optimal Configuration for Tree Species Classification from Bark Images: A Leakage-Safe Ablation Study
The automatic identification of tree species from bark photographs is of practical interest for forestry inventory and the wood-processing industry, yet the design choices that govern the accuracy…Neural networks and games
Hopfield neural networks and noncooperative games provide seemingly different approaches to understanding how collective outcomes emerge from local interactions.Agents, Think Only When it Helps
LLM agent distillation transfers agentic capabilities to student models by training on complete teacher trajectories that typically interleave thoughts and actions.CoReFlow: Consistency-Relaxed Flow Maps for Probabilistic Traffic Forecasting
Probabilistic traffic forecasting is essential for reliable Intelligent Transportation Systems (ITS), as it captures uncertainties in complex road networks.Give the Drafter a Second Chance to Guess: Parallel Reconsideration for Speculative Decoding
Speculative decoding accelerates large language model inference with a small drafter, whose per-guess acceptance rate determines the achievable speedup.Do Language Models Know Which Constraints Belong Together?
Dantzig-Wolfe reformulation strengthens the linear relaxation of a mixed-integer program by convexifying a chosen set of constraint families, and every candidate costs a column generation run.Generative Modeling for Cascading Extremes via Autoregressive Transformers
Extreme events often occur sequentially, from earthquake-tsunami cascades to successive extreme losses in financial markets.FRESHPart: Feedback-Reshaped Spectral Embedding for Hypergraph Partitioning
With scalable Laplacian solvers now available, the next frontier is to exploit them more effectively for high-quality hypergraph partitioning.Self-Supervised Learning of Universal Latent Spaces for Operator Learning
Neural operators fit their representation of functions to the task they are trained on, so every new task rebuilds a reduced space from labeled data.OdorSpaces: Visual-Olfactory Embodied Navigation in 3D Environments
Olfaction provides critical information about airborne chemical cues, yet it remains largely underexplored in embodied AI, with limited prior machine olfaction work focusing primarily on semantic…Learning Hierarchical Patch Splitting Policies for Faster Vision Transformers
Vision Transformers (ViTs) are bottlenecked by the length of their input token sequence.Text-Only Domain Adaptation for Low-Resource Medical ASR: Shallow Fusion and Density Ratio Rescoring for Hausa
Automatic speech recognition (ASR) systems often struggle with specialised medical vocabulary, especially in low-resource languages where domain-specific speech data are scarce.Pairing Expert Critique with LLM-Driven Refinement: An Iterative Approach for Curating Quality AI Evaluation Datasets
Evaluating and improving domain-specific LLM responses requires high-quality reference data and rubrics, yet expert authoring is slow and expensive, and synthetic datasets may fail to capture…SteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Calibrated Uncertainty
Steel property models can perform well on random test splits even when they extrapolate poorly to unfamiliar alloys.Octopus: One Brain, Many Branches for Latent-KV Multi-Agent Reinforcement Learning
Multi-agent LLM systems explore in parallel by spawning sub-agents with isolated context windows, whereas single-agent systems append every search and verification step into one growing trajectory.CytoWave: Perturbation-Centric Pretraining for Single-Cell Response Prediction
Predicting gene expression under perturbations from single-cell data is a fundamental task for modeling cellular responses, with applications in discovering gene function and designing therapeutics.Improving Financial Cybersecurity Through Fraud Transaction Detection with Deep Learning
Financial fraud detection remains a critical challenge in cybersecurity, with traditional rule-based systems struggling to keep pace with evolving attack patterns.A Capitulation Direction Emerges With Scale and Is Causally Load-Bearing Where Validation Is Strong: Eight Models, Two Families, and Five Measurement Hazards
Sycophancy in language models is well documented as a behaviour but poorly localised as a mechanism.When Does Adaptive Halting Win? Selection Rules Matter in Looped Language Models
Looped language models can stop iterating early, and recent work shows that post-hoc readouts of the loop trajectory often rival the learned exit gate of the looped model Ouro.Attribution-based diagnostics for single-cell foundation models
Single-cell RNA foundation models (scFMs) are increasingly used for in-silico biological discovery, yet predictive performance alone does not reveal whether their decisions rely on biologically…