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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 10
RACE: Calibrated Cumulative Scoring for Rollout-Free Robot Policy Checkpoint Selection
Assessing the deployment performance of learned robot policies during training is challenging, as directly measuring task success requires costly real-world trials for each candidate checkpoint.PEARL: Prediction Error-Adaptive Regularized Learning for Selective Forgetting under Partial Preference Drift
User preferences shift as needs, context, and tastes evolve.A low-rank decoder bottleneck bounds reliability in foundation-model perturbation prediction
Foundation-model predictors of perturbation response are evaluated primarily by aggregate accuracy.On Convergence of Gradient Temporal-Difference Learning with Regularized Corrections and Eligibility Traces
Temporal-difference (TD) learning underlies many prediction and control algorithms, but can diverge when combined with off-policy learning, bootstrapping, and function approximation.AI-Assisted Classification under Correlation Neglect and Trust
When does an AI assistant improve human decision-making, and when does it hurt?MIGONet: a multiple-input graph neural operator with solver-free discrete manufactured-solution training
Surrogate modeling of transport requires operators that capture the combined influence of spatially distributed sources and Dirichlet and Neumann boundary conditions, together with training datasets…When Is One Query per Template Enough? Adapting Learned Cost Models to New Hardware
A learned cost model that predicts query runtimes is trained on measurements from one machine.Information-Matched Auditing of Conversational Emotion-Shift Forecasting: A Leakage-Safe Evaluation Protocol
Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion…Retrieval Supersedes Scale: Efficient Log Level Prediction via Multiplex Socio-Technical Context
Developers insert logging statements in source code to record its execution, and the resulting logs are essential for detecting and troubleshooting system anomalies.Does Your Trial Simulator Know What the Drug Does? An Intervention-Blind Test of Interventional Validity on 7,207 Registered Trials
Patient world models are increasingly proposed as simulators of clinical trials, and recent surveys rank their capabilities on a ladder from temporal prediction to counterfactual rollouts.Improved approximation algorithms for capacitated cluster editing
We study the minimum-disagreement cluster editing problem that, from a complete edge-signed graph, where a positive edge represents two vertices agreeing to each other while a negative represents a…Routes of Reasoning: Reading and Reshaping Test-Time Compute in MoE Models
Reasoning models allocate test-time compute across distinct behaviors such as planning, deriving, and verifying.Diversity-Aware Experience Replay via Statistical Leverage Scores
Off-policy deep reinforcement learning trains on random samples from a replay buffer of past experience.Deep Meets Classical: A CPU-Efficient Decision-Fusion Model for Automatic Modulation Classification
Automatic modulation classification (AMC) underpins spectrum sensing and non-cooperative signal identification in cognitive radio, yet the two standard CPU-feasible approaches to it, a compact…BAM: Bivariate Additive Models for Interpretable Tabular Prediction
Generalized Additive Models with pairwise interactions (GA$^2$Ms) are regarded as glass-box models, since each pairwise shape function can be visualized as a contour plot.Reward Budgeting Reduces Premature Convergence in Reinforcement Learning for LLM Reasoning
RL for LLM reasoning often reaches an early performance plateau as the policy distribution becomes increasingly sharp.On the Design of Hyper-Connections: Simplification, Stability, and Scalability
Hyper-Connections (HC) architectures are increasingly adopted in frontier language models, yet their components remain insufficiently understood and introduce non-negligible training overhead.A Comparative Study of Importance Attribution to Columns in Deep Learning over Relational Databases
Deep learning over relational databases has led to increasingly effective prediction models, motivating a growing need to explain their behavior.A Controlled Experimental Evaluation of Configuration and Cache Architecture in LLM Serving Engines
Multi-agent LLM applications repeat most of their prompt tokens across requests, and serving engines reuse the cached state of shared prefixes. vLLM and SGLang reuse it at different granularities…Clips2Story: Training-Free Video Storyboarding and Editing Using Multimodal Retrieval-Embedded Generation
Video editors often work with large collections of fragmented footage and must select and organize relevant clips into a coherent story.