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Machine Learning
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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 197
Noise-Space Reward Alignment for Few-Step Generative Models
Few-step generative models produce high-quality samples in one or a few evaluations.Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble
Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous, and contain pathway-dependent variables prone…Pathwise Shrinkage and Probabilistic Calibration of the Multiplicative Half-Cauchy Process
We characterize the multiplicative half-Cauchy process through its log-scale random-walk representation, establishing shrinkage, expansion, and critical regimes, almost-sure summability, local…When is a better LLM worth paying for? Stochastic Optimization with Switching Mixed-Fidelity Oracles
Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values.Coordination-Free Workload Regularization for Variable-Cost Distributed Training
Synchronous data-parallel training usually assigns every rank the same number of records, even when record costs vary by orders of magnitude.Language Models Share a Readout Bottleneck in Working Memory
Large language models (LLMs) reproduce a human working-memory signature on the N-back task: performance declines as memory load N increases, and errors concentrate on lures, items that repeat the…Judging the Judges: Confidence and Disagreement in Rubrics as Rewards
Rubrics as Rewards (RaR) extends reinforcement learning to non-verifiable domains by using an LLM judge to score responses against a structured rubric.Mine, Sample, Render: Test-Time Diversity Alignment Of Generative Models
Modern generative models produce high-quality individual outputs but struggle to sample diversely, even when explicitly instructed to do so.Sensor Diagnosis for Fuel and Emissions Monitoring in Heavy-Duty Diesel Vehicles
Faulty sensor readings can distort estimates of fuel use and carbon dioxide emissions from diesel engines.Resisting Harmful Instructions through Training-Free Weight Editing
Instruction-following language models can comply with requests that conflict with intended behaviors and safety alignment.Poisoning the corpora of protein design systems during pre-training and retrieval
Protein foundation models increasingly support the generation of novel biological sequences, yet their reliance on large public datasets creates opportunities for adversarial manipulation of the…Hard Way, The Only Way: Provable Merits of Training on Hardest Examples
Despite a surge of empirical interest in the ``unreasonable effectiveness'' of drastic data pruning for AI systems, a unified theoretical understanding of this phenomenon remains elusive.Clean Data Can Still Carry Backdoors: Support-Persistent Backdoors for Model Reuse
Traditional backdoor attacks inject artificial trigger patterns into training data, causing models to misclassify triggered inputs while maintaining normal behavior on clean samples.Learning and Forecasting Multiscale Spatiotemporal Epidemic Dynamics with Lagrangian-Eulerian Graph Neural ODEs
Accurate medium- to long-range epidemic forecasting requires extrapolating high-dimensional, heterogeneous and nonstationary dynamics from partial observations.What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination
Reinforcement learning with verifiable rewards (RLVR) math corpora are routinely screened for benchmark overlap, but the reliability and data-loss costs of cleaning policies remain underexamined.Not All Change Is Damage: Update-Matched Unlearning for Large Language Models
Unlearning aims to remove selected knowledge from large language models to mitigate privacy and safety risks while preserving useful capabilities.Compatibility Matters: A Framework for Token-Level Teacher Selection in On-Policy Distillation
On-policy distillation (OPD) has recently been introduced as a promising paradigm for continual learning, where a student policy can be supervised at the token-level.Representation Redistribution in Sparse Autoencoders
Sparse autoencoders (SAEs) decompose language model activations into sparse features, but prompt transformations designed to preserve the underlying request can redistribute activity across them.System1-Arena: Which Decisions Can Be Left to System 1?
Large language models (LLMs) often make discrete decisions, such as flagging phishing or escalating to a human.Isolating a Language Model's Judgment with a Label-Free Subspace
Language models are often asked to make judgments: whether a proposed answer is correct, whether one statement follows logically from another, or whether a response is safe.