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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 225
Foundation Models for Event Prediction Across Heterogeneous Event Logs
Event logs are ubiquitous across real-world domains, including business processes, manufacturing workflows, healthcare, and many other operational systems.Merit: Making every modality earn its contribution in multimodal attention decoding
More than 1.5 billion people have hearing loss.CÆNL: Collapse-Based Active Neural Learning with Objective-Aligned Representation Control
Active learning changes the representations used to acquire subsequent labels.ATTUNE: Agentic Throughput Tuning for LLM Training
Training recipes are tuned by hand for one model, cluster and workload, and every new model, accelerator, framework version or dataset means tuning again.Towards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference
While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact on energy usage and carbon intensity are…Dissociating Answerability from Extraction in LLM Question Answering over Radiology Reports
Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it.Risk-Averse Inference-Time Diffusion Model Alignment with Finite-Sample Guarantees
Inference-time alignment steers diffusion models toward downstream objectives without costly fine-tuning, but existing methods typically optimize expected cost (or reward) without explicitly…Chasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification
Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and firms transition between financial risk states.Taming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry
Muon controls weight updates through the RMS movement of a linear layer's output.Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models
Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregressive language models (ARMs)…Training Generalist Navigation Policies via Action Self-Distillation
Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning typically requires expensive manual annotations.Morphology-aware deep learning predicts colorectal cancer survival from whole-slide images
Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and the tissue morphologies carrying prognostic…FedLoRD: Personalized Federated Learning based on Low-Rank Structures of Model Parameter Differences
We propose FedLoRD, a personalized federated learning (PFL) method based on the low-rank structure of the differences between the global and personalized model parameters.RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning
Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the rubric carries and gives no GRPO reward gradient…Calibrated Disambiguation for Partial Multi-label Learning
Partial multi-label learning (PML) aims to train a classifier on datasets whose instances are over-annotated with not only relevant labels but also irrelevant labels, which is common when datasets…The Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models.Mixed Precision Scaling Laws for Quantized Pretraining
Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the emerging low-precision data types supported on AI…DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss
Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit.textGrain watermarking is harder to detect: can sum-tests still do it optimally?
Watermarking embeds an imperceptible signal in text from large language models that is statistically detectable with a secret key.DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter
Reinforcement learning with verifiable rewards (RLVR) for multi-turn agents relies on sparse terminal rewards that say little about individual steps.