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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 66
Spectral and Conformal: Neural Network Methods for Calibrated Trajectory Prediction and Control-Point Intervals
Autonomous systems that navigate their environment need methods to quickly assess the feasibility and safety of desired trajectories.Localized anomaly detection with a calorimeter normalizing flow
Normalizing flows trained as fast calorimeter simulators also give the likelihood of each shower, which can serve as an anomaly score.ProLand: A New Infeasible Manifold Optimization Method for LoRA Fine-Tuning
Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning but can suffer from redundant basis directions.IsoAlign: Isoform-Aware DNA-Protein Alignment via Joint-Embedding Predictive Architectures
Genomic foundation models (GFMs) have emerged as a powerful paradigm for learning DNA sequence representations, yet existing self-supervised pretraining strategies overlook alternative splicing and…Model Collapse Is Avoidable Even as Real Data Vanishes: A Sharp Criterion for Iterative Gaussian Estimation
Large generative models are commonly trained on data collected from the open web, which may contain data produced by previous generations of the same model.KINDER: Kernel-based Independence for Fair Representation Learning via Prototype-space Erasure
Concept erasure is a prominent approach to achieving fairness in machine learning through the removal of sensitive attributes from learned representations.Outstrumental Variable Regression
Estimating the causal effect of a treatment $X$ on an outcome $Y$ from observational data in the presence of unobserved confounding requires additional structure or assumptions.DELBERT-2: Pretrained Fingerprint Language Models for DEL Protein Binder Prediction
We present DELBERT-2, a pretrained fingerprint language model that converts sparse molecular fingerprints into unified token sequences using a ModernBERT encoder trained with masked language…OCELOT: Planetary-Scale Species Distribution Modeling with Satellite Foundation Embeddings
Large-scale repositories of species observations now contain billions of occurrence records from around the world, but these sightings cluster heavily in human-accessible areas.Quantization of Large Models with Weight Outliers and Sparsity
Post-training quantization (PTQ) reduces the memory burden of large model deployment by casting the high precision trained weights to fewer bits.A Framework for Task-Agnostic Counterfactual Fairness in Transformers through Activation Steering
Bias in transformer-based models is a growing concern as these architectures become more prevalent in automated decision-making systems, such as prediction models for hiring, lending, and clinical…The Rise of Low Precision Floating Point
Floating point computing has dominated scientific computations since the 1950s because of its versatile ability to approximate real numbers with both sufficiently high precision and sufficiently…RaMMOBO: Random Max-Margin Multi-Objective Bayesian Optimization
Multi-objective Bayesian optimization (MOBO) maximizes $M$ expensive black-box objectives with the goal of approximating the Pareto set, whose objective vectors cannot be improved in one component…Cyber-physical attack detection approaches in the wide-area measurement system
Improving situational awareness within the Smart Grid is an effective preventive measure to detect potential anomalies, and avoid their adverse consequences.C3: Speech-Guided KV Retrieval for Long-Context SpeechLLM Inference
Speech language models encode audio at a much higher rate than text, causing the KV cache to dominate the memory, bandwidth, and decoding cost of long-form generation.Decomposing Irregular Time Series into Interpretable Temporal Patterns with Mixture-of-Experts
Irregularly-spaced sequential data, such as transaction logs, encode multi-scale temporal structure that is critical for prediction but difficult to recover explicitly with standard sequence models…DEPLOY-RL: Active Boundary Discovery and Conservative Certification for Deployable Reinforcement Learning in Safety-Critical Continuous Processes
Reinforcement learning (RL) policies often outperform classical controllers in simulation, yet rarely reach production in safety-critical processes.SPARK: Self-Correcting Embodied Memory via Physical Re-Observation
Retrieval-augmented generation (RAG) systems have seen growing adoption in embodied settings as language-queryable memories of the environment.On the Limits of Test-Time Compute for Self-Evaluation
Self-verification and aggregation have emerged as popular approaches for bridging the gap between pass@1 and pass@N.Unlearning by Extrapolating a Shrink-and-Heal Lookahead
Unlearning aims to make a large language model behave like one retrained without its forget set.