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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 27
Logarithmic Regret for Alternating Mirror Descent
Alternating online mirror descent (AltOMD) is a simple learning rule for zero-sum games, which improves upon simultaneous OMD numerically and theoretically.Going Deeper With Memories
Byte level language models avoid tokenizer dependence and provide universal coverage, but processing every byte at full resolution is expensive.NovoCRF: Globally Normalized De Novo Peptide Sequencing on a Prefix-Mass Lattice
De novo peptide sequencing has moved from dynamic programming over hand-designed or generative scores to large transformers that learn the task end to end.Gaussian Partitioning for Reliable Learning of Multi-Index Models
We introduce Gaussian partitioning, an algorithmic primitive for distributions smooth with respect to a Gaussian.Stabilizing On-Policy Targeted Guidance in Agentic Reinforcement Learning
Naive on-policy RL is often bottlenecked by the capabilities of the base model --- insufficient coverage can result in an inability to learn from certain rewards.iGC: Antibody Optimization via Reinforcement Learning over a Learned Somatic Hypermutation Prior
Antibody affinity maturation is a natural optimization process in which B cells acquire mutations through somatic hypermutation (SHM), undergo selection for improved antigen binding, and iteratively…De Rham cohomological architectures for PINNs and PDEs with topological holes
We attempt to alleviate failure modes of classical physics-informed neural networks (PINNs) in scenarios of topological pathologies corresponding to punctures, holes, poles, and voids by introducing…Conformal Prediction at Performative Equilibrium: The Price of Performativity
Conformal prediction gives finite-sample coverage when calibration and test points are exchangeable, but exchangeability can fail when deployment changes the data distribution.Knowingly Wrong: Training LLMs Against Their Own Beliefs can Induce Misalignment
Fine-tuning a large language model (LLM) on harmful data in a narrow domain, such as bad medical advice, can make it broadly misaligned.GSBR: Geometric Support Behavior Regularization for Online RL
Regularizing online reinforcement learning (RL) toward a pretrained base policy can improve sample efficiency and learning stability, but regularization that is too restrictive limits how much the…Universal Hardness From Masking Channels
We prove a broad learning-theoretic generalization of the celebrated hardcore lemma concerning the average-case complexity of computing a Boolean function (Impagliazzo, 1995).LoopVQ: Sub 1-bit KV Cache Quantization for Looped Transformers
Looped transformers are a parameter-efficient alternative to standard transformers for language modeling.$Φ$asmaNet: A Staged Spectral Token-Refinement Architecture for Fast Gravitational-Wave Signal-to-Noise Ratio Prediction
Quantifying selection effects in gravitational-wave population studies requires millions of costly signal-to-noise ratio (SNR) calculations.CATF: A Manager-Worker Framework for Context-Aware Time-Series Forecasting
We study context-aware time-series forecasting, in which a single target variable is predicted from its own history and a set of contextual variables, such as weather and calendar information.A simple adaptive accelerated optimistic dual averaging method for unconstrained convex optimization in $p$-norm geometry
Efficient preconditioning plays an important role in large-scale first-order optimization.A Circuit for In-Context Learning
In-context learning (ICL) lets a pretrained language model perform a task specified by demonstrations in its prompt, with no parameter updates.Agnostic Learning by Imitating
Given a class of machine learning models and sample access to a data distribution with binary labels, which computational task is harder: training a discriminative model that distinguishes positive…Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits
When a language model answers, we can usually only estimate why.Suppressing Misaligned Behaviors in Language Models, Durably
Undesired model behaviors can emerge or re-emerge at any stage of training.GUILD: Guided Iterative Data Collection for Imitation Learning
Imitation learning policies in robotics are trained on datasets collected through costly real-world teleoperation.