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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 212
INTACT: Inducing Latent Reasoning on a Frozen Backbone
Latent reasoning replaces natural-language chains of thought with compact continuous "soft-token" representations, reducing the number of intermediate tokens that must be decoded autoregressively.Geometric Kernel Diversity: An Axiomatic Diversity Measure in Euclidean Space
Quantifying the diversity of a set of objects is a fundamental problem in machine learning and statistics.On Inherent Privacy of Posterior Sampling: A Unified Rényi-Divergence Framework
Differential Privacy (DP) provides a rigorous framework for quantifying privacy guarantees.Learning Across Temporal Graph Representation Silos
Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains unclear.Bridging the Topological Divide: Dual-Branch Graph Distillation with Semantic Priors for Robust Cold-Start Recommendation
Graph Collaborative Filtering models have achieved remarkable success in recommendation systems.Who's Behind the Text? Robust Model Authorship Attribution
As large language models (LLMs) are increasingly used to generate text, their misuse has become a growing societal problem, with implications for academic misconduct, model distillation, and…Projected Wasserstein-Fisher-Rao Flows for Hawkes Background-Intensity Estimation
We consider the problem of estimating the background intensity of a Hawkes process when the triggering mechanism is known, treating the background as an unknown finite positive measure with a…L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents
The lasting gains in AI have come from methods that absorb more computation, and language models already scale computation within a call.InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design
The ability to generate proteins with desired functions is a central goal of computational biology.Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting
Transformers have demonstrated strong performance in long-term time series forecasting (LTSF), yet modeling non-stationary temporal dependencies remains challenging.PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference
Long-output inference in large language models suffers from severe KV cache memory and bandwidth bottlenecks.A Graph-Based Analysis of Comparability, Scale, and Attribution in Private Transformer Inference
Rising privacy concerns around large language models have accelerated research in private Transformer inference (PTI).Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations.DAGFormer: Global Topology Prediction for Transformer Cross-Layer Connections
We propose DAGFormer, a Transformer language model whose cross-layer connections are planned by a jointly trained topology predictor.Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching
Recent work has explored visual reasoning through image generation, where models solve visual-reasoning problems by directly generating solution images.Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials
Automated materials discovery requires evaluating large pools of candidate configurations, with density functional theory (DFT) as a major throughput bottleneck.Sparse Autoencoders Are a Shared Coordinate System for Symbolic Matching
Large language models (LLMs) hold broad knowledge in one set of weights, but lack interpretability and trustworthy reasoning, especially in high-stakes use cases.Synthetic reaction data from transformation rules for graph-to-sequence pretraining in biodegradation pathway prediction
The scarcity of observed biodegradation reactions constrains the training of product and pathway predictors.Model What Matters: Decision-Guided Selective World Modeling for Language Agents
Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typically sparse trajectory-level signals that only…On Global Joint Lower Bounds in Zeroth-Order Nonconvex Optimization
Recent work has sought to transfer first-order nonconvex optimization guarantees to zeroth-order methods through gradient estimators, typically incurring a factor linear in $d$…