All entries

LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations

arXiv:2605.08279v1 Announce Type: cross Abstract: Learning predictive world models from visual observations is a core problem in embodied AI, with applications to model-based reinforcement learning an

Layer Collapse in Diffusion Language Models

DGX agent

arXiv:2605.06366v2 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their

model-releasesarxiv-cs-lg

12 May 2026

LBI: Parallel Scan Backpropagation via Latent Bounded Interfaces

arXiv:2605.09204v1 Announce Type: new Abstract: Backpropagation is inherently sequential across depth, creating an O(K)-deep dependency chain that bottlenecks parallel training. While parallel-scan fo

Agents

LCGNav: Local Candidate-Aware Geometric Enhancement for General Topological Planning in Vision-Language Navigation

arXiv:2605.09053v1 Announce Type: new Abstract: Online topological planning has become an effective paradigm for Vision-Language Navigation in Continuous Environments (VLN-CE), but existing methods st

agentsarxiv-cs-cv

Agents

LE-PAVD: Learning-Enhanced Physics-Aware Vehicle Dynamics for High-Speed Autonomous Navigation

arXiv:2605.08489v1 Announce Type: new Abstract: Accurate modeling of nonlinear vehicle dynamics is essential for high-speed autonomous racing, where controllers operate at the handling limits. Model-b

agentsarxiv-cs-ro

LEAD: Length-Efficient Adaptive and Dynamic Reasoning for Large Language Models

arXiv:2605.09806v1 Announce Type: cross Abstract: Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated

LEAF-SQL: Level-wise Exploration with Adaptive Fine-graining for Text-to-SQL Skeleton Prediction

model-releasesarxiv-cs-cl

arXiv:2605.09295v1 Announce Type: new Abstract: Text-to-SQL translates natural language questions into executable SQL queries, enabling intuitive database access for non-experts. While large language

Applications

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling

arXiv:2605.10292v1 Announce Type: cross Abstract: Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-maki

applicationsarxiv-cs-ai

Learn the system

This article likely provides guidance on understanding how AI systems work, their underlying mechanisms, and best practices for effectively using or interacting with AI tools. Based on Ben's Bites' fo

tutorialsben-s-bites

Learnability and Competition in High-Dimensional Multi-Component ICA

arXiv:2605.08552v1 Announce Type: cross Abstract: Independent Component Analysis (ICA) is a foundational tool for unsupervised representation learning, yet its high-dimensional theory remains largely

Learngene Search Across Multiple Datasets for Building Variable-Sized Models

arXiv:2605.08209v1 Announce Type: new Abstract: Deep learning methods are widely used under diverse resource constraints, resulting in models of varying sizes, such as the Vision Transformer (ViT) ser

Learning Agile Striker Skills for Humanoid Soccer Robots from Noisy Sensory Input

model-releasesarxiv-cs-ro

arXiv:2512.06571v3 Announce Type: replace Abstract: Learning fast and robust ball-kicking skills is a critical capability for humanoid soccer robots, yet it remains a challenging problem due to the ne

Learning Approximate Nash Equilibria in Cooperative Multi-Agent Reinforcement Learning via Mean-Field Subsampling

arXiv:2603.03759v2 Announce Type: replace-cross Abstract: Many large-scale platforms and networked control systems have a centralized decision maker interacting with a massive population of agents und

Applications

Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts

arXiv:2605.09382v1 Announce Type: cross Abstract: The Linear Assignment Problem (LAP) is a fundamental combinatorial optimization task with applications ranging from computer vision to logistics. Clas

applicationsarxiv-cs-cv

Learning Confidence Ellipsoids and Applications to Robust Subspace Recovery

model-releasesarxiv-cs-lg

arXiv:2512.16875v4 Announce Type: replace-cross Abstract: We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution D an

Learning from Acceptance: Cumulative Regret in the Game of Coding

arXiv:2605.09754v1 Announce Type: cross Abstract: Classical coding-theoretic guarantees often rely on trust assumptions, such as requiring sufficiently many honest nodes compared with adversarial ones

tutorialsarxiv-cs-lg

Learning from Trials and Errors: Reflective Test-Time Planning for Embodied LLMs

arXiv:2602.21198v2 Announce Type: replace-cross Abstract: Embodied LLMs endow robots with high-level task reasoning, but they cannot reflect on what went wrong or why, turning deployment into a sequen

Learning Graph Foundation Models on Riemannian Graph-of-Graphs

arXiv:2605.09993v1 Announce Type: new Abstract: Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across

Learning Less Is More: Premature Upper-Layer Attention Specialization Hurts Language Model Pretraining

model-releasesarxiv-cs-cl

arXiv:2605.10504v1 Announce Type: new Abstract: A causal-decoder block is hierarchical: lower layers build the residual basis that upper layers attend over. We identify a failure mode in GPT pretraini

Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding

arXiv:2605.10855v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable progress in chart understanding, largely driven by supervised fine-tuning (SFT) on increasing

researcharxiv-cs-cl

Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies

arXiv:2605.09665v1 Announce Type: cross Abstract: Data selection is a key component of efficient instruction tuning for large language models, as recent work has shown that data quality often matters

Local Ai

Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice

arXiv:2605.10456v1 Announce Type: new Abstract: Point clouds are a fundamental representation for robotic perception tasks such as localization, mapping, and object pose estimation. However, LiDAR-acq

local-aiarxiv-cs-ro

Learning Polyhedral Conformal Sets for Robust Optimization

arXiv:2605.08506v1 Announce Type: new Abstract: Robust optimization (RO) provides a principled framework for decision-making under uncertainty, but its performance critically depends on the choice of

Learning predictive models for combinations of heterogeneous proteomic data sources

arXiv:2605.08958v1 Announce Type: new Abstract: Multiple technologies that measure expression levels of protein mixtures in the human body offer a potential for detection and understanding the disease

Learning Pure Quantum States in Any Dimension (Almost) Without Regret

arXiv:2605.09019v1 Announce Type: cross Abstract: We extend quantum state tomography with minimal cumulative disturbance, first investigated in [arXiv:2406.18370], to arbitrary finite-dimensional pure

tutorialsarxiv-cs-lg

Learning Rate Scheduling with Matrix Factorization for Private Training

arXiv:2511.17994v2 Announce Type: replace Abstract: We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correl

Learning stochastic multiscale models through normalizing flows

arXiv:2605.09718v1 Announce Type: cross Abstract: Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influe

Learning Strategic Value and Cooperation in Multi-Player Stochastic Games through Side Payments

arXiv:2303.05307v2 Announce Type: replace-cross Abstract: We study general-sum, multi-player stochastic games with transferable utility, motivated by settings where agents can use side payments to mak

researcharxiv-cs-ai

Agents

Learning the Channel Gain from Anywhere to Anywhere via Cross-environment Transformer Estimators

arXiv:2605.08211v1 Announce Type: cross Abstract: Channel-gain maps provide the channel gain between any two locations in a geographical region. They find numerous applications, from resource allocati

agentsarxiv-cs-lg

Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach

arXiv:2605.09964v1 Announce Type: new Abstract: Protein-protein interactions (PPIs) are fundamental to cellular function and disease mechanisms. Current learning-based PPI predictors focus on learning

researcharxiv-cs-ai

Learning the Preferences of a Learning Agent

arXiv:2605.09217v1 Announce Type: new Abstract: For AI systems to be useful to humans, they must understand and act in accordance with our values and preferences. Since specifying preferences is a har

Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity

arXiv:2605.08811v1 Announce Type: cross Abstract: This paper investigates the learning theory of Transformer networks for regression tasks on the compact Euclidean domain [0,1]^d and d-dimensional com

Learning to Align Generative Appearance Priors for Fine-grained Image Retrieval

arXiv:2605.09859v1 Announce Type: new Abstract: Fine-grained image retrieval (FGIR) typically relies on supervision from seen categories to learn discriminative embeddings for retrieving unseen catego

safetyarxiv-cs-cv

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions

arXiv:2605.09448v1 Announce Type: new Abstract: The transition to First-Price Auctions (FPA) in digital advertising has spurred significant research, yet existing work typically assumes access to a va

Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management

arXiv:2605.09818v1 Announce Type: new Abstract: Reinforcement learning (RL) in healthcare has had mixed results, with reward sparsity, unreliable off-policy evaluation, and deployment-simulation gap a

safetyarxiv-cs-lg

Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization

arXiv:2605.08978v1 Announce Type: new Abstract: Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of e

Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques

arXiv:2508.14137v2 Announce Type: replace Abstract: The Macroscopic Fundamental Diagram is a popular tool used to describe traffic dynamics in an aggregated way, with applications ranging from traffic

tutorialsarxiv-cs-lg

Learning to Perceive 'Where': Spatial Pretext Tasks for Robust Self-Supervised Learning

model-releasesarxiv-cs-cv

arXiv:2605.09963v1 Announce Type: new Abstract: Existing self-supervised learning (SSL) methods primarily learn object-invariant representations but often neglect the spatial structure and relationshi

Learning to Sparsify Stochastic Linear Bandits

model-releasesarxiv-cs-lg

arXiv:2605.10151v1 Announce Type: new Abstract: This paper addresses the problem of learning to sparsify stochastic linear bandits, where a decision-maker sequentially selects actions from a high-dime

Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

arXiv:2602.17546v2 Announce Type: replace Abstract: Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and wor

safetyarxiv-cs-cl

Learning Unified Representations of Normalcy for Time Series Anomaly Detection

arXiv:2605.09685v1 Announce Type: cross Abstract: The core challenge in unsupervised anomaly detection is identifying abnormal patterns without prior knowledge of their characteristics. While existing

tutorialsarxiv-cs-ai

Learning When to Jump for Off-road Navigation

arXiv:2602.00877v2 Announce Type: replace Abstract: Low speed does not always guarantee safety in off-road driving. For instance, crossing a ditch may be risky at a low speed due to the risk of gettin

safetyarxiv-cs-ro

Learning When to Stop: Selective Imitation Learning Under Arbitrary Dynamics Shift

arXiv:2605.09183v1 Announce Type: new Abstract: Behavior cloning provides strong imitation learning guarantees when training and test environments share the same dynamics. However, in many deployment

safetyarxiv-cs-lg

Learning When to Trust LLM Priors: A Validated Framework for Semantic Prior Integration

arXiv:2601.21410v3 Announce Type: replace-cross Abstract: Large language models (LLMs) encode rich semantic knowledge that can be useful for supervised learning, but their outputs are unreliable as st

Lecture Notes on Statistical Physics and Neural Networks

arXiv:2605.06394v1 Announce Type: cross Abstract: These lecture notes introduce some topics of classical statistical physics, particularly those that are relevant for neural networks and deep learning

LegalCiteBench: Evaluating Citation Reliability in Legal Language Models

arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent

LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models

arXiv:2512.23025v2 Announce Type: replace-cross Abstract: Multimodal health sensing offers rich behavioral signals for assessing mental health, yet translating these numerical time-series measurements

researcharxiv-cs-ai

Less Diverse, Less Safe: The Indirect But Pervasive Risk of Test-Time Scaling in Large Language Models

arXiv:2510.08592v3 Announce Type: replace-cross Abstract: Test-Time Scaling (TTS) improves LLM reasoning by exploring multiple candidate responses and then operating over this set to find the best out