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Large Spikes in Stochastic Gradient Descent: A Large-Deviations View

arXiv:2603.10079v2 Announce Type: replace Abstract: Large loss spikes in stochastic gradient descent are studied through a rigorous large-deviations analysis for a shallow, fully connected network in

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LARY: A Latent Action Representation Yielding Benchmark for Generalizable Vision-to-Action Alignment

DGX agent

arXiv:2604.11689v1 Announce Type: new Abstract: While the shortage of explicit action data limits Vision-Language-Action (VLA) models, human action videos offer a scalable yet unlabeled data source. A

model-releasesarxiv-cs-cv

14 Apr 2026

LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset

arXiv:2604.10417v1 Announce Type: new Abstract: In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchma

researcharxiv-cs-cl

Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size

arXiv:2604.09909v1 Announce Type: new Abstract: We study last-iterate convergence of SGD with greedy step size over smooth quadratics in the interpolation regime, a setting which captures the classica

LAST: Leveraging Tools as Hints to Enhance Spatial Reasoning for Multimodal Large Language Models

model-releasesarxiv-cs-ai

arXiv:2604.09712v1 Announce Type: cross Abstract: Spatial reasoning is a cornerstone capability for intelligent systems to perceive and interact with the physical world. However, multimodal large lang

Latent attention on masked patches for flow reconstruction

arXiv:2603.02028v2 Announce Type: replace Abstract: Vision transformers have shown outstanding performance in image generation, yet their adoption in fluid dynamics remains limited. We introduce the L

Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs

arXiv:2604.10403v1 Announce Type: new Abstract: We address jailbreaks, backdoors, and unlearning for large language models (LLMs). Unlike prior work, which trains LLMs based on their actions when give

Latent Structure of Affective Representations in Large Language Models

arXiv:2604.07382v2 Announce Type: replace-cross Abstract: The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implica

LayerNorm Induces Recency Bias in Transformer Decoders

arXiv:2509.21042v3 Announce Type: replace Abstract: Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alon

safetyarxiv-cs-cl

Layerwise Dynamics for In-Context Classification in Transformers

arXiv:2604.11613v1 Announce Type: cross Abstract: Transformers can perform in-context classification from a few labeled examples, yet the inference-time algorithm remains opaque. We study multi-class

LDEPrompt: Layer-importance guided Dual Expandable Prompt Pool for Pre-trained Model-based Class-Incremental Learning

arXiv:2604.11091v1 Announce Type: new Abstract: Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-leve

researcharxiv-cs-cv

LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving

arXiv:2512.20563v2 Announce Type: replace-cross Abstract: Simulators can generate virtually unlimited driving data, yet imitation learning policies in simulation still struggle to achieve robust close

LEADER: Learning Reliable Local-to-Global Correspondences for LiDAR Relocalization

model-releasesarxiv-cs-cv

arXiv:2604.11355v1 Announce Type: new Abstract: LiDAR relocalization has attracted increasing attention as it can deliver accurate 6-DoF pose estimation in complex 3D environments. Recent learning-bas

Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation

arXiv:2604.09955v1 Announce Type: new Abstract: Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled sour

applicationsarxiv-cs-cv

Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images

arXiv:2604.10573v1 Announce Type: new Abstract: Robust 3D representation learning forms the perceptual foundation of spatial intelligence, enabling downstream tasks in scene understanding and embodied

researcharxiv-cs-cv

Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness

arXiv:2509.21879v2 Announce Type: replace Abstract: Despite Neural Ordinary Differential Equations (Neural ODEs) exhibiting intrinsic robustness, existing methods often impose Lyapunov stability for f

Learning and Enforcing Context-Sensitive Control for LLMs

arXiv:2604.10667v1 Announce Type: cross Abstract: Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limit

tutorialsarxiv-cs-ai

Learning Discrete Diffusion of Graphs via Free-Energy Gradient Flows

arXiv:2604.11311v1 Announce Type: new Abstract: Diffusion-based models on continuous spaces have seen substantial recent progress through the mathematical framework of gradient flows, leveraging the W

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

arXiv:2604.11365v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain ineffici

tutorialsarxiv-cs-ai

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration

arXiv:2604.10150v1 Announce Type: new Abstract: Generative listwise reranking leverages global context for superior retrieval but is plagued by intrinsic position bias, where models exhibit structural

Learning Geometry and Topology via Multi-Chart Flows

arXiv:2505.24665v2 Announce Type: replace Abstract: Real world data often lie on low-dimensional Riemannian manifolds embedded in high-dimensional spaces. This motivates learning degenerate normalizin

applicationsarxiv-cs-lg

Local Ai

Learning Hierarchical and Geometry-Aware Graph Representations for Text-to-CAD

arXiv:2604.10075v1 Announce Type: new Abstract: Text-to-CAD code generation is a long-horizon task that translates textual instructions into long sequences of interdependent operations. Existing metho

local-aiarxiv-cs-ai

Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification

arXiv:2604.11473v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static

Learning Long-term Motion Embeddings for Efficient Kinematics Generation

arXiv:2604.11737v1 Announce Type: new Abstract: Understanding and predicting motion is a fundamental component of visual intelligence. Although modern video models exhibit strong comprehension of scen

tutorialsarxiv-cs-cv

Learning noisy phase transition dynamics from stochastic partial differential equations

arXiv:2604.09664v1 Announce Type: cross Abstract: The non-equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but ac

researcharxiv-cs-ai

Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

arXiv:2604.10783v1 Announce Type: new Abstract: Designing reward functions remains a central challenge in reinforcement learning (RL) for healthcare, where outcomes are sparse, delayed, and difficult

applicationsarxiv-cs-ai

Learning Racket-Ball Bounce Dynamics Across Diverse Rubbers for Robotic Table Tennis

model-releasesarxiv-cs-ro

arXiv:2604.11349v1 Announce Type: new Abstract: Accurate dynamic models for racket-ball bounces are essential for reliable control in robotic table tennis. Existing models typically assume simple line

Learning Robustness at Test-Time from a Non-Robust Teacher

model-releasesarxiv-cs-cv

arXiv:2604.11590v1 Announce Type: new Abstract: Nowadays, pretrained models are increasingly used as general-purpose backbones and adapted at test-time to downstream environments where target data are

Learning to Adapt: In-Context Learning Beyond Stationarity

model-releasesarxiv-cs-lg

arXiv:2604.10946v1 Announce Type: new Abstract: Transformer models have become foundational across a wide range of scientific and engineering domains due to their strong empirical performance. A key c

Agents

Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning

arXiv:2603.11346v2 Announce Type: replace Abstract: Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking w

agentsarxiv-cs-cv

Agents

Learning to Focus and Precise Cropping: A Reinforcement Learning Framework with Information Gaps and Grounding Loss for MLLMs

arXiv:2603.27494v2 Announce Type: replace-cross Abstract: To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introdu

agentsarxiv-cs-ai

Learning to Focus: CSI-Free Hierarchical MARL for Reconfigurable Reflectors

arXiv:2604.05165v2 Announce Type: replace Abstract: Reconfigurable Intelligent Surfaces (RIS) has a potential to engineer smart radio environments for next-generation millimeter-wave (mmWave) networks

Learning to Forget -- Hierarchical Episodic Memory for Lifelong Robot Deployment

arXiv:2604.11306v1 Announce Type: cross Abstract: Robots must verbalize their past experiences when users ask 'Where did you put my keys?' or 'Why did the task fail?' Yet maintaining life-long episodi

tutorialsarxiv-cs-ai

Learning to Play Piano in the Real World

model-releasesarxiv-cs-ai

arXiv:2503.15481v3 Announce Type: replace-cross Abstract: Towards the grand challenge of achieving human-level manipulation in robots, playing piano is a compelling testbed that requires strategic, pr

Learning to Test: Physics-Informed Representation for Dynamical Instability Detection

arXiv:2604.10967v1 Announce Type: new Abstract: Many safety-critical scientific and engineering systems evolve according to differential-algebraic equations (DAEs), where dynamical behavior is constra

Learning to Unscramble: Simplifying Symbolic Expressions via Self-Supervised Oracle Trajectories

arXiv:2603.11164v2 Announce Type: replace-cross Abstract: We present a new self-supervised machine learning approach for symbolic simplification of complex mathematical expressions. Training data is g

Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video

arXiv:2511.18322v3 Announce Type: replace-cross Abstract: Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumpt

researcharxiv-cs-cv

Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

model-releasesarxiv-cs-lg

arXiv:2604.09787v1 Announce Type: cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a s

Learning When Not to Learn: Risk-Sensitive Abstention in Bandits with Unbounded Rewards

arXiv:2510.14884v3 Announce Type: replace-cross Abstract: In high-stakes AI applications, even a single action can cause irreparable damage. However, nearly all of sequential decision-making theory as

Learning World Models for Interactive Video Generation

model-releasesarxiv-cs-ai

arXiv:2505.21996v3 Announce Type: replace-cross Abstract: Foundational world models must be both interactive and preserve spatiotemporal coherence for effective future planning with action choices. Ho

Lectures on AI for Mathematics

arXiv:2604.11504v1 Announce Type: new Abstract: This book provides a comprehensive and accessible introduction to the emerging field of AI for mathematics. It covers the core principles and diverse ap

researcharxiv-cs-ai

Legal2LogicICL: Improving Generalization in Transforming Legal Cases to Logical Formulas via Diverse Few-Shot Learning

arXiv:2604.11699v1 Announce Type: cross Abstract: This work aims to improve the generalization of logic-based legal reasoning systems by integrating recent advances in NLP with legal-domain adaptive f

LETGAMES: An LLM-Powered Gamified Approach to Cognitive Training for Patients with Cognitive Impairment

arXiv:2604.09566v1 Announce Type: cross Abstract: The application of games as a therapeutic tool for cognitive training is beneficial for patients with cognitive impairments. However, effective game d

researcharxiv-cs-ai

let's go open-source and local models!

model-releasesclem-delangue--x

let's go open-source and local models! Uber's CTO told @LauraBratton5 that AI coding tools—particularly Anthropic’s Claude Code—has already maxed out its 2026 AI budget 📈 “I'm back to the drawing boar

Leveraging Machine Learning Techniques to Investigate Media and Information Literacy Competence in Tackling Disinformation

arXiv:2604.09635v1 Announce Type: cross Abstract: This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among

applicationsarxiv-cs-ai

LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning

model-releasesarxiv-cs-cv

arXiv:2604.10780v1 Announce Type: new Abstract: Three-dimensional (3D) point cloud analysis has become central to applications ranging from autonomous driving and robotics to forestry and ecological m

LIDEA: Human-to-Robot Imitation Learning via Implicit Feature Distillation and Explicit Geometry Alignment

arXiv:2604.10677v1 Announce Type: cross Abstract: Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction dat

safetyarxiv-cs-cv

Hardware

Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge

arXiv:2509.08193v2 Announce Type: replace-cross Abstract: We present FlexiFlow, a lifetime-aware design framework for item-level intelligence (ILI) where computation is integrated directly into dispos

hardwarearxiv-cs-ai