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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

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

DGX agent

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

14 Apr 2026

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

Tutorials

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

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

safetyarxiv-cs-lg

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

safetyarxiv-cs-lg

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

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

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

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

Applications

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

LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning

model-releasesarxiv-cs-cl

arXiv:2502.14644v5 Announce Type: replace Abstract: Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine

Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net

arXiv:2604.11071v1 Announce Type: cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fe

Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit

arXiv:2604.09998v1 Announce Type: cross Abstract: Large language models (LLMs) have recently emerged as promising tools for augmenting Security Operations Center (SOC) workflows, with vendors increasi

Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output

arXiv:2604.11524v1 Announce Type: new Abstract: Surrogates provide a cheap solution evaluation and offer significant leverage for optimizing computationally expensive problems. Usually, surrogates onl

local-aiarxiv-cs-ai

Linear Attention Based Deep Nonlocal Means Filtering for Multiplicative Noise Removal

arXiv:2407.05087v2 Announce Type: replace-cross Abstract: Multiplicative noise widely exists in radar images, medical images and other important fields' images. Compared to normal noises, multiplicati

Linear Programming for Multi-Criteria Assessment with Cardinal and Ordinal Data: A Pessimistic Virtual Gap Analysis

arXiv:2604.09555v1 Announce Type: new Abstract: Multi-criteria Analysis (MCA) is used to rank alternatives based on various criteria. Key MCA methods, such as Multiple Criteria Decision Making (MCDM)

LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops

arXiv:2506.14493v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can ex

researcharxiv-cs-cl

Linguistic Accommodation Between Neurodivergent Communities on Reddit:A Communication Accommodation Theory Analysis of ADHD and Autism Groups

arXiv:2604.10063v1 Announce Type: new Abstract: Social media research on mental health has focused predominantly on detecting and diagnosing conditions at the individual level. In this work, we shift

researcharxiv-cs-cl

Tutorials

LitPivot: Developing Well-Situated Research Ideas Through Dynamic Contextualization and Critique within the Literature Landscape

arXiv:2604.02600v2 Announce Type: replace-cross Abstract: Developing a novel research idea is hard. It must be distinct enough from prior work to claim a contribution while also building on it. This r

tutorialsarxiv-cs-ai

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs

arXiv:2511.14774v3 Announce Type: replace-cross Abstract: Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either

LiveGesture Streamable Co-Speech Gesture Generation Model

arXiv:2604.10927v1 Announce Type: new Abstract: We propose LiveGesture, the first fully streamable, speech-driven full-body gesture generation framework that operates with zero look-ahead and supports

LLM-as-Judge on a Budget

arXiv:2602.15481v2 Announce Type: replace Abstract: LLM-as-a-judge has emerged as a cornerstone technique for evaluating large language models by leveraging LLM reasoning to score prompt-response pair

safetyarxiv-cs-lg

LLM-based Realistic Safety-Critical Driving Video Generation

arXiv:2507.01264v2 Announce Type: replace-cross Abstract: Designing diverse and safety-critical driving scenarios is essential for evaluating autonomous driving systems. In this paper, we propose a no

LLM Nepotism in Organizational Governance

arXiv:2604.09620v1 Announce Type: cross Abstract: Large language models are increasingly used to support organizational decisions from hiring to governance, raising fairness concerns in AI-assisted ev

LLMs for Game Theory: Entropy-Guided In-Context Learning and Adaptive CoT Reasoning

arXiv:2601.10775v2 Announce Type: replace Abstract: We propose a novel LLM-based framework for reasoning in discrete, game-theoretic tasks, illustrated with Tic-Tac-Toe. The method integrates in-conte

researcharxiv-cs-cl

LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments

arXiv:2604.10834v1 Announce Type: cross Abstract: [Background:] Thematic analysis of free-text justifications in human experiments provides significant qualitative insights. Yet, it is costly because

LLMs for Text-Based Exploration and Navigation Under Partial Observability

arXiv:2604.09604v1 Announce Type: new Abstract: Exploration and goal-directed navigation in unknown layouts are central to inspection, logistics, and search-and-rescue. We ask whether large language m

LLMs Should Incorporate Explicit Mechanisms for Human Empathy

arXiv:2604.10557v1 Announce Type: cross Abstract: This paper argues that Large Language Models (LLMs) should incorporate explicit mechanisms for human empathy. As LLMs become increasingly deployed in

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation

arXiv:2604.11789v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks req

Locket: Robust Feature-Locking Technique for Language Models

arXiv:2510.12117v3 Announce Type: replace-cross Abstract: Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for

researcharxiv-cs-lg

LoDAdaC: a unified local training-based decentralized framework with adaptive gradients and compressed communication

arXiv:2604.09970v1 Announce Type: new Abstract: In the decentralized distributed learning, achieving fast convergence and low communication cost is essential for scalability and high efficiency. Adapt

local-aiarxiv-cs-lg

LogitDynamics: Reliable ViT Error Detection from Layerwise Logit Trajectories

arXiv:2604.10643v1 Announce Type: new Abstract: Reliable confidence estimation is critical when deploying vision models. We study error prediction: determining whether an image classifier's output is

LoGo-MR: Screening Breast MRI for Cancer Risk Prediction by Efficient Omni-Slice Modeling

model-releasesarxiv-cs-cv

arXiv:2604.11348v1 Announce Type: new Abstract: Efficient and explainable breast cancer (BC) risk prediction is critical for large-scale population-based screening. Breast MRI provides functional info

Agents

LOLGORITHM: Funny Comment Generation Agent For Short Videos

arXiv:2604.09729v1 Announce Type: cross Abstract: Short-form video platforms have become central to multimedia information dissemination, where comments play a critical role in driving engagement, pro

agentsarxiv-cs-ai

Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation

arXiv:2604.10103v1 Announce Type: new Abstract: Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window att

local-aiarxiv-cs-cv

Look Twice before You Leap: A Rational Framework for Localized Adversarial Anonymization

arXiv:2512.06713v3 Announce Type: replace-cross Abstract: Current LLM-based frameworks for text anonymization usually rely on remote API services from powerful LLMs, which creates an inherent privacy

local-aiarxiv-cs-cl

LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2601.14706v3 Announce Type: replace Abstract: In this paper, we present LookBench (We use the term 'look' to reflect retrieval that mirrors how people shop -- finding the exact item, a close sub

LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers

arXiv:2504.14386v2 Announce Type: replace-cross Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permut

LoopGuard: Breaking Self-Reinforcing Attention Loops via Dynamic KV Cache Intervention

arXiv:2604.10044v1 Announce Type: new Abstract: Through systematic experiments on long-context generation, we observe a damaging failure mode in which decoding can collapse into persistent repetition