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arXiv:2606.01596v1 Announce Type: cross Abstract: Learning chaotic dynamical systems from data requires more than short-term predictive accuracy: the learned model must preserve the attractor geometry

local-aiarxiv-cs-lg

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Learning Discriminative and Generalizable Anomaly Detector for Dynamic Graph with Limited Supervision

DGX agent

arXiv:2602.20019v2 Announce Type: replace-cross Abstract: Dynamic graph anomaly detection is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies.

applicationsarxiv-cs-ai

2 Jun 2026

Learning from Saturated Data: Signals Beyond Correctness for LLM Training

arXiv:2606.01436v1 Announce Type: new Abstract: The growing capabilities of large language models (LLMs) have led to the saturation of many benchmarks and training datasets used to improve them. Motiv

researcharxiv-cs-cl

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

arXiv:2509.24627v2 Announce Type: replace Abstract: Embedding physical intuition into network architectures allows the learning of dynamics that enforce fundamental properties, such as energy conserva

Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulation

arXiv:2606.01833v1 Announce Type: cross Abstract: Generative emulators of protein dynamics produce plausible trajectories at a fraction of the cost of molecular dynamics, but they inherit their traini

Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model

arXiv:2606.01698v1 Announce Type: new Abstract: Deep learning has revolutionized medical image analysis, delivering exceptional diagnostic accuracy across diverse applications. Yet, the lack of interp

researcharxiv-cs-cv

Learning Multi-Modal Trajectory Policies for Data-Efficient Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2606.01047v1 Announce Type: new Abstract: Robotic manipulation requires the effective integration of heterogeneous inputs, including visual observations, language instructions, and trajectory re

Learning Neural Deformation Representation for 4D Dynamic Shape Generation

arXiv:2606.01021v1 Announce Type: new Abstract: Recent developments in 3D shape representation opened new possibilities for generating detailed 3D shapes. Despite these advances, there are few studies

researcharxiv-cs-cv

Agents

Learning Query-Specific Rubrics from Human Preferences for DeepResearch Report Generation

arXiv:2602.03619v2 Announce Type: replace Abstract: Nowadays, developing reliable DeepResearch-style long-form report generation remains challenging, as training and evaluation lack verifiable reward

agentsarxiv-cs-cl

Agents

Learning to Construct Practical Agentic Systems

arXiv:2606.00189v1 Announce Type: cross Abstract: Automated design and optimization of agentic LLM-based systems leads to sophisticated systems that substantially improve result quality over off-the-s

agentsarxiv-cs-ai

Agents

Learning-To-Measure: In-Context Active Feature Acquisition

arXiv:2510.12624v2 Announce Type: replace-cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by

agentsarxiv-cs-ai

Learning to Reduce Search Space for Generalizable Neural Routing Solver

arXiv:2503.03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to

Learning to Remember, Learn, and Forget in Attention-Based Models

arXiv:2602.09075v3 Announce Type: replace-cross Abstract: In-Context Learning (ICL) in transformers acts as an online associative memory and is believed to underpin their high performance on complex s

Learning to Retrieve: Dual-Level Long-Term Memory for Text-to-SQL Agents

model-releasesarxiv-cs-cl

arXiv:2606.00547v1 Announce Type: new Abstract: Interactive text-to-SQL agents solve database tasks through multi-turn interactions involving schema exploration, query execution, feedback interpretati

Learning To Sample From Diffusion Models Via Inverse Reinforcement Learning

arXiv:2602.08689v2 Announce Type: replace Abstract: Diffusion models generate samples through an iterative denoising process guided by a pretrained neural network. Once the denoiser is fixed, the samp

safetyarxiv-cs-lg

Learning to Trim: End-to-End Causal Graph Pruning with Dynamic Anatomical Feature Banks for Medical VQA

arXiv:2603.26028v2 Announce Type: replace Abstract: Medical Visual Question Answering (MedVQA) models often exhibit limited generalization due to reliance on dataset-specific correlations, such as rec

researcharxiv-cs-cv

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning

arXiv:2606.02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning. Existing appr

safetyarxiv-cs-ai

Learning When to Translate for Multilingual Reasoning

arXiv:2606.02465v1 Announce Type: cross Abstract: Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, but still exhibit substantial multilingual reasoning gaps, lar

LEGS: Fine-Tuning Teleop-Free VLAs for Humanoid Loco-manipulation in an Embodied Gaussian Splatting World

arXiv:2606.01458v1 Announce Type: new Abstract: Training vision-language-action (VLA) policies for humanoid loco-manipulation is constrained by the high cost and complexity of collecting human teleope

safetyarxiv-cs-ro

Length Generalization Bounds for Transformers

arXiv:2603.02238v2 Announce Type: replace Abstract: Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite tr

LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

arXiv:2602.18195v2 Announce Type: replace-cross Abstract: Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-base

Lessons from the Trenches on Reproducible Evaluation of Language Models

arXiv:2405.14782v3 Announce Type: replace Abstract: Reliable evaluation of language models (LMs) remains an open challenge. Re- searchers and engineers face methodological issues such as the sensitivi

researcharxiv-cs-cl

Leyline: KV Cache Directives for Agentic Inference

arXiv:2606.01065v1 Announce Type: cross Abstract: Modern KV cache management assumes the chatbot workload: prompts arrive once and the cache grows append-only, so prefix caching and forward-only evict

safetyarxiv-cs-ai

LFA: Layer Feature Attention for Run-Time Introspection of 2D Object Detectors in Automated Driving

arXiv:2606.00372v1 Announce Type: new Abstract: Reliable object detection is critical for automated driving, yet even state-of-the-art detectors inevitably make errors that can compromise safety. Intr

safetyarxiv-cs-cv

Limits of Resolution Equivariance in Fourier Neural Operators

arXiv:2606.00677v1 Announce Type: new Abstract: Fourier Neural Operators are often assumed to generalize across spatial resolutions, enabling training on a coarse grid and deployment on a finer grid.

Limits of Spatial Imagery Reasoning in Frontier LLM Models

arXiv:2603.26779v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, yet they struggle with spatial tasks that require mental sim

Linear Motility Maps in Nonlinear Viscous Fluids

arXiv:2606.00063v1 Announce Type: new Abstract: Systems moving in low Reynolds number fluid regimes are known to be governed by a ``motility map'' which linearly relates their shape change rates to th

researcharxiv-cs-ro

Applications

Linear Strategic Classification with Endogenous Improvements

arXiv:2606.01198v1 Announce Type: new Abstract: Strategic classification studies settings in which agents respond to a deployed classifier by modifying observable features at a cost. Classical models

applicationsarxiv-cs-lg

Linguistics-Aware Non-Distortionary LLM Watermarking

arXiv:2606.00613v1 Announce Type: cross Abstract: Watermarking should identify language-model output without degrading quality or limiting verification to the model provider. Multilingual deployment m

LinguIUTics at PsyDefDetect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8B for Psychological Defense Mechanism Classification

arXiv:2606.00647v1 Announce Type: cross Abstract: Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (n

safetyarxiv-cs-ai

Literary Emotions in Motion: A Soft Robotics Installation for Tactile Storytelling

arXiv:2606.00418v1 Announce Type: new Abstract: Soft robotics is increasingly explored in artistic contexts, where tactile interaction provides audiences with embodied engagement beyond visual or audi

researcharxiv-cs-ro

Hardware

LithoGRPO: Fast Inverse Lithography via GRPO Reinforced Flow Matching

arXiv:2606.00228v1 Announce Type: new Abstract: In semiconductor manufacturing, lithography projects circuit layouts onto silicon wafers through an optical mask. As circuit features shrink below the w

hardwarearxiv-cs-lg

LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding

arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate t

researcharxiv-cs-cl

LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models

model-releasesarxiv-cs-cv

arXiv:2606.02535v1 Announce Type: new Abstract: Large-scale generative models have demonstrated remarkable capabilities across image generation and editing tasks. However, their performance in low-lev

LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation

arXiv:2603.09403v2 Announce Type: replace Abstract: Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English

safetyarxiv-cs-cl

LLM Consortium for Software Design Refinement: A Controlled Experiment on Multi-Agent Collaboration Topologies

arXiv:2606.01490v1 Announce Type: cross Abstract: We present a controlled experiment evaluating 12 multi-agent LLM collaboration topologies for software architecture design. Using a 2imes2imes2 factor

Local Ai

LLM-Driven Co-Evolutionary Automated Heuristic Design for Bi-Component Coupled Combinatorial Optimization

arXiv:2606.00718v1 Announce Type: new Abstract: While Large Language Models (LLMs) have recently shown promise in Automated Heuristic Design (AHD), existing methods typically generate and evolve heuri

local-aiarxiv-cs-ai

LLM-Evolved Pattern Generators for Optimal Classical Planning

arXiv:2606.02438v1 Announce Type: new Abstract: Learned heuristics have recently become a competitive alternative to traditional domain-independent heuristics for satisficing planning. Existing approa

LLM Trainer: Automated Robotic Data Generation via Demonstration Augmentation using LLMs

arXiv:2509.20070v2 Announce Type: replace Abstract: We present LLM Trainer, a fully automated pipeline that leverages the world knowledge of Large Language Models (LLMs) to transform a small number of

safetyarxiv-cs-ro

LLM-WikiRace Benchmark: How Far Can LLMs Plan over Real-World Knowledge Graphs?

arXiv:2602.16902v4 Announce Type: replace Abstract: We introduce LLM-Wikirace, a benchmark for evaluating planning, reasoning, and world knowledge in large language models (LLMs). In LLM-Wikirace, mod

LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation

arXiv:2602.16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making

LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

arXiv:2606.00031v1 Announce Type: cross Abstract: Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support ea

LLMs Need Encoders for Semantic IDs Too

arXiv:2606.00324v1 Announce Type: cross Abstract: Multimodal LLMs use dedicated encoders to bridge non-language modalities (vision encoders for images, depth models for audio codec tokens) because raw

Applications

LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models

arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable

applicationsarxiv-cs-lg

lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation

arXiv:2606.00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because 'funny' is audience-dependent and supervision i

Local Diagnostics of Continuous Normalizing Flow for Out-of-Distribution Detection

model-releasesarxiv-cs-cl

arXiv:2606.00684v1 Announce Type: cross Abstract: We address the problem of out-of-distribution (OOD) detection for target observations embedded in a subspace of the high dimensional data space. Using

Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning

arXiv:2606.01128v1 Announce Type: new Abstract: Communication overhead is a crucial bottleneck in scalable distributed learning. While existing methods aim to efficiently utilize data points, such as

Local Preferential Bayesian Optimization

model-releasesarxiv-cs-lg

arXiv:2606.02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit obje