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LAMP: Lean-based Agentic framework with MCP and Proof Repair

arXiv:2606.28841v1 Announce Type: cross Abstract: Large language models are increasingly capable of mathematical reasoning, but the proofs they generate are often unreliable and hard to verify. Intera

agentsarxiv-cs-ai

Tutorials

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LAMP: Long-Horizon Adaptive Manipulation Planning for Multi-Robot Collaboration in Cluttered Space

DGX agent

arXiv:2606.29358v1 Announce Type: new Abstract: Multi-robot manipulation requires jointly reasoning about contact formations, robot motions under coupled dynamics, and collision avoidance. Systematica

tutorialsarxiv-cs-ro

30 Jun 2026

Langshaw: Declarative Interaction Protocols Based on Sayso and Conflict

arXiv:2606.29601v1 Announce Type: cross Abstract: Current languages for specifying multiagent protocols either over-constrain protocol enactments or complicate capturing their meanings. We propose Lan

Large and Deep Factor Models

arXiv:2402.06635v3 Announce Type: replace-cross Abstract: We show that a deep neural network (DNN) trained to construct a stochastic discount factor (SDF) admits an additive decomposition separating n

Latent Actions from Factorized Transition Effects under Agent Ambiguity

arXiv:2606.30544v1 Announce Type: new Abstract: Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual eff

Latent Bridges for Multi-Table Question Answering

arXiv:2606.28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, e

Applications

Latent-CURE for Breast Cancer Diagnosis

arXiv:2606.29928v1 Announce Type: cross Abstract: Multimodal Large Models have significantly advanced automated breast ultrasound diagnosis. However, most existing frameworks utilize opaque, end-to-en

applicationsarxiv-cs-ai

Latent Noise Mask for Reducing Visual Redundancy in Multimodal Large Language Models

arXiv:2606.30168v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often fail in fine-grained visual reasoning, as question-relevant visual cues are diluted by dense and redundan

LatentRevise: Learning from Zero-Hit Reasoning

arXiv:2606.29938v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by hard prompts on which correct trajectories have low probability, so sampling mi

safetyarxiv-cs-cl

Lateral String Stability for Vehicle Platoons

arXiv:2606.29677v1 Announce Type: new Abstract: Connected and automated vehicle (CAV) platooning promises gains in energy efficiency and traffic throughput and, most critically, in safety. These safet

safetyarxiv-cs-ro

LatSearch: Latent Reward-Guided Search for Faster Inference-Time Scaling in Video Diffusion

arXiv:2603.14526v2 Announce Type: replace Abstract: The recent success of inference-time scaling in large language models has inspired similar explorations in video diffusion. In particular, motivated

LaVPR: Benchmarking Language and Vision for Place Recognition

arXiv:2602.03253v2 Announce Type: replace Abstract: Visual Place Recognition (VPR) often fails under extreme environmental changes and perceptual aliasing. Beyond these limitations, standard systems c

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

arXiv:2606.29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE),

LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection

arXiv:2512.05663v3 Announce Type: replace Abstract: Real-time monocular 3D object detection remains challenging due to severe depth ambiguity, viewpoint shifts, and the high computational cost of 3D r

Leader Reward for POMO-Based Neural Combinatorial Optimization

arXiv:2405.13947v2 Announce Type: replace Abstract: Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown

safetyarxiv-cs-lg

Learned Coordination Conventions in Cooperative MARL: Measuring the Translation Gap Between Theory-Informed Roles and Learned Routing

arXiv:2606.29541v1 Announce Type: new Abstract: Role-semantic assignments provide priors over how heterogeneous agents may coordinate, but cooperative MARL systems instead settle on conventions throug

Tutorials

Learned iterative networks: An operator learning perspective

arXiv:2512.08444v2 Announce Type: replace-cross Abstract: Learned image reconstruction has become a pillar in computational imaging and inverse problems. Among the most successful approaches are learn

tutorialsarxiv-cs-lg

Learning as Observable Matrix Dynamics: Diffusive Relaxations versus Phase Transitions

arXiv:2606.29679v1 Announce Type: new Abstract: Observable Matrix Dynamics (OMD) is a diagnostic framework that probes the dynamics of high-dimensional internal representations of inputs by a neural n

Learning Cross-view Correspondences for Geo-localization on Planetary Surfaces

arXiv:2606.29821v1 Announce Type: new Abstract: Maintaining global position awareness is a fundamental challenge for planetary surface exploration, since satellite-based positioning systems are unavai

Learning Deterministic Finite-State Machines from the Prefixes of a Single String is NP-Complete

arXiv:2601.12621v2 Announce Type: replace-cross Abstract: It is well known that computing a minimum deterministic finite automaton consistent with a given set of positive and negative examples is NP-h

Learning Efficient 4D Gaussian Representations from Monocular Videos with Flow Splatting

arXiv:2606.29976v1 Announce Type: new Abstract: Reconstructing dynamic 3D scenes from monocular videos is challenging due to scene complexity and temporal dynamics. With the advancement of 3D Gaussian

Learning from Acquisition: Metadata-driven Multimodal Pre-training for Cardiac MRI

arXiv:2606.28991v1 Announce Type: new Abstract: Cardiac magnetic resonance imaging (CMR) routinely records structured acquisition metadata, yet most CMR foundation models rely primarily on image-only

Learning from Mistakes: Rollout-Retrieval Lifelong Policy Learning for Autonomous Driving

arXiv:2606.30537v1 Announce Type: cross Abstract: Autonomous driving policies should be able to improve continually as deployment exposes them to increasingly diverse and long-tail traffic situations.

Learning from Reliable Latent Prompts for Visual Recognition with Missing Modalities

arXiv:2606.30597v1 Announce Type: new Abstract: Large-scale multimodal models (LMMs) have achieved superior performance in visual recognition by synergizing information across diverse, massive-scale p

Learning from samples: inverse problems over measures

model-releasesarxiv-cs-lg

arXiv:2505.07124v3 Announce Type: replace Abstract: We study inverse problems where an unknown potential is observed only through samples from the measure it induces by a convex variational principle.

Tutorials

Learning How to Use Tools, Not Just When: Pattern-Aware Tool-Integrated Reasoning

arXiv:2509.23292v4 Announce Type: replace Abstract: Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly stu

tutorialsarxiv-cs-ai

Learning the structure of open quantum systems

arXiv:2606.30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an n-qubit constant-local Lindbladian to arepsilon error with O(g d^2 log(n) / arepsilon^2) to

Learning to Adaptively Allocate Gaussians for Arbitrary-Scale Image Super-Resolution

arXiv:2606.29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled. This occurs when engines upscale frames, users zoom into 3D scenes,

Learning to Bid in Discriminatory Auctions with Budget Constraints

model-releasesarxiv-cs-lg

arXiv:2606.29252v1 Announce Type: new Abstract: We study repeated bidding in multi-unit discriminatory (pay-as-bid) auctions for a single bidder with per-round utility equal to value minus alpha times

Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter

arXiv:2606.28441v1 Announce Type: cross Abstract: Online latent state estimation constitutes a fundamental challenge within the artificial intelligence field, serving as a foundational tool for divers

Learning to Route and Schedule LLMs from User Retrials via Contextual Queueing Bandits

model-releasesarxiv-cs-lg

arXiv:2602.02061v2 Announce Type: replace Abstract: Explosive demands for LLMs often cause user queries to accumulate in server queues, requiring efficient routing (query-LLM matching) and scheduling

Learning to Segment Liquids in Real-world Images

arXiv:2601.00940v2 Announce Type: replace Abstract: Liquids like water, wine and medicine are everywhere. However, limited attention has been given to the task of segmenting liquids, hindering the abi

Learning Transferable Dynamics Priors from Action to World Modeling

arXiv:2606.29501v1 Announce Type: new Abstract: We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for robot learning. By pretraining a model to predict

safetyarxiv-cs-ro

Local Ai

Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection

arXiv:2606.29498v1 Announce Type: new Abstract: Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neg

local-aiarxiv-cs-cv

LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval

arXiv:2606.28379v1 Announce Type: cross Abstract: We introduce LEDGER to tackle the novel context engineering challenge of agentic document editing, where localized edits to long, structured documents

Applications

Legal Domain Adaptation of Modern BERT Models

arXiv:2606.28538v1 Announce Type: new Abstract: We investigate domain adaptation of modern BERT models in the legal domain. We further pre-train ModernBERT on all US court opinions using the masked la

applicationsarxiv-cs-cl

Legible Shared Autonomy: Implicit Communication of Robot Belief through Motion

arXiv:2606.29846v1 Announce Type: new Abstract: Shared autonomy systems combine user input with autonomous assistance to help users with motor impairments control robot arms to perform everyday manipu

agentsarxiv-cs-ro

LEIQ-Assessor: Multi-dimensional Quality Assessment of Low-light Enhanced Images via Multi-task Learning

arXiv:2606.29752v1 Announce Type: new Abstract: Low-light image enhancement algorithms (LIEAs) aim to improve the visibility of images captured under poor illumination. However, the enhancement proces

LETT-NeXt: A Lightweight RECIST-Guided Model for 3D CT Lesion Segmentation

arXiv:2606.30108v1 Announce Type: new Abstract: RECIST diameter measurements are widely used for tumor response assessment, but they provide only a limited 2D description of lesion extent. We present

LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training

arXiv:2606.30642v1 Announce Type: cross Abstract: Full-length song generation must preserve coherence and musicality, render detailed vocal and accompaniment acoustics, and follow lyrics and prompts.

Local Ai

Lie Group Diffusion Models for Hardware-Aware Quantum Circuit Synthesis

arXiv:2606.29636v1 Announce Type: cross Abstract: An important task in quantum computing is unitary circuit synthesis compatible with physical hardware constraints. This problem has a natural hybrid s

local-aiarxiv-cs-lg

Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing

arXiv:2606.30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to exe

agentsarxiv-cs-ai

Liquidity-Based Audit of Algorithmic Trading Strategies

arXiv:2606.29018v1 Announce Type: cross Abstract: We show that net demand for liquidity by algo strategies is identifiable from its trade and price history alone, with no knowledge of its signal or op

Little Brains, Big Feats: Exploring Compact Language Models

arXiv:2606.30062v1 Announce Type: cross Abstract: While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains;

LLM Agents Are Latent Context Managers: Eliciting Self-Managed Context via a Proprioceptive Dashboard

model-releasesarxiv-cs-cl

arXiv:2606.30005v1 Announce Type: new Abstract: Long-horizon tool agents are bottlenecked by how their context grows toward the limits of the context window. Recent systems make context management age

LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity

arXiv:2606.28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial

agentsarxiv-cs-ai

LLM-based Multimodal Personality Recognition via Facial Action Unit-Text Semantic Fusion

arXiv:2606.29900v1 Announce Type: cross Abstract: Personality recognition in asynchronous video interviews (AVIs) has become increasingly important due to their widespread adoption in modern recruitme

LLM-Guided Planning for Multi-hop Reasoning over Multimodal Nuclear Regulatory Documents

arXiv:2606.29399v1 Announce Type: new Abstract: Reviewing nuclear regulatory documents requires multi-hop reasoning across tens of thousands of pages, where judgments depend on evidence assembled acro