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Industrial Dexterity Benchmark: A Hardware-Software Benchmarking Platform for Industrial Dexterous Manipulation

model-releasesarxiv-cs-ro

arXiv:2607.14021v1 Announce Type: new Abstract: Dexterous manipulation remains a critical bottleneck in industrial automation; tasks such as cable routing, connector insertion, and precision assembly

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Inference Economics of Enterprise Coding Agents: A Case Study of Cloud vs. On-Premise LLMs

DGX agent

arXiv:2607.13080v1 Announce Type: cross Abstract: Autonomous coding agents force engineering organizations to choose between API-based frontier models -- strong reasoning at high token cost -- and on-

model-releasesarxiv-cs-ai

16 Jul 2026

Interaction Protocol Shapes Moral Judgment in Multi-Agent Debate

arXiv:2510.10002v3 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in sensitive everyday contexts -- offering personal advice, mental health support, and mor

Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution

arXiv:2607.13069v1 Announce Type: new Abstract: Large language models produce chain-of-thought (CoT) reasoning that appears logically sound yet may not genuinely depend on its stated premises. We intr

Introducing Human-Centeredness in AI-Assisted Lexicography

arXiv:2607.11808v2 Announce Type: replace-cross Abstract: This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography. While generative AI offers signifi

Inverse-LLaVA: Rethinking Multimodal Alignment via Text-to-Vision Mapping

arXiv:2508.12466v2 Announce Type: replace-cross Abstract: Traditional multimodal learning approaches rely on alignment pre-training to bridge vision and language modalities, typically by projecting vi

Is the Statistical Advantage Worth the Cost? An Empirical Comparison of KANs and MLPs for Structured Data Classification

arXiv:2607.13413v1 Announce Type: cross Abstract: This study presents an empirical benchmarking comparison between Kolmogorov-Arnold Networks (KANs) and Multi-Layer Perceptrons (MLPs) on structured ta

Joint On-and-Off Policy Learning for Vision-and-Language Navigation

arXiv:2607.13461v1 Announce Type: new Abstract: Vision-and-Language Navigation (VLN) necessitates an embodied agent to navigate in the physical world by adhering to natural language instructions. Rece

safetyarxiv-cs-ro

Just-In-Time Scene Graph Growth: Combating Perceptual Saturation in Long-Horizon Robotics

arXiv:2607.13245v1 Announce Type: new Abstract: While 3D Scene Graphs (3DSGs) provide crucial structured representations for embodied agents, conventional Ahead-of-Time, build-everything-then-filter p

Kaleido: Algorithm-Hardware Co-Design for Video Diffusion Transformers by Exploiting Latent Space Correlations

arXiv:2607.13770v1 Announce Type: cross Abstract: Video diffusion transformers (vDiTs) generate high quality video but introduce extremely high compute cost due to the long diffusion timesteps and sel

Kepler-Encoder-v0.1: Towards a Multimodal Embedding Model for Robots

arXiv:2607.13522v1 Announce Type: cross Abstract: A robot must understand the state of its own body, but a camera sees only part of it. Force and contact leave almost no trace in a single frame, and r

Koopman-driven grip force prediction through EMG sensing

arXiv:2409.17340v2 Announce Type: replace-cross Abstract: Loss of hand function due to conditions like stroke or multiple sclerosis significantly impacts daily activities. Robotic rehabilitation provi

Lag Operator SSMs: A Geometric Framework for Structured State Space Modeling

arXiv:2512.18965v2 Announce Type: replace Abstract: Structured State Space Models (SSMs), which are at the heart of the recently popular Mamba architecture, are powerful tools for sequence modeling. H

LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck

arXiv:2606.13061v2 Announce Type: replace Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. De

LAPO: Leave-One-Turn Attribution for Self-Generated Process Rewards in Multi-Turn Search Reasoning

arXiv:2607.13501v1 Announce Type: new Abstract: Reinforcement learning for multi-turn search reasoning typically relies on terminal outcome rewards, which cannot distinguish useful, redundant, and har

Layered Risk Mapping for Autonomous Patient Transport in Expeditionary Medical Facilities

arXiv:2607.13497v1 Announce Type: new Abstract: In expeditionary medical facilities, routine patient transport imposes a compounding burden of personal protective equipment consumption, staff diversio

safetyarxiv-cs-ro

Learned Pairwise Deep Dual-Optimal Inequalities for Stabilizing Column Generation

arXiv:2607.13373v1 Announce Type: cross Abstract: Column generation (CG) is central to many large-scale optimization algorithms, including branch-price-and-cut methods for vehicle routing problems, bu

Agents

Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System

arXiv:2607.13370v1 Announce Type: cross Abstract: This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tu

agentsarxiv-cs-ai

Tutorials

Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks

arXiv:2607.13882v1 Announce Type: new Abstract: Learning from demonstration (LfD) enables robots to learn manipulation skills directly from expert demonstrations but remains challenging for contact-ri

tutorialsarxiv-cs-ro

Learning Physics-Guided Residual Dynamics for Deformable Object Simulation

arXiv:2607.13451v1 Announce Type: cross Abstract: Simulating deformable objects is essential for a wide range of robotic manipulation applications, yet accurately predicting their dynamics remains cha

applicationsarxiv-cs-ai

Learning Red Agent Policy from Observations for Neurosymbolic Autonomous Cyber Agents

arXiv:2606.18223v2 Announce Type: replace-cross Abstract: With sophisticated cyber-attacks becoming increasingly prevalent, modern networks require intelligent autonomous cyber-defense agents trained

Learning Robust Execution in Robotic Manipulation with Agentic Reinforcement Learning

model-releasesarxiv-cs-ro

arXiv:2607.13818v1 Announce Type: new Abstract: Robotic manipulation poses fundamental challenges due to uncertainty, long-horizon execution, and compounding errors, which can easily destabilize execu

Learning Safe Agent Behaviour from Human Preferences and Justifications via World Models

arXiv:2607.13172v1 Announce Type: new Abstract: We address the problem of safely training an agent policy and deploying a good and safe policy, in settings where the environment dynamics are unknown a

Learning Speaker Identity Beyond Language and Modality Constraints: Insights from the POLY-SIM 2026 Challenge

arXiv:2607.13669v1 Announce Type: new Abstract: Multimodal speaker identification systems typically assume the availability of complete and homogeneous audio-visual modalities during both training and

applicationsarxiv-cs-cv

Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies

arXiv:2604.00830v3 Announce Type: replace-cross Abstract: Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at

Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence

arXiv:2602.12811v2 Announce Type: replace-cross Abstract: When humans and large language models (LLMs) process the same text, activations in the LLMs correlate with brain activity measured, e.g., with

LessonBench-V1: A Benchmark Dataset for Evaluating AI Lesson Generation Agents

arXiv:2607.13041v1 Announce Type: cross Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to s

Leveraging Differentiable PDE Solvers for Semi-Neural Spatial Reconstruction From Sparse Measurements

arXiv:2601.20496v2 Announce Type: replace-cross Abstract: Generating dense physical fields from sparse measurements is a fundamental question in sampling, signal processing, and many other application

Leveraging unlabelled data for generalizable neural population decoding

arXiv:2607.14086v1 Announce Type: new Abstract: Robust and accurate neural decoders are integral to neurotechnologies such as brain-computer interfaces and closed-loop experiments. Recent work has sho

Lighthouse RL: Sample-Efficient Circuit Optimization via Strategic Reset Points

model-releasesarxiv-cs-lg

arXiv:2607.14008v1 Announce Type: new Abstract: In this paper, we introduce Lighthouse RL, a sample-efficient reinforcement learning (RL) approach for analog circuit sizing. Traditional methods lack g

Linear Independent Component Analysis via Optimal Transport

arXiv:2607.14081v1 Announce Type: new Abstract: Linear Independent Component Analysis (ICA) recovers jointly independent source signals from their linear mixtures. To achieve this, classical ICA algor

LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement

arXiv:2603.13952v3 Announce Type: replace-cross Abstract: In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared

Local Ai

Local Redundancy: An Information-Theoretic Measure of Plasticity from Synthetic Memorization

arXiv:2607.13432v1 Announce Type: new Abstract: Plasticity -- a neural network's ability to adapt to new tasks -- is critical for continual and transfer learning. Existing measures, such as effective

local-aiarxiv-cs-lg

Look Again Before You Abstain:Budgeted Conformal Evidence Acquisition for Reliable Vision-Language Model

model-releasesarxiv-cs-cv

arXiv:2606.16667v2 Announce Type: replace Abstract: Large vision-language models (LVLMs) hallucinate: they assert visual details that the image does not support. A principled remedy is selective predi

Local Ai

Low-latency Event-based Object Detection with Spatially-Sparse Linear Attention

arXiv:2603.06228v2 Announce Type: replace Abstract: Event cameras provide sequential visual data with spatial sparsity and high temporal resolution, making them attractive for low-latency object detec

local-aiarxiv-cs-cv

LPM: Industrial-Scale Generative Video Restoration

arXiv:2607.13460v1 Announce Type: new Abstract: We present the Large Processing Model (LPM), a diffusion-based generative framework for photorealistic video restoration under complex, in-the-wild degr

applicationsarxiv-cs-cv

Agents

Lyapunov Exponent as Physics-Informed Dense Reward: RL Discovery of Stabilization Beyond the Kapitza Pendulum

arXiv:2607.14001v1 Announce Type: new Abstract: We suggest using the Lyapunov characteristic exponent (LCE) as a dense reward signal for the reinforcement learning problem of stabilizing the inverted

agentsarxiv-cs-lg

M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detection

arXiv:2607.13499v1 Announce Type: new Abstract: 3D anomaly detection has recently emerged as an important research topic in computer vision. Although existing methods have achieved high performance, e

applicationsarxiv-cs-cv

M+Adam: Low-Precision Training via Additive-Multiplicative Optimization

model-releasesarxiv-cs-lg

arXiv:2607.10611v2 Announce Type: replace Abstract: Training with quantized weights can reduce costs but often results in degraded accuracy, especially when optimization is carried out in low precisio

Marker-free deformable registration and fusion for augmented reality-guided positive margin localization during tumor resection surgery

arXiv:2607.13343v1 Announce Type: new Abstract: Positive margins in head and neck oncologic surgery require mapping specimen-side pathology findings to the patient resection bed. This is challenging b

safetyarxiv-cs-cv

MASPRM: Multi-Agent System Process Reward Model

arXiv:2510.24803v3 Announce Type: replace-cross Abstract: Inference-time search over multi-agent systems (MAS) wastes compute when it cannot identify which agent's intermediate message advanced progre

Maximally Robust Satisficing Bayesian Optimization

arXiv:2607.13652v1 Announce Type: new Abstract: Many design tasks can be cast as black-box function optimization, enabling use of Bayesian optimization to find an ideal design with minimal number of t

MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation

arXiv:2607.09142v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in online medical consultation, yet existing benchmarks remain poorly aligned with real clini

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607.13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approach

Merging Reaction to Cognition: A Hybrid Cognitive Strategy for Odour Source Localisation in Natural Environments

model-releasesarxiv-cs-ro

arXiv:2607.13853v1 Announce Type: new Abstract: Chemical pollutants released into the environment are transported by turbulent flows, generating complex, intermittent plume structures that threaten ec

MetaPerch: Learning from metadata for bioacoustics foundation models

arXiv:2607.14072v1 Announce Type: new Abstract: Bioacoustic foundation models rely on large-scale citizen science platforms like Xeno-Canto for geographically and ecologically diverse data. Recent wor

applicationsarxiv-cs-lg

Agents

M^ext{4}World: A Multi-view Multimodal Driving World Model for Interactive Object Manipulation and Minute-long Streaming

arXiv:2607.14005v1 Announce Type: new Abstract: Driving-world generation has emerged as a core capability for scalable autonomous-driving simulation, yet existing methods remain limited in object-leve

agentsarxiv-cs-cv

MGFace: Mask-Gated Face Matching via Conditional Similarity Routing

arXiv:2607.13187v1 Announce Type: new Abstract: Face identification has achieved remarkable performance under normal conditions. Yet, its accuracy often degrades significantly when query faces are par