Search: “papers”

TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation

arXiv:2605.25547v1 Announce Type: new Abstract: Existing embodied control research demonstrates remarkable performance improvements by scaling training data and model size. We instead explore inferenc

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The Concept Allocation Zone: Tracking How Concepts Form Across Transformer Depth

DGX agent

arXiv:2605.24856v1 Announce Type: cross Abstract: Concept formation in transformer language models is depth-extended, not a single-layer event: concepts emerge gradually across a contiguous region of

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26 May 2026

The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot

arXiv:2409.08379v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors cho

Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate

arXiv:2605.25134v1 Announce Type: cross Abstract: Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is ell_p regularization. H

Toward a Benchmark for Controllable Simulation of Imperfect Students with Large Language Models

arXiv:2605.25601v1 Announce Type: cross Abstract: Teacher education requires deliberate practice with learners who exhibit identifiable strengths, weaknesses, and partial mastery. Large language model

Toward Reliable Design of LLM-Enabled Agentic Workflows: Optimizing Latency-Reliability-Cost Tradeoffs

arXiv:2605.23929v1 Announce Type: new Abstract: Modern AI systems increasingly rely on workflows composed of multiple interacting agents, some powered by large language models (LLMs) and others by con

Towards Large Model Feature Coding

model-releasesarxiv-cs-lg

arXiv:2605.24025v1 Announce Type: cross Abstract: Large models have delivered remarkable performance across a wide range of perception and generation tasks, yet practical deployment is increasingly co

Agents

Towards Multi-Turn Dialog Systems for Industrial Asset Operations and Maintenance

arXiv:2605.24953v1 Announce Type: new Abstract: Industrial asset operations and maintenance question answering is inherently multi-turn, iterative, and highly dependent on external tool invocation. Ho

agentsarxiv-cs-ai

Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring

arXiv:2605.25731v1 Announce Type: new Abstract: Multi-trait essay scoring aims to provide fine-grained evaluation of writing quality across multiple dimensions. However, how to effectively post-train

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Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains

arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of

Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing

arXiv:2605.24052v1 Announce Type: cross Abstract: To better serve users' demands in mobile applications (e.g., navigation), mobile crowdsourcing platforms can iteratively align large language model (L

TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks

arXiv:2605.23988v1 Announce Type: cross Abstract: Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacit

Applications

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

arXiv:2011.10254v3 Announce Type: replace-cross Abstract: Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all

applicationsarxiv-cs-ai

Uncovering Vulnerabilities of LLM-Assisted Cyber Threat Intelligence

arXiv:2509.23573v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to help security analysts manage the surge of cyber threats, automating tasks from vulnerab

Understanding the Impact of Geometric Foundation Models on Vision-Language-Action Models

arXiv:2605.24642v1 Announce Type: cross Abstract: Recent work explores new opportunities at the intersection of vision-language-action models (VLAs) and geometric foundation models (GFMs) for 3D recon

researcharxiv-cs-ro

UtilityMax Prompting: A Formal Framework for Multi-Objective Large Language Model Tasks

arXiv:2603.11583v4 Announce Type: replace-cross Abstract: The success of a Large Language Model (LLM) task depends heavily on its prompt. Most use-cases specify prompts using natural language, which i

Tutorials

Weakly Supervised Camouflaged Object Detection Based on the SAM Model and Mask Guidance

arXiv:2605.25385v1 Announce Type: cross Abstract: Camouflaged object detection (COD) from a single image is a challenging task due to the high similarity between objects and their surroundings. Existi

tutorialsarxiv-cs-ai

When Self-Belief Misleads: Active Label Acquisition for Reinforcement Learning with Verifiable Rewards

arXiv:2605.25864v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable advancements in reasoning capabilities empowered by Reinforcement Learning with Verifiable Rewar

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When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agents

arXiv:2605.24069v1 Announce Type: cross Abstract: The rise of tool-using Large Language Model (LLM) agents, standardized by protocols like the Model Context Protocol (MCP), has unlocked unprecedented

World-State Transformations for Neuro-symbolic Interactive Storytelling

arXiv:2605.24719v1 Announce Type: cross Abstract: Large Language Models (LLMs) have changed the possibilities of Interactive Storytelling systems that process free-text user input. However, as more of

XRPO: Pushing the limits of GRPO with Targeted Exploration and Exploitation

arXiv:2510.06672v3 Announce Type: replace Abstract: Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of roll

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You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos

arXiv:2303.07863v3 Announce Type: replace-cross Abstract: Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target moment semantically according to a sentence query. Althoug

4DThinker: Thinking with 4D Imagery for Dynamic Spatial Understanding

arXiv:2605.05997v2 Announce Type: replace Abstract: Dynamic spatial reasoning from monocular video is essential for bridging visual intelligence and the physical world, yet remains challenging for vis

safetyarxiv-cs-cv

A Comparative Evaluation of Structural Topic Models and BERTopic for Short, Open-Ended Survey Responses

model-releasesarxiv-cs-cl

arXiv:2605.23093v1 Announce Type: new Abstract: Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based appro

Tutorials

📢 Accepted to TMLR, with reproducibility certification 🏅 v2 of our JEPA-WM study (arXiv:2512.24497) is out, with new data-scaling experime…

📢 Accepted to TMLR, with reproducibility certification 🏅 v2 of our JEPA-WM study (arXiv:2512.24497) is out, with new data-scaling experiments, a Lipschitz analysis of multistep rollout training, and e

tutorialsyann-lecun--x

AGZO: Activation-Guided Zeroth-Order Optimization for LLM Fine-Tuning

arXiv:2601.17261v4 Announce Type: replace Abstract: Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under strict memory constraints, as it avoids the prohibitiv

Agents

AI Assurance: A Comprehensive Testing Strategy for Enterprise AI Systems

arXiv:2605.23459v1 Announce Type: cross Abstract: Enterprise AI systems, built on large language models, retrieval pipelines and autonomous agents, introduce a class of risks that traditional software

agentsarxiv-cs-ai

Local Ai

Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity

arXiv:2605.22871v1 Announce Type: cross Abstract: Machine unlearning is a fundamental mechanism that enforces the right to be forgotten. Existing unlearning studies that rely on label manipulation or

local-aiarxiv-cs-ai

AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse

model-releasesarxiv-cs-cl

arXiv:2605.23325v1 Announce Type: new Abstract: Social media has become a crucial arena for shaping public narratives during armed conflicts, providing space for both harmful and constructive communic

Are Targeted Data Poisoning Attacks as Effective as We Think?

arXiv:2509.06896v2 Announce Type: replace Abstract: Targeted data poisoning attacks manipulate model predictions on specific test samples by injecting malicious data into training. Yet existing evalua

Applications

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

arXiv:2605.23473v1 Announce Type: cross Abstract: Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding,

applicationsarxiv-cs-ai

BarrierSteer: LLM Safety via Learning Barrier Steering

arXiv:2602.20102v2 Announce Type: replace-cross Abstract: Despite the strong performance of large language models (LLMs) across diverse tasks, their susceptibility to adversarial attacks and unsafe co

Benchmarking and Enhancing VLM for Compressed Image Understanding

model-releasesarxiv-cs-cv

arXiv:2512.20901v2 Announce Type: replace Abstract: With the rapid development of Vision-Language Models (VLMs) and the growing demand for their applications, efficient compression of the image inputs

Benchmarking Gaslighting Attacks Against Speech Large Language Models

arXiv:2509.19858v2 Announce Type: replace Abstract: As Speech Large Language Models (Speech LLMs) become increasingly integrated into voice-based applications, ensuring their robustness against manipu

researcharxiv-cs-cl

Beyond VLM-Based Rewards: Diffusion-Native Latent Reward Modeling

arXiv:2602.11146v2 Announce Type: replace-cross Abstract: Preference optimization for diffusion and flow-matching models relies on reward functions that are both discriminatively robust and computatio

Tutorials

CALAD: Channel-Aware contrastive Learning for multivariate time series Anomaly Detection

arXiv:2605.23139v1 Announce Type: cross Abstract: Multivariate time series anomaly detection has become increasingly important in real-world applications, where labeled data are often scarce. Many exi

tutorialsarxiv-cs-ai

Calibration-Informative Region Selection for Online LiDAR--Camera Calibration in Agricultural Environments

arXiv:2605.23580v1 Announce Type: new Abstract: Reliable multi-modal calibration requires identifying which observations truly constrain the extrinsic parameters and which ones mainly add noise or amb

researcharxiv-cs-cv

CarlaNCAP: A Framework for Quantifying the Safety of Vulnerable Road Users in Infrastructure-Assisted Collective Perception Using EuroNCAP Scenarios

arXiv:2512.11551v2 Announce Type: replace Abstract: The growing number of road users has significantly increased the risk of accidents in recent years. Vulnerable Road Users (VRUs) are particularly at

safetyarxiv-cs-ro

CBANet: A Compact Attention-Based CNN-BiLSTM Network for Aggressive Driving Event Detection

arXiv:2605.23471v1 Announce Type: cross Abstract: Aggressive driving is a major cause of traffic accidents and poses a serious threat to road safety. Although deep learning methods have shown promisin

Local Ai

CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs

arXiv:2605.23344v1 Announce Type: cross Abstract: Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language p

local-aiarxiv-cs-ai

Convex Compositional Reasoning Models

arXiv:2605.23395v1 Announce Type: new Abstract: Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constra

Cultural Adaptation in Large Language Models for Political Discourse

model-releasesarxiv-cs-cl

arXiv:2605.23332v1 Announce Type: new Abstract: The integration of large language models into political discourse analysis creates new opportunities for comparative research, policy analysis, and civi

Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models

arXiv:2605.23797v1 Announce Type: cross Abstract: Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the r

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Decoupling Spatio-Temporal Adapter for Fine-Grained Badminton Action Localization

model-releasesarxiv-cs-cv

arXiv:2605.23355v1 Announce Type: new Abstract: Temporal Action Localization (TAL) has been extensively studied in generic video understanding, while fine-grained sports scenarios, such as professiona

DELICATE: Diachronic Entity LInking using Classes And Temporal Evidence

arXiv:2511.10404v2 Announce Type: replace Abstract: In spite of the remarkable advancements in the field of Natural Language Processing, the task of Entity Linking (EL) remains challenging in the fiel

researcharxiv-cs-cl

DepthAgent: Towards Better Universal Depth Estimation via Sample-wise Expert Selection

model-releasesarxiv-cs-cv

arXiv:2605.23281v1 Announce Type: new Abstract: Monocular metric depth estimation has achieved strong progress with large-scale training and universal-camera modeling, yet robust deployment across div

Design and Report Benchmarks for Knowledge Work

arXiv:2605.23262v1 Announce Type: new Abstract: The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowl

Hardware

DFSAttn: Dynamic Fine-grained Sparse Attention for Efficient Video Generation

arXiv:2605.23445v1 Announce Type: new Abstract: Diffusion transformers have achieved remarkable success in high-quality video generation, yet their reliance on spatiotemporal 3D full attention incurs

hardwarearxiv-cs-cv