arXiv AI
Jun 24
Governed Shared Memory for Multi-Agent LLM Systems
arXiv:2606. 24535v1 Announce Type: new Abstract: Multi-agent LLM environments require robust mechanisms for shared knowledge management.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 24
PixJail: Self-Evolving Paper-to-Pipeline Reproduction for Text-to-Image Jailbreak Evaluation
arXiv:2606. 24081v1 Announce Type: cross Abstract: As Text-to-Image (T2I) jailbreak techniques evolve rapidly, existing benchmarks and reproduction workflows often struggle to keep pace.
arXiv AI
Jun 24
EMAgnet: Parameter-Space EMA Regularization for Policy Gradient Self-Play in Large Games
arXiv:2606. 23995v1 Announce Type: cross Abstract: Recent work has established that regularized policy gradient methods such as PPO, when used in self-play, can match or exceed specialized game-theoretic algorithms for solving two-player zero-sum imperfect-information games.
arXiv AI
Jun 24
InSight: Self-Guided Skill Acquisition via Steerable VLAs
arXiv:2606. 24884v1 Announce Type: cross Abstract: Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training data.
arXiv AI
Jun 24
Decentralized Coordination of Autonomous Traffic Through Advanced Air Mobility Corridors
arXiv:2606. 23832v1 Announce Type: cross Abstract: The use of dedicated corridors for Advanced Air Mobility (AAM) traffic is one of the most commonly proposed pathways to integrating them into existing airspace operations.
arXiv AI
Jun 24
HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training
arXiv:2606. 20189v3 Announce Type: replace-cross Abstract: Leveraging Vision Foundation Models (VFMs) for camera-to-LiDAR knowledge distillation offers a promising solution to the scarcity of annotated data needed to represent the immense geometric and kinematic diversity of real-world autonomous driving (AD).
arXiv AI
Jun 24
OpenThoughts-Agent: Data Recipes for Agentic Models
arXiv:2606. 24855v1 Announce Type: new Abstract: Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents.
arXiv AI
Jun 24
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
arXiv:2606. 23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage.
arXiv AI
Jun 24
DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
arXiv:2606. 24779v1 Announce Type: cross Abstract: Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability.
arXiv AI
Jun 24
GUI vs. CLI: Execution Bottlenecks in Screen-Only and Skill-Mediated Computer-Use Agents
arXiv:2606. 24551v1 Announce Type: new Abstract: Computer-use agents can execute software tasks through either graphical interfaces or programmatic command interfaces, but existing evaluations confound interaction modality with differences in tasks, initial states, verifiers, and permitted actions.
arXiv AI
Jun 24
Agentic AI for Bilevel Long-Term Optimization of Policy-Driven Physical Layer Systems
arXiv:2606. 24416v1 Announce Type: new Abstract: Network operators' changing policies, service requirements, and stringent real-time constraints render existing methods designed with fixed objectives and constraints ineffective.
arXiv Machine Learning
Jun 24
Open-Vocabulary BEV Segmentation with 3D-Aware Geometric Constraints
arXiv:2606. 24353v1 Announce Type: cross Abstract: Bird's-eye view (BEV) perception fuses multi-camera images into a unified top-down representation for autonomous driving.
arXiv AI
Jun 24
Accuracy and Satisfaction in Multi-Turn LLM Dialogues for NFR Assessment
arXiv:2606. 24834v1 Announce Type: new Abstract: LLM-based dialogue assistants have become mainstream tools for software developers, yet current evaluation benchmarks focus exclusively on functional correctness.
arXiv AI
Jun 24
Skills for the future software profession: beyond agentic AI!
arXiv:2606. 21894v2 Announce Type: replace-cross Abstract: As coding agents are rapidly changing software engineering, a natural question is: what are the core skills needed by future software engineers?
arXiv AI
Jun 24
More Skills, Worse Agents? Skill Shadowing Degrades Performance When Expanding Skill Libraries
arXiv:2605. 24050v2 Announce Type: replace-cross Abstract: Skill libraries allow LLM agents to load task-specific instructions on demand, letting non-expert users solve domain-specific tasks through natural language without knowing which skills exist or how they work.
arXiv AI
Jun 24
Beyond the Autoregressive Horizon: A Comprehensive Survey of Diffusion Models, World Modelling, and State Space Models for Code
arXiv:2606. 23690v1 Announce Type: cross Abstract: Autoregressive (AR) language models have driven significant progress in automated software engineering, enabling powerful code generation and assistance systems.
arXiv AI
Jun 24
Engineering Reliable Autonomous Systems: Challenges and Solutions
arXiv:2606. 23760v1 Announce Type: cross Abstract: Engineering reliable autonomous systems is an important and growing topic in computer science.
arXiv AI
Jun 24
Breaking the Filter Bubble: A Semantic Pareto-DQN Framework for Multi-Objective Recommendation
arXiv:2606. 24042v1 Announce Type: new Abstract: Recommender systems often induce filter bubbles and semantic homogenization by monolithically optimizing for immediate user engagement.
arXiv AI
Jun 24
The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents
arXiv:2606. 24470v1 Announce Type: new Abstract: A real-time agent for general computer use - with games as the most demanding case - must act within tens of milliseconds while still planning over seconds.
arXiv Machine Learning
Jun 24
Multi-agent imitation learning with function approximation: Linear Markov games and beyond
arXiv:2602. 22810v2 Announce Type: replace Abstract: In this work, we present the first theoretical analysis of multi-agent imitation learning (MAIL) in linear Markov games where both the transition dynamics and each agent's reward function are linear in some given features.