arXiv AI
Jul 3
GroundEval: A Deterministic Replacement for LLM-as-Judge in Stateful Agent Evaluation
arXiv:2606. 22737v2 Announce Type: replace Abstract: Before letting an agent operate over real context, can you prove it used the right evidence?
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jul 3
Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
arXiv:2604. 14228v2 Announce Type: replace-cross Abstract: Claude Code is an agentic coding tool that can run shell commands, edit files, and call external services on behalf of the user.
arXiv AI
Jul 3
Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction
arXiv:2607. 01764v1 Announce Type: new Abstract: Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar that reaches a vulnerable path, construct a proof-of-conceptv(PoC), and verify that the crash disappears on the patched build.
arXiv Machine Learning
Jul 3
Mean Field Reinforcement Learning
arXiv:2607. 01525v1 Announce Type: cross Abstract: This monograph provides an introduction to mean field reinforcement learning through the lens of Markov decision processes arising from large-population stochastic control with mean field interactions and common noise.
arXiv AI
Jul 3
Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence
arXiv:2604. 08169v2 Announce Type: replace Abstract: Alignment in LLMs is more brittle than commonly assumed: misalignment can be induced by adversarial prompts, benign fine-tuning, emergent misalignment, and goal misgeneralization.
arXiv AI
Jul 3
Playing 20 Question Game with Policy-Based Reinforcement Learning
arXiv:1808. 07645v5 Announce Type: replace-cross Abstract: The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity.
arXiv AI
Jul 3
Safeguarding LLM Agents from Misalignment through Provenance Analysis
arXiv:2607. 01236v1 Announce Type: cross Abstract: As LLM agents gain increasing access to powerful tools, ensuring that their actions are aligned with the user's intent becomes critical.
arXiv AI
Jul 3
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use
arXiv:2607. 01874v1 Announce Type: new Abstract: Skills are becoming a reusable operational layer for LLM agents, encoding SOPs, domain rules, tool workflows, scripts, and validation routines.
arXiv Machine Learning
Jul 3
Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
arXiv:2607. 02037v1 Announce Type: cross Abstract: Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment.
arXiv Machine Learning
Jul 3
LIME: Learning Intent-aware Camera Motion from Egocentric Video
arXiv:2607. 02417v1 Announce Type: cross Abstract: Autonomous robots often need to move their camera before they can act: to inspect an object, reveal an occluded region, or obtain a view that responds to a user's intent.
arXiv AI
Jul 3
Janus: a Playground for User-Involved Agentic Permission Management
arXiv:2607. 01510v1 Announce Type: new Abstract: AI agents that autonomously execute tool calls on a user's behalf raise pressing questions about permission management: what role could users play, and what role should they play?
arXiv AI
Jul 3
DiPS: Dialogue Policy Selection for High-Stakes Persuasion Agents
arXiv:2607. 01557v1 Announce Type: cross Abstract: Large Language Models (LLMs) often struggle with persuasion in high-stakes scenarios.
arXiv AI
Jul 3
The Agentic Garden of Forking Paths
arXiv:2607. 01507v1 Announce Type: new Abstract: Empirical research rarely admits a unique analysis.
arXiv AI
Jul 3
A-TMA: Decoupling State-Aware Memory Failures in Long-Term Agent Memory
arXiv:2607. 01935v1 Announce Type: new Abstract: Long term memory lets LLM agents act as persistent assistants, but user facts change.
arXiv AI
Jul 3
BaRA: Budget-constrained and Reliable Web Data Collection Agent
arXiv:2607. 00007v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based web agents automate web navigation and data collection.
Hugging Face Trending Papers
Jul 2
Criticality-Based Guard Rail Validation for AI Agent Decisions in Autonomous Telecom Networks
The evolution toward fully autonomous telecommunications networks (Autonomous Network Levels 4-5) requires AI/ML agents to make real-time network decisions without human intervention. However, no standardized runtime mechanism exists to intercept and validate individual inference outputs before they trigger live network state changes, creating risks of erroneous autonomous decisions.
Hugging Face Trending Papers
Jul 2
Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving
Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models remains untested.
Hugging Face Trending Papers
Jul 2
Cross-Platform Control for Autonomous Surface Vehicles via Adaptive Reinforcement Learning
Autonomous surface vehicles vary widely in hydrodynamic and actuation characteristics, yet most controllers are designed for single-platform deployment. We present an adaptive reinforcement learning approach for trajectory tracking that enables zero-shot cross-platform deployment using a single policy.
Hugging Face Trending Papers
Jul 2
Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization
Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic planning, and knowledge-graph construction. Measuring how closely an output matches a gold reference is essential yet surprisingly hard: exact match is brittle, text similarity ignores structure, and an LLM judge is expensive, opaque, and non-deterministic.
Hugging Face Trending Papers
Jul 2
SimWorlds: A Multi-Agent System for Dynamic 3D Scene Creation
LLM agents are increasingly used to translate natural language into 3D scenes in a procedural way, but existing systems focus on static output. Dynamic 4D scenes from text alone, in which liquids flow, particles emit, rigid bodies cascade, and articulated mechanisms move, remain largely unexplored despite their value as editable content and as physics-grounded training data for video generation and embodied AI.