LLM Agents
New Method Pinpoints and Fixes LLM Planning Errors Automatically
Researchers develop a system that can identify where LLM-based planners go wrong and automatically correct mistakes, improving AI agent reliability for complex tasks.
LLM Agents
LLM Agents
Why LLM Reasoning Breaks Down in Long-Horizon Planning Tasks
New research reveals systematic failures in how large language models approach multi-step planning, with implications for AI agents in content generation and autonomous systems.
LLM Agents
Counterfactual Intent Generation Improves LLM Agent Control
New research introduces a counterfactual generation framework that helps LLM-based autonomous systems reason about alternative intents, improving decision-making reliability in control applications.
LLM Agents
New Framework for Automated Testing of LLM Agent Reliability
Researchers introduce methods and a framework for automated structural testing of LLM-based agents, addressing critical reliability challenges in agentic AI systems through systematic evaluation approaches.
LLM Agents
Cross-Domain RL Training: Reducing the Generalization Tax for LLM
New research explores how reinforcement learning training affects LLM agent generalization across domains, introducing the concept of 'generalization tax' and strategies to minimize performance degradation.
LLM Agents
Diagnosing Tool Failures in Multi-Agent LLM Systems
New research introduces a systematic framework for identifying why LLM agents fail to invoke tools correctly, addressing a critical reliability gap in multi-agent AI systems.
LLM Agents
New Testing Framework Ensures LLM Agents Behave Predictably
Researchers introduce a determinism-faithfulness assurance harness for tool-using LLM agents, enabling reliable replay testing to catch unpredictable AI behavior in critical applications.
LLM Agents
Aeon: Neuro-Symbolic Memory Boosts Long-Horizon LLM Agents
New research introduces Aeon, a memory management system combining neural and symbolic approaches to help LLM agents maintain coherent reasoning across extended task sequences.
Agentic AI
Survey Maps Agentic AI Architectures and LLM Agent Taxonomies
New comprehensive survey systematically categorizes agentic AI architectures, evaluation frameworks, and taxonomies for large language model agents, providing foundational insights for autonomous AI systems.
LLM Agents
New LLM Agent Framework Tackles ML Feature Engineering Reliabilit
Researchers propose a constrained-topology planning approach for LLM agents that improves reliability in automated feature engineering, addressing key challenges in ML pipeline automation.
LLM Agents
Task2Quiz: New Framework Tests How AI Agents Understand Environme
Researchers introduce Task2Quiz, a systematic paradigm for evaluating what LLM agents actually know about their operating environments, revealing critical gaps in agent world models.
LLM Agents
SimpleMem: A Lightweight Architecture for LLM Agent Memory
New research presents SimpleMem, an efficient memory architecture enabling LLM agents to maintain persistent context across extended interactions without traditional retrieval overhead.