EP232: Why AI agents lie to fit in

EP234: FATE fixes safe but useless AI agents

Title: On-Policy Self-Evolution via Failure Trajectories for Agentic Safety Alignment Source: http://arxiv.org/abs/2605.11882v1 Summary: FATE establishes a foundational framework for on-policy self-evolution by transforming agentic failure trajectories into high-density repair supervision without human demonstrations. By employing Pareto-Front Policy Optimization, it provides a scalable architectural primitive for agents to autonomously balance safety and utility across long-horizon tool-use tasks.

EP233: Fixing AI memory with backward chaining

Title: Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems Source: http://arxiv.org/abs/2605.12213v1 Summary: This paper presents Goal-Mem, a framework that employs backward chaining and Natural Language Logic to create a goal-oriented reasoning loop for agentic memory systems. It provides a foundational advancement in how agents can systematically decompose complex queries and retrieve missing intermediate facts for robust multi-hop reasoning.

EP232: Why AI agents lie to fit in

Title: The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions Source: http://arxiv.org/abs/2605.10698v1 Summary: This study formalizes the 'Bystander Effect' in multi-agent systems, identifying a critical failure mode where agents subjugate independent reasoning to social compliance. It introduces the Interaction Depth Limit and Sovereignty Gap as foundational architectural constraints for designing robust and independent multi-agent reasoning topologies.

EP231: Amazon PIVOT solves the AI execution gap

Title: PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement Source: http://arxiv.org/abs/2605.11225v1 Summary: PIVOT introduces a novel self-supervised framework that treats agent trajectories as optimizable objects refined through iterative environment feedback, bridging the gap between high-level planning and execution. This methodology establishes a principled approach to trajectory optimization that enhances both constraint satisfaction and computational efficiency in autonomous systems.

EP230: DeepRefine fixes messy AI knowledge bases

Title: DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning Source: http://arxiv.org/abs/2605.10488v1 Summary: DeepRefine establishes a general reinforcement learning framework for the autonomous refinement of agent-compiled knowledge bases using abductive diagnosis and a novel Gain-Beyond-Draft reward. It provides a foundational reasoning loop for maintaining persistent, high-fidelity external knowledge, which is essential for long-term agentic performance in knowledge-intensive tasks.