EP242: Ending AI Amnesia with Experience Graphs

EP257: How planning wakes up deep AI layers

Title: Do Agents Think Deeper? A Mechanistic Investigation of Layer-Wise Dynamics in Sequential Planning Source: http://arxiv.org/abs/2605.27935v1 Summary: This study provides foundational mechanistic evidence that agentic reasoning requires dynamic, adaptive recruitment of model depth, distinguishing it from static inference tasks. These insights into layer-wise dynamics are critical for developing the next generation of LLM architectures optimized for long-horizon planning and iterative tool use.

EP256: Teaching AI to Doubt Its Own Answers

Title: Confidence-Orchestrated Self-Evolution against Uncertain LLM Feedback Source: http://arxiv.org/abs/2605.28010v1 Summary: COSE provides a foundational framework for LLM self-evolution by using intrinsic model confidence as an uncertainty signal to filter and weigh self-generated training signals. This approach addresses the critical bottleneck of error propagation in autonomous learning loops, enabling models to improve their reasoning and mathematical capabilities without human-curated supervision or external verifiers.

EP255: MUSE-Autoskill creates self-evolving AI agents

Title: MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation Source: http://arxiv.org/abs/2605.27366v1 Summary: This paper proposes a novel architectural framework for self-evolving agents that can autonomously create, store, and refine a library of reusable skills through a unified lifecycle management system. It introduces the concept of skill-level memory and unit-testable assets, representing a major advancement in building agents capable of continuous improvement and cross-task experience accumulation.

EP254: Why Innovation Guarantees AI Hallucination

Title: Innovation: An Almost Characterization of Hallucination Source: http://arxiv.org/abs/2605.26808v1 Summary: This work establishes a foundational probabilistic framework that formalizes hallucination as "innovation," providing a mathematical characterization of why LLMs produce outputs outside their training data. By deriving new lower bounds on hallucination rates based on "missing mass," it offers a critical theoretical breakthrough for understanding and mitigating the core reliability limits of generative models.

EP253: MACA optimizes AI agent coordination

Title: Multi-Agent Coordination Adaptation via Structure-Guided Orchestration Source: http://arxiv.org/abs/2605.25746v1 Summary: The paper introduces a novel probabilistic framework for multi-agent coordination by casting orchestration as posterior inference over task-specific structures. This foundational approach balances structural stability with dynamic adaptability, significantly improving execution efficiency and performance while reducing token overhead in complex agentic workflows.