EP216: Shadow memory stops long horizon AI heists

EP238: SepsisAgent outperforms clinicians using clinical world models

Title: Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model Source: http://arxiv.org/abs/2605.14723v1 Summary: This work presents a novel world-model-augmented agentic reasoning loop that utilizes a 'propose-simulate-refine' workflow to ground LLM decisions in action-conditioned dynamics. It demonstrates how integrating world models with agentic reinforcement learning can significantly improve decision-making safety and efficacy in complex environments.

EP237: Why AI agents must map before acting

Title: MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning Source: http://arxiv.org/abs/2605.13037v1 Summary: MAP proposes a paradigm shift for interactive agents by establishing environmental understanding through structured cognitive mapping before task execution. This approach overcomes the epistemic bottlenecks and inefficient failure cycles inherent in traditional reactive, goal-conditioned stepwise planning.

EP236: AI agents rewriting their own code

Title: Harnessing Agentic Evolution Source: http://arxiv.org/abs/2605.13821v1 Summary: AEvo introduces a meta-editing framework that treats the evolution context as a process-level state, allowing agents to iteratively refine their own procedures. This shifts agentic evolution from rigid hand-designed loops to a unified interface for actionable, long-horizon self-improvement.

EP235: How SAGE Fixes AI Memory

Title: SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memory Source: http://arxiv.org/abs/2605.12061v1 Summary: SAGE introduces a self-evolving graph-memory engine that couples a memory writer with a Graph Foundation Model-based reader to create a dynamic, self-improving long-term memory substrate. This framework is foundational for its architectural move beyond static RAG, enabling agents to autonomously refine their structure-aware associative memory through downstream feedback.

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.