EP230: DeepRefine Curing AI Memory

EP240: The Orchard Framework

Title: Orchard: An Open-Source Agentic Modeling Framework Source: http://arxiv.org/abs/2605.15040v1 Summary: Orchard provides a scalable open-source framework for agentic modeling, introducing reusable environment primitives and training recipes that enable LLMs to achieve state-of-the-art performance on complex tasks. It addresses critical gaps in agent infrastructure by standardizing sandbox management and introducing credit-assignment SFT for learning from unresolved trajectories.

EP239: The LIFE Progression

Title: Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent SystemsSource: http://arxiv.org/abs/2605.14892v1 Summary: This work introduces the LIFE progression framework, which formally characterizes the causal dependencies between agent foundation, collaboration, failure attribution, and autonomous self-evolution. It establishes a foundational conceptual roadmap for building self-organizing multi-agent systems that can continuously diagnose and refine their own collective intelligence.

EP238: SepsisAgent Future ICU Care

Title: Agentifying Patient Dynamics within LLMs through Interacting with Clinical World ModelSource: 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: Look Around First

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: AEVO Mastering Evolution

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.