EP228: Do Self-Evolving Agents Forget

EP242: The Experience Graph

Title: EXG: Self-Evolving Agents with Experience Graphs Source: http://arxiv.org/abs/2605.17721v1 Summary: This paper introduces the first experience graph framework for self-evolving agents, providing a structured relational representation for successes and failures that enables real-time experience reuse. It establishes a principled foundation for scalable agent behavior by allowing behaviorally static agents to systematically improve through structured memory.

EP241: Parallelizing CFR

Title: Parallelizing Counterfactual Regret MinimizationSource: http://arxiv.org/abs/2605.14277v1 Summary: This work introduces a generalized framework that reframes counterfactual regret minimization as linear algebra operations, allowing for massive parallelization on modern hardware. By achieving a four-order-of-magnitude speedup, it provides a foundational efficiency breakthrough for the reasoning algorithms central to strategic decision-making in complex environments.

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.