EP203: Agents Under Real Capital

EP244: Learning to Hand Off

Title: Learning to Hand Off: Provably Convergent Workflow Learning under Interface Constraints Source: http://arxiv.org/abs/2605.19140v1 Summary: This research provides the first finite-sample guarantee for neural Q-learning in decentralized multi-agent settings, a foundational breakthrough for reliable agentic workflow learning. By formalizing handoffs as interface-constrained SMDPs, it enables provably convergent learning in complex LLM pipelines where agents have restricted observability.

EP243: Smashing the Data Wall

Title: Generating Pretraining Tokens from Organic Data for Data-Bound Scaling Source: http://arxiv.org/abs/2605.17849v1 Summary: This work addresses the transition of LLM pretraining into data-bound regimes by introducing a synthetic data generation framework that maximizes the utility of limited organic datasets. It represents a significant breakthrough in scaling laws, demonstrating how to unlock up to 5x more effective tokens through model-aware rephrasing and reformatting.

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.