EP240: Small AI agents beat giants with Orchard

EP264: Science Earth and Planet Scale AI Discovery

Title: Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery Source: http://arxiv.org/abs/2606.01316v1 Summary: It introduces a planet-scale scientific runtime and the EACN protocol, enabling decentralized AI capabilities to autonomously discover and negotiate task ownership. This shifts agentic orchestration from pre-designed workflows to emergent, self-correcting coordination for large-scale discovery.

EP263: How POPO ends AI training waste

Title: RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning Source: http://arxiv.org/abs/2606.01281v1 Summary: This paper introduces POPO, a novel optimization framework that solves the critical zero-variance reward bottleneck in Reinforcement Learning with Verifiable Rewards (RLVR) for LLM reasoning. By implementing prioritized group replay and decoupled off-policy optimization, it provides a foundational efficiency breakthrough for training reasoning-intensive models with significantly reduced rollout overhead.

EP262: Web agents that learn from failure

Title: Learning to Adapt: Self-Improving Web Agent via Cognitive-Aware Exploration Source: http://arxiv.org/abs/2605.31365v1 Summary: SCALE introduces a foundational self-improving framework that enables agents to autonomously expand their cognitive boundaries through adversarial exploration and global planning strategies. It marks a significant shift from static, handcrafted execution pipelines to truly adaptive agentic systems that learn and generalize from their own environmental interactions.

EP261: EchoRL turns hesitation into genius

Title: EchoRL: Reinforcement Learning via Rollout Echoing Source: http://arxiv.org/abs/2605.31228v1 Summary: This paper introduces EchoRL, a novel reinforcement learning primitive that prevents training signal collapse in reasoning models by recovering gradients from successfully verified rollouts. It establishes a foundational method for post-training LLMs to achieve higher reasoning performance without encountering the typical diminishing returns of standard RLVR methods.

EP260: GrepSeek brings Unix precision to AI

Title: GrepSeek: Training Search Agents for Direct Corpus Interaction Source: http://arxiv.org/abs/2605.29307v1 Summary: This paper introduces Direct Corpus Interaction (DCI), a foundational paradigm shift where search agents treat text corpora as executable environments via shell commands instead of traditional ranked indices. By training agents to find and compose evidence directly from raw data using a two-stage RL pipeline, it establishes a new architectural framework for knowledge-intensive agentic reasoning.