EP264: Science Earth and Planet Scale AI Discovery

EP265: How AI agents rewrite their own tools

Title: SkillSmith: Co-Evolving Skills and Tools for Self-Improving Agent Systems Source: http://arxiv.org/abs/2606.01314v1 Summary: SkillSmith presents a foundational co-evolution framework that allows agents to simultaneously evolve their skill libraries and underlying toolsets through a synergy-aware reflection process. By utilizing an ecological utility model to manage skill-tool interactions, it establishes a novel architectural loop for autonomous, self-improving agent systems capable of repairing their own functional primitives.

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.