Notas do episódio
This episode examines "Harness 2," a paper on recursive agent harnessing. Its premise is that the wrapper around a frozen model can be improved automatically on a stream of unlabeled tasks, with no ground-truth verifier. The "harness" here is eight editable files: prompts, guardrails, memory, skills, tools, scripts, subagents, and middleware. The discussion places the work against prior approaches. These include SWE-agent's interface findings, Meta-Harness's freeze-after-search approach, per-instance scaling, ReasoningBank, Reflexion, and the Darwin Gödel Machine. It then explains the two coupled loops. Task-level refinement edits the execution harness for the current task. Harnessing-level recursion folds the lessons into a separate improvement harness, made of a library of adopted harnesses plus an EXPERIENCE.md file, while each new task still starts from a common base. The episode begins a walkthrough of the Propose, Probe, Compose refinement step, using a JobBench case that rose from 43.1 to 77.8. Listeners interested in self-improving agents will find it useful for seeing why self-correction without external feedback is hard and how this design tries to work around that.
Sources:
1. Harness^2: Recursive Agent Harnessing for an Open World — Ruiyao Xu, Yanfei Chen, Zhongying CuiZhu, Bhavana Dalvi Mishra, Yifei Ming, Han Yu, Rujun Han, Chen-Yu Lee, Tomas Pfister, 2026
http://arxiv.org/abs/2610.14224
2. Reflexion: Language Agents with Verbal Reinforcement Learning — Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao, 2023
https://scholar.google.com/scholar?q=Reflexion%3A+Language+Agents+with+Verbal+Reinforcement+Learning
3. Automated Design of Agentic Systems (ADAS) — Shengran Hu, Cong Lu, Jeff Clune, 2024
https://scholar.google.com/scholar?q=Automated+Design+of+Agentic+Systems+%28ADAS%29
4. Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents — Jenny Zhang, Shengran Hu, Cong Lu, Robert Lange, Jeff Clune, 2025
https://scholar.google.com/scholar?q=Darwin+G%C3%B6del+Machine%3A+Open-Ended+Evolution+of+Self-Improving+Agents
5. ReasoningBank: Scaling Agent Self-Evolving with Reasoning Memory — Siru Ouyang et al. (Google Cloud AI Research and collaborators), 2025
https://scholar.google.com/scholar?q=ReasoningBank%3A+Scaling+Agent+Self-Evolving+with+Reasoning+Memory
6. Self-Refine: Iterative Refinement with Self-Feedback — Aman Madaan, Niket Tandon, Peter Clark, Yiming Yang, et al., 2023
https://scholar.google.com/scholar?q=Self-Refine%3A+Iterative+Refinement+with+Self-Feedback
7. Large Language Models Cannot Self-Correct Reasoning Yet — Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, Denny Zhou, 2023
https://scholar.google.com/scholar?q=Large+Language+Models+Cannot+Self-Correct+Reasoning+Yet
8. Large Language Models as Optimizers (OPRO) — Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen…
