UC Berkeley 提出让研究智能体定期遗忘历史以扩展自动化研究
Berkeley 这篇论文挺有意思：让研究智能体定期清空记忆重启，反而比保留全部历史多拿最多 39% 成绩。
UC Berkeley 的论文提出一种扩展自动化研究智能体的新方法：不再让智能体保留完整会话历史迭代，而是周期性丢弃会话与候选历史，从当前最优候选重新启动，以此避免智能体锚定在过往尝试上只做小幅修改。在启发式编程、内核优化和模型训练任务上，该方法比同预算的并行智能体最高提升 15%，比单个智能体最高提升 39%。论文编号为 arXiv:2610.20563。
Brilliant paper on scaling research agents from UC Berkeley. (bookmark it) They propose an unexpected and interesting way to scale automated research with agents. It's common to see research agents keep their full history and iterate on it to solve a task. After a while, each agent anchors on its own past attempts, makes smaller edits, and extra budget stops improving things. So this work induces forgetting in the agents. Periodically, they discard each agent's session and the candidate history, and then restart from the best candidate found so far. Across heuristic programming, kernel optimization, and model training tasks, this gives up to 15% over parallel agents and up to 39% over a single agent with the same budget. arxiv.org/abs/2610.20563 Chat with Paper: academy.dair.ai/papers/scaling… 💬 6 🔄 1 ❤️ 13 👀 2182 📊 9 ⚡