TRUST 框架为递归自我改进加统计检验门槛
Trustworthy Recursive Self-Improvement with Statistical Guarantees
智能体改自己的代码时最怕把噪声当进步，贪心门控近一半更新其实是在变差。这篇论文用在线假设检验把劣化率压到 2.78%，还给了性能增益下界证明。做自进化智能体框架的开发者值得把这套门控思路搬过去。
arXiv 论文提出 TRUST（Trustworthy Recursive Updates with Statistical Testing）框架，用于让 AI 智能体在不重训底层 LLM 的前提下通过修改自身 harness 实现可靠的递归自我改进。核心问题在于更新门控：贪心门控只看验证集分数提升就接受更新，实验中 45.59% 的被接受更新实际是劣化更新，导致性能退化。
TRUST 的机制是把在线多重假设检验流程移植到进化轮次中，控制跨轮次的虚假进化率，并由 LLM 分析历史上下文来决定接受一次更新需要多少证据。论文在可解释的假设下证明了 TRUST 能限制被接受更新中造成不可接受性能损失的比例，并推导出最终性能增益的下界。
在 HotpotQA、DROP、APPS 三个基准上，使用 DeepSeek-V4-Flash 和 Qwen-3.8-Flash 两个模型，TRUST 把劣化更新接受比例从贪心门控的 45.59% 降到 2.78%，最终分数也高于现有的统计门控基线。
Trustworthy Recursive Self-Improvement with Statistical Guarantees
Recursive self-improvement offers a promising path toward AI agents that improve through experience by refining their own harnesses without retraining their underlying large language models (LLMs). Making this self-improvement process reliable, however, requires more than generating new updates: agents must decide which changes are worth keeping. A greedy gate that accepts updates based solely on higher validation scores can mistake an observed gain for genuine progress and lead to performance degeneration, whereas overly conservative statistical gates may reject beneficial updates and hinder performance improvement. We introduce Trustworthy Recursive Updates with Statistical Testing (TRUST), a framework that adapts an online multiple hypothesis testing procedure to control the rate of false evolution across rounds, with an LLM analyzing historical context to help determine how much evidence is required to update the incumbent. Under interpretable assumptions, we prove that TRUST limits the proportion of accepted updates that result in unacceptable performance loss. We further derive a lower bound on the final performance gain. Across the benchmarks HotpotQA, DROP, and APPS with the models DeepSeek-V4-Flash and Qwen-3.8-Flash, TRUST reduces the average ratio of accepted inferior updates from the greedy gate's 45.59% to 2.78%, while achieving higher final scores than the state-of-the-art statistical gate baselines for recursive self-improvement. These findings demonstrate that TRUST strikes a balance between statistical reliability and performance gain in recursive self-improvement.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：TRUST、DeepSeek-V4-Flash、Qwen-3.8-Flash、智能体，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始信息发布自一手权威信源「arXiv: DeepSeek」（Tier-1 一手发布），具备高确定性参考价值。