这篇论文戳中了编码智能体效率低下的核心痛点——不是试得不够多，而是记不住经验。做 AI 编程工具或智能体开发的团队，可以直接借鉴其摘要复用和锦标赛选择方法，值得点开看看。
Meta 最新论文发现，编码智能体在复用过去尝试的简短摘要（而非原始日志）时，性能显著提升。研究表明，更强的编码智能体不仅需要更多尝试，更需要更好的记忆方式。论文提出将每次完整尝试转化为紧凑摘要，包含主要猜测、部分进展和失败点，然后利用这些摘要选择最佳尝试并指导新尝试。在 SWE-Bench Verified 基准上，Claude 4.5 Opus 从 70.9% 提升至 77.6%，在 Terminal-Bench v2.0 上从 46.9% 提升至 59.1%。核心结论是：长编码任务的测试时扩展瓶颈不在于生成更多尝试，而在于以智能体可复用的形式存储经验。
Meta paper shows that coding agents get much bette…
Meta paper shows that coding agents get much better when they reuse short summaries of past attempts instead of raw logs.
i.e. stronger coding agents do not just need more attempts, but better ways to remember attempts.
That sounds obvious until you look at what an agent actually produces: not an answer, but a messy trail of file reads, shell commands, errors, partial fixes, and abandoned ideas.
The paper’s idea is to turn each full attempt into a compact summary of the main guess, partial progress, and failure points, then use those summaries both to pick the best attempts and to guide new ones.
Test-time scaling breaks when the model cannot compare its own past work.
For short answers, ranking is easy.
For long-horizon coding, the bottleneck shifts from generation to representation.
Once rollouts become summaries, two useful things happen.
The system can run tournament-style selection over small groups of candidates, which works better than forcing one giant comparison, and it can feed the best summaries back into a fresh round of attempts instead of starting blind.
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The authors test this on 2 hard coding benchmarks by running many attempts in parallel, selecting promising summaries with a tournament style voting method, and then launching fresh attempts that can read the selected summaries first.
The results are strong, with Claude 4.5 Opus rising from 70.9% to 77.6% on SWE-Bench Verified and from 46.9% to 59.1% on Terminal-Bench v2.0.
What matters is that the paper says better test-time scaling for long coding agents is not mostly about making more attempts, but about storing experience in a form the agent can actually reuse.
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Paper Link – arxiv. org/abs/2604.16529
Paper Title: "Scaling Test-Time Compute for Agentic Coding"