arXiv cs.AI· Jian Zhao, Haoren Luo, Yu Wang, Yuhan Cao, Pingyue Sheng, Tianxing He·· 9 天前AI 评分29
LLM 能否重新发明基础算法？研究显示当前模型仍高度依赖记忆
Can Large Language Models Reinvent Foundational Algorithms?
AI 导读
研究用 LLM 遗忘方法抑制模型对目标算法的直接回忆，考察其能否凭剩余知识重新推导出算法。结果显示 LLM 无法恢复近一半的目标算法，仅在解码时抑制算法名称提及就会让恢复率骤降至 19–39%，表明模型过度依赖记忆。可恢复的算法多为结构或核心思想较简单的，作者还引入生成式验证器以维持推理强度、避免“思维崩溃”。
正文 · 原文
Abstract:LLMs have shown strong potential to advance scientific discovery. Whether they possess the capacity for foundational innovation, however, remains an open question. In this work, we focus on a prerequisite for foundational innovation: \textit{can LLMs reinvent foundational algorithms in computer science?} We use LLM unlearning methods to suppress direct recall of the target algorithm and let the model reason with the remaining knowledge to recover it. Although unlearning does not guarantee full knowledge removal, LLMs fail to recover nearly half of the target algorithms. Notably, even suppressing the mention of the algorithm's name during decoding without unlearning makes the models' recovery rate drop dramatically (19--39\%), suggesting their overreliance on memorized knowledge. We observe that recoverable algorithms tend to be simple in structure or core ideas, whereas the others are less straightforward. We also introduce a generative verifier that sustains models' reasoning strength, helping to avoid the ``thought collapse'' phenomenon. Taken together, by treating the unlearned model's recovery rate as an approximate upper bound, our empirical results suggest that current LLM systems still have limited ability to make foundational algorithm innovation. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.05716 [cs.AI] |
| (or arXiv:2604.05716v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2604.05716 arXiv-issued DOI via DataCite |
Submission history
From: Jian Zhao [view email]
[v1]
Tue, 7 Apr 2026 11:15:22 UTC (7,589 KB)
[v2]
Thu, 8 Oct 2026 02:29:15 UTC (426 KB)
来源：arXiv cs.AI · arxiv.org