DeepSeek带领Qwen团队逆向二进制文件
AI 总结
据Reddit用户转述的实验，DeepSeek-V4.1-Flash协调四个Qwen3.5-9B工作模型，逆向分析两个小型C程序二进制文件，并将函数改写为Python。团队完成了全部38个和56个测试函数，但分别耗时86分钟和100分钟、花费2.97美元和3.60美元；DeepSeek单独运行则分别耗时14分钟和54分钟、花费0.87美元和3.28美元。主导模型多次修改工作模型指令并加入函数签名与字段偏移查询工具，但约40%的函数仍需返回主导模型处理。
深度见解
实验显示主导模型能够组织较小的本地模型完成多智能体逆向工程，但在这两个未剥离符号的小型程序上，协作仍带来明显的额外开销。
| Reverse Engineer Anything blew up recently but, every impressive reverse engineering result we’ve seen uses (closed) frontier models. We wanted to know whether small local models can do useful work here if a bigger model organizes them, hopefully cutting cost and allowing a broder range of people to Reverse Engineer Anything :) Setup To begin with a proof of concept, the targets were two small C programs we generated with Claude Code and compiled. The team only ever saw the binaries. As a baseline, we also ran DeepSeek (the lead) alone on each tasks. The team work is pure overhead on smaller jobs, the lead has to organize and fix the smaller models, taking more time than if it had just worked alone. The most interesting part For the shooter game, the lead ended up changing the instructions of the smaller models 3 times. Caveats You can find the full write-up here: https://huggingface.co/blog/ybelkada/reverse-engineering-anything-with-small-local-mode submitted by /u/HauntingMoment [link] [comments] |
#DeepSeek-V4.1-Flash#Qwen3.5-9B#Reverse Engineer Anything#Claude Code