asmsense offers 228k stripped x86-64 functions and a Qwen3-8B naming model
Posted on r/MachineLearning by /u/luxurycashew, asmsense is a dataset plus small model (Qwen3-8B with LoRA) for naming and describing functions in stripped binaries. It currently holds 228,177 functions from 341 projects compiled from -O0 through -Os, with English and Turkish descriptions and Chinese planned. The author compiled permissively licensed C projects so each function retains its real name and source, and lists remaining gaps: other architectures such as ARM64, RISC-V and MIPS, GCC/ELF builds (everything so far is clang/Mach-O on macOS), human-checked labels, and runs on public benchmarks including BinMetric, REBench, BinSum and DeBinVul.
The author says descriptions were written by teacher models that could see the C source and only a sample was checked, by another model, so label quality has not been human-verified.
| asmsense is a dataset and a small model (Qwen3-8B + LoRA) for naming and describing functions in stripped binaries. I compiled permissively licensed C projects myself, so every function has its real name and source. Right now it has 228,177 functions from 341 projects, compiled at -O0 through -Os, with English and Turkish descriptions (Chinese is planned). What’s missing is listed step by step on one page: https://asmsense.luxurycashew.com/data/ Code and data: https://github.com/krxi/asmsense In short: other architectures (ARM64, RISC-V, MIPS), GCC/ELF builds (so far everything is clang/Mach-O on macOS), Chinese descriptions, human-checked labels, agent trajectories, and runs on public benchmarks like BinMetric, REBench, BinSum and DeBinVul. I’m thinking about turning the pipeline into an RL environment: compile, strip, let the model name the function, and score it against the real name with token-level F1. That reward is easy to game, though: a model can collect partial credit with generic names like get_data. If you’ve trained on a fuzzy target like this, how did you shape the reward, and did RL beat plain fine-tuning? Two other questions: A note on LLM use: the descriptions were written by teacher LLMs that could see the C source, and only a sample was checked, by another LLM. I also used LLMs while building this and checking my writings to check grammar, so there may be small mistakes; corrections are welcome. https://preview.redd.it/bplp468vr7wh1.png?width=1822&format=png&auto=webp&s=1b39946680aa8e3df218ccb688551c658b92f741 submitted by /u/luxurycashew [link] [comments] |