Large Language Models (LLMs) trained on massive code corpora are now increasingly capable of generating code that is hard to distinguish from human-written code. This raises practical concerns, including security vulnerabilities and licensing ambiguity, and also motivates a forensic question: 'Who (or which LLM) wrote this piece of code?' We present GoCoMA, a multimodal framework that models an extrinsic hierarchy between (i) code stylometry, capturing higher-level structural and stylistic signatures, and (ii) image representations of binary pre-executable artifacts (BPEA), capturing lower-level, execution-oriented byte semantics shaped by compilation and toolchains. GoCoMA projects modality embeddings into a hyperbolic Poincaré ball, fuses them via a geodesic-cosine similarity-based cross-modal attention (GCSA) fusion mechanism, and back-projects the fused representation to Euclidean space for final LLM-source attribution. Experiments on two open-source benchmarks (CoDET-M4 and LLMAuthorBench) show that GoCoMA consistently outperforms unimodal and Euclidean multimodal baselines under identical evaluation protocols.

翻译：大型语言模型（LLM）经过海量代码语料训练后，所生成的代码越来越难以与人工编写的代码区分。这引发了实际层面的担忧，包括安全漏洞和许可模糊性问题，也催生了一个取证问题：“这段代码是谁（或哪个大语言模型）写的？”我们提出GoCoMA——一个多模态框架，该框架对（i）代码文体学（捕捉高层结构与风格特征）和（ii）二进制预执行工件（BPEA）的图像表示（捕捉由编译和工具链塑造的低层面向执行的字节语义）之间的外在层级进行了建模。GoCoMA将模态嵌入投影到双曲庞加莱球体中，通过基于测地余弦相似度的跨模态注意力（GCSA）融合机制对其进行融合，并将融合后的表示反向投影回欧几里得空间，以实现最终的LLM来源归属。在两个开源基准测试（CoDET-M4和LLMAuthorBench）上的实验表明，在相同的评估协议下，GoCoMA始终优于单模态和欧几里得多模态基线方法。