# GoCoMA: Hyperbolic Multimodal Representation Fusion for Large Language Model-Generated Code Attribution

arXiv: https://arxiv.org/abs/2604.16377 · HTML: https://mlsanity.com/paper/2604.16377/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 28 of its 39 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Poincar'e Embeddings for Learning Hierarchical Representations](https://mlsanity.com/paper/1705.08039/) (2017)
- [MaxViT: Multi-Axis Vision Transformer](https://mlsanity.com/paper/2204.01697/) (2022)
- [Hyperbolic Neural Networks](https://mlsanity.com/paper/1805.09112/) (2018)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](https://mlsanity.com/paper/2406.10279/) (2024)
- [Revisiting the Robustness of Watermarking to Paraphrasing Attacks](https://mlsanity.com/paper/2411.05277/) (2024)
- [De-mark: Watermark Removal in Large Language Models](https://mlsanity.com/paper/2410.13808/) (2024)
- [When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries](https://mlsanity.com/paper/1512.08546/) (2015)
- [MalNet: A Large-Scale Image Database of Malicious Software](https://mlsanity.com/paper/2102.01072/) (2021)
- [Detecting Stylistic Fingerprints of Large Language Models](https://mlsanity.com/paper/2503.01659/) (2025)
- [CoDet-M4: Detecting Machine-Generated Code in Multi-Lingual, Multi-Generator and Multi-Domain Settings](https://mlsanity.com/paper/2503.13733/) (2025)
- [Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code](https://mlsanity.com/paper/2502.18851/) (2025)
- [Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineering](https://mlsanity.com/paper/2001.11593/) (2020)
- [I Know Which LLM Wrote Your Code Last Summer: LLM generated Code Stylometry for Authorship Attribution](https://mlsanity.com/paper/2506.17323/) (2025)
- [BinMLM: Binary Authorship Verification with Flow-aware Mixture-of-Shared Language Model](https://mlsanity.com/paper/2203.04472/) (2022)
- [Is This You, LLM? Recognizing AI-written Programs with Multilingual Code Stylometry](https://mlsanity.com/paper/2412.14611/) (2024)
- [Stack Overflow: A Code Laundering Platform?](https://mlsanity.com/paper/1703.03897/) (2017)
- [HYFuse: Aligning Heterogeneous Speech Pre-Trained Representations in Hyperbolic Space for Speech Emotion Recognition](https://mlsanity.com/paper/2506.03403/) (2025)
- [Detection of LLM-Generated Java Code Using Discretized Nested Bigrams](https://mlsanity.com/paper/2502.15740/) (2025)
- [Reassessing Code Authorship Attribution in the Era of Language Models](https://mlsanity.com/paper/2506.17120/) (2025)

## Abstract (arXiv)

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 Poincar'e 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.