Margin-Regularized Structured Semantic Alignment for Brain-Language Correspondence
With the rapid advancement of large language models, brain-language decoding has achieved remarkable progress. However, it remains unclear whether decoded content genuinely reflects neural representations or is largely reconstructed by the language model itself. This ambiguity limits interpretability and hinders the investigation of intrinsic brain-language correspondence. To address this challenge, we propose MD-SigLIP. This margin-regularized structured semantic alignment framework directly aligns brain embeddings with text embeddings in a shared semantic space, enabling retrieval-based decoding. This formulation enables explicit modeling of the correspondence between neural representat...
| Authors | Jiaqi Wang, Huawen Hu, Shu Zhang |
|---|---|
| Primary category | cs.CL |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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