Where Hallucinations Live: A Cross-Architecture Circuit in VQ-Tokenized Vision-Language Models
Unified vision-language models (VLMs) that tokenize images through a vector-quantized (VQ) codebook routinely hallucinate objects on grounded yes/no benchmarks, yet existing decoding-time fixes treat this as generic miscalibration without an architectural account. Using activation patching across twenty-five models spanning eight LLM families, we identify an early-layer ($L_0$) attention routing circuit shared across VQ-tokenized VLMs and propose a three-gate diagnostic that distinguishes the models carrying it from those that do not. The diagnostic isolates ten positive models (five natural unified-VQ VLMs across three LLM families and five induced variants) and rejects the remaining fif...
| Authors | Shamanthak Hegde, Xiangrui Liu, Maitreya Patel et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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