Hydra: Unifying Document Retrieval and Generation in a Single Vision-Language Model
Visual document understanding typically requires separate retrieval and generation models, doubling memory and system complexity. We present Hydra, a dual-head approach that provides both ColBERT-style late-interaction retrieval and autoregressive generation from a single vision-language model. A single LoRA adapter, trained only for retrieval, is toggled at inference: enabling it produces multi-vector embeddings; disabling it recovers the base model's generation quality, with 426 of 426 language-model weight tensors byte-for-byte identical to a freshly-loaded Qwen3.5-4B. We identify two failure modes that can silently break generation in retrieval-fine-tuned VLMs (attention-mode restorat...
| Authors | Athos Georgiou |
|---|---|
| Primary category | cs.CV |
| Published | 30 March 2026 |
| Last updated at source | 19 April 2026 |
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