ECLAR: Event-Conditioned Visual Token Repair for Low-Light Vision-Language Models

Low-light vision-language understanding depends on recovering the visual representations that support correct answers. We introduce ECLAR (Event-Conditioned Latent Alignment and Recovery), which uses synchronized events to repair RGB patch states inside a frozen vision encoder. Four residual adapters align representations with paired clean targets, retaining pretrained readout modules and the visual token count. Inference uses only low-light RGB and events. SDE-VQA-Bench pairs 8,357 reviewed questions with 1,838 frames from 91 sequences to evaluate representation and semantic recovery. With 4.89M parameters trained on SEE-600K, ECLAR leads four final-token recovery metrics against the evaluated restoration cascades, reaching 0.7817 cosine similarity to clean tokens. It improves frozen Qwen3-VL-2B by 11.91 points in six-family Macro and exceeds the strongest cascade by 4.19 points, recovering 57.2% of the clean-to-degraded gap. Matched events add 2.58 points over a capacity-matched RGB-only control; InternVL2.5-2B gains 4.41 points. These results connect clean-token alignment with recovery of both visual representations and downstream semantic performance.