Thinking Once is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA

High-resolution visual question answering (HR-VQA) is often treated as a problem of insufficient evidence acquisition, where failing multimodal large language models must inspect images again through cropping, re-encoding, or multi-round search. We show that this view is incomplete: in many cases, fine-grained evidence has already survived visual encoding and become identifiable and influential within an intermediate-layer routing window, but is later diluted before answer generation. We propose Thinking-Once, a **training-free, single-visual-pass** evidence-routing method that reconstructs question-conditioned attention at this window, preserves core entity tokens and compact background context, and routes this evidence to later layers without extra visual encoding. Across five base models, Thinking-Once consistently improves or matches the corresponding base setting, increasing the average scores on $V^{\ast }$Bench, HRBench-4K, and HRBench-8K by $+3.1$, $+3.0$, and $+2.7$ points while reducing the average peak memory by about $4\mathrm{GB}$. On Qwen2.5-VL-7B, it improves the three benchmarks by $+9.9$, $+4.6$, and $+5.5$ points, raising the cross-benchmark mean from $72.5$ to $79.1$. With the ZwZ-8B base model, Thinking-Once reaches a mean score of $82.7$. Against 11 open-source HR-VQA baselines, it obtains the best or tied-best score on all three benchmark averages and the best overall mean; for example, compared with DeepScan, it reduces $V^{\ast }$Bench inference time by **$97.2$%** while improving the cross-benchmark mean from $77.8$ to $79.1$. These results show that HR-VQA can be improved by routing already encoded evidence rather than repeatedly acquiring new visual inputs. Code is available in the appendix.