FIRA: Fixed-Support Intervention and Readout Audit for Visual Token Pruning

A stable visual-token ranking can coexist with failures shared by the compared selectors. Fixed-Support Intervention and Readout Audit (FIRA) holds selected positions fixed while recording actual outputs and candidate-restricted decisions. On 800 A-OKVQA images, changing the readout reduces historical LLaVA delete–zero ranking interactions by 19.625 points in NF4 and 18.625 in FP16. Qwen2.5- VL with MMTok/DivPrune supports instead exhibits shared failures that largely cancel in selector comparisons. Native OCRBench-v2 text-image choices provide 536 questions and seven complete arms. On 429 confirmation questions, full-input accuracy is 68.30%, the shared excluded-correct shift is 19.35 [15.81,23.10] points and the differential shift is 3.73 [-1.18,8.51], with 97.5% paired-image intervals. The task meets its frozen difficulty criterion. Another 7,504 decisions confirm positive shared shifts at 25/75% retention. Saved traces from 194 receipts reveal different multi-token candidate decisions in 59/970 Qwen and 116/970 Monkey OCR cells. These results motivate reporting absolute answer outcomes alongside relative rankings under explicit answer contracts.