When a vision-language model (VLM) is shown an irrelevant image containing a digit (the anchor image) alongside a numeric question, the model’s numeric answer shifts toward that digit. This matters because multi-image inputs (retrieval-augmented QA, multi-screenshot analysis, VLM-as-judge) routinely contain irrelevant numeric content. We call this phenomenon cross-modal numerical anchoring: across six VLMs and five numeric VQA datasets, the per-model mean SDF (signed direction-follow rate) ranges from 3% to 24%, with the effect growing stronger as the model’s confidence in the base answer decreases. Masking only the digit pixels of the anchor image, leaving the rest of the image intact, recovers the non-anchored answer, isolating the digit pixels as the causal channel, with the effect concentrated at low confidence. We propose a mitigation that uses this anchored-masked contrast as a calibration signal: at a single per-model integration site, we estimate a low-rank subspace spanning the anchoring directions and remove it via a projection at inference time. On LLaVA-OneVision-7B, the projection reduces anchoring (𝛥SDF_a=-1.64 pp, rel% -30.0%) without degrading accuracy on non-anchored inputs or average performance on six capability benchmarks (within ±1 pp).