Static importance scores compress visual evidence into a single ranking, but the value of remaining evidence can change after one cue has been observed. We study this possibility in controlled compositional visual search, where color, shape, and texture evidence can be independently exposed and their conditional marginal utility measured across acquisition states. In a held-out confirmation on 800 scenes, frozen OpenCLIP and SigLIP exhibit robust state-dependent rank reversals that concentrate in candidate-overlap regimes designed to induce ordering changes. The structure persists across two evidence-accumulation constructions and ten equivalent query wordings, but disappears under query-scene derangement. We also ask whether these reversals matter for decisions. In a post-confirmation exploratory matched-first-action analysis, reranking only after the first acquisition yields positive step-2 utility when decisions are selected under one evidence mode, wording, or backbone and evaluated under another. Together, these results show that evidence importance is state-dependent in this controlled setup and that updating an evidence ordering can retain decision-relevant value across evaluator changes. They motivate evaluating vision-language evidence use conditionally rather than through a single static ranking, while providing a measurable target for future adaptive evidence-selection methods.
In batch-style trace distillation, the same memory operation may receive contradictory feedback across different batches. Existing methods lack a cross-batch, operation-level evidence accumulation mechanism, making it impossible to distinguish stably effective operations from accidental hits. This paper formalizes the requirement as two structural conditions, alignability and comparability, and proposes Marginal Advantage Accumulation (MAA). MAA constructs differential signals to make them comparable across batches, accumulates signed evidence per operation via EMA, and ensures cross-batch traceability through semantic identity merging. As a post-processing architecture, MAA achieves the best results in 14 out of 16 settings across 4 benchmarks and 4 target models, consistently outperforming existing batch-level distillation baselines and matching or surpassing online alternatives in most settings, while reducing optimization-phase token consumption by approximately 75%.