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Adaptive Relevance-guided Evidence Allocation (AREA) is introduced, a training-free inference-time method that formulates evidence highlighting as adaptive allocation and establishes the best performance among training-free highlighting methods.
StackTok is introduced, a training-free selector that treats query relevance as the objective and visual coverage as budget-calibrated support in vision-language models and ranks first among training-free selectors in every tested model--budget setting.
LLMs struggle to effectively integrate and order evidence in attack chain reconstruction, with top models only succeeding 39.6% of the time on critical tasks.