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Overall, assistant-generated history has active but selective effects on multi-turn performance, motivating selective rather than uniform history management.
This paper investigates the influence of task-irrelevant context by formulating it as a controlled intervention within a binary visual judgment framework and reveals a robust geometric regularity: contextconditioned margins follow a consistent affine transformation of their context-free counterparts.
The results indicate that for precise, evidence-grounded questions over chat archives, much of the benefit credited to elaborate memory structures is recoverable by giving an agent controllable search over the unmodified record, with no LLM-based index construction at all.
Extensive experiments show that Groc-PO achieves improved performance in hallucination mitigation, faithful reasoning, and overall reliability, supporting the value of more explicit grounded supervision for trustworthy multimodal reasoning.
This work provides an attention-based perspective on mitigating hallucinations by exploring the model's internal text-to-image cross-attention behaviors, and proposes ADAPT, an attention-based framework that intervenes directly on text-to-image cross-attention dynamics.
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