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Adaptive Memory via Multi-Agent Collaboration (AMA), a novel framework that leverages coordinated agents to manage memory across multiple granularities, significantly outperforms state-of-the-art baselines while reducing token consumption by approximately 80% compared to full-context methods.
A benchmark and training framework for shortcut-resistant cross-scale pathology reasoning, and an Adversarial Text-only Screening strategy for semantic reasoning questions and a Structure-controlled Distractor Sampling strategy for visual grounding questions, encouraging models to rely on cross-scale visual evidence.
A memory-free recent-window protocol is fixed and a transparent and reproducible reference for open-source streaming-video research is provided, combining verifiable streaming-video data, thinking-mode OPD, and instruct-mode deployment.
Unified-MAS is proposed that decouples granular node implementation from topological orchestration via offline node synthesis via offline node synthesis and is robustness across different designer LLMs and its effectiveness on conventional tasks such as mathematical reasoning.
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