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State-Matched Routing and Contextualized Self-Distillation boosts task success rates by over 15% in complex interactive environments by aligning guidance with the agent's actual execution state.
MLLMs can now refuse to localize non-existent objects without sacrificing their accuracy on valid requests, thanks to a novel reinforcement learning strategy.
