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MMDynOpt-Agent is a lightweight multimodal agent that models the dynamic optimization of multimodal reasoning as a Markov decision process via end-to-end reinforcement learning, designed to reduce the cost of multimodal reasoning.
TIEM, a timestamp-gated framework with three coordinated components: an Event-Evidence Hypergraph (EEH) for timestamp-filtered multi-tier retrieval; a Case-based Skill Memory (CSM) for source-tagged temporal skills; and Heterogeneous Evidence-Experience Fusion Reasoning (HEFR) for evidence-experience fusion and prediction.
SEED (SElf-Evolving On-Policy Distillation), a self-evolving framework that converts completed on-policy trajectories into training-time hindsight skills and distills their behavioral effect back into the policy model, is proposed.
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