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The routed-state interface unifies contextual memory and persistent capability adaptation within the model's native computation, reframing memory from a passive record of prior context into an active substrate for maintaining and evolving model behavior.
Memory-Anchor Routing across Context History (MARCH), a network architecture that effectively scales state-space models beyond a fixed-size dimension, while maintaining computational efficiency over long-sequences, is introduced.
HFlow is proposed, an evolutionary framework for generating agentic workflows through human-agent collaboration that produces cost-effective and adaptive solutions, better aligning automated agen-tic systems with dynamic human needs.
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