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HEAR is established as a reusable coordination substrate for efficient agentic LLM serving by separating protocol semantics from optimization policies, and Mooncake shows that workflow intent and live engine state provide complementary benefits across load regimes.
Atria Dawn Preview is introduced, a foundation agentic language model designed for scientific research and engineering workflows, with the goal of expanding the frontier of agent productivity in the real world.
This work uses the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, and introduces the RSI concept and its development roadmap: from improvement-execution autonomy, improvement-strategy autonomy, experience-acquisition autonomy, and environment-adaptation autonomy, to recursive meta-improvement.
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