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RedKnot establishes a new foundation for AI infrastructure by transforming the KV cache from a monolithic, passive runtime artifact into a dynamic, model-aware runtime substrate for scalable LLM serving.
This work presents a DeepSeek-V4 realization of RedKnot's head-aware reuse principle, a DeepSeek-V4 realization of RedKnot's head-aware reuse principle, and describes the factorization, position repair, token-row closure, sparse-MoE support, TP8 integration, and the measurement boundaries needed to interpret these resu...
GDPevo is presented, an evolution-native benchmark grounded in GDP-related enterprise workflows, together with the fully automated data pipeline that generates it, and the best evolved agents remain far below the fully informed oracle ceiling, indicating that the self-evolution ability of current agents remains far fro...
CoRun is presented, a scheduling-based system that achieves deterministic inference without requiring batch invariance, and employs isolated prefill and fixed-shape batched decode to handle the two stages of LLM inference, respectively, leveraging CUDA graphs for efficient execution and simplified implementation.
This work proposes Akashic, a low-overhead memory system built around MemAttention, which organizes context into bounded chunks and models semantic relationships across chunks, preserving cross-chunk evidence without repeatedly rewriting the full history.
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