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This paper proposes a sliding-window-based reinforcement learning framework (SWRL) for end-to-end online scheduling in dynamic assembly flow shop scheduling with complex kitting constraints, demonstrating consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods on real-world instances.
This paper proposes Kara, a sliding-window KV cache compression method for efficient serving of reasoning LLMs, addressing limitations in existing compression techniques by using bidirectional attention and a Token2Chunk module. The method is integrated into the KvLLM inference framework built upon vLLM, improving output throughput while maintaining performance.
Tensor Cache introduces a two-level caching mechanism that compresses evicted key-value pairs from sliding-window attention into a fixed-size associative memory, improving long-context language modeling without unbounded memory growth.