 LAPS: A Length-Aware-Prefill LLM Serving System
 https://arxiv.org/abs/2601.11589


Understanding Bottlenecks for Efficiently Serving LLM Inference With KV Offloading
https://arxiv.org/abs/2601.19910



DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
https://arxiv.org/abs/2602.21548


DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving
https://www.usenix.org/system/files/osdi24-zhong-yinmin.pdf


Not All Prefills Are Equal: PPD Disaggregation for Multi-turn LLM Serving
https://arxiv.org/pdf/2603.13358


Efficient Multi-round LLM Inference over Disaggregated Serving
https://arxiv.org/pdf/2602.14516v1


 Roadmap: SGLang Distributed KVCache System For Agentic Workload
 https://github.com/sgl-project/sglang/issues/21846
 
 RFC: P/D Prefill compute optimizations with bi-directional KV cache transfers between P and D nodes
 https://github.com/vllm-project/vllm/issues/32733



- [Not All Prefills Are Equal: PPD Disaggregation for Multi-turn LLM Serving](https://arxiv.org/html/2603.13358v1?utm_source=chatgpt.com)
- [SWE-AGI: Benchmarking Specification-Driven Software Construction with MoonBit in the Era of Autonomous Agents](https://arxiv.org/html/2602.09447v1?utm_source=chatgpt.com)
- [DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference](https://arxiv.org/html/2602.21548v1?utm_source=chatgpt.com)
- [Prefill/Decode Disaggregation | llm-d](https://llm-d.ai/docs/guide/Installation/pd-disaggregation?utm_source=chatgpt.com)
- [CacheSlide: Unlocking Cross Position-Aware KV Cache Reuse for Accelerating LLM Serving | USENIX](https://www.usenix.org/conference/fast26/presentation/liu-yang?utm_source=chatgpt.com)

