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arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆arXiv — cs.AIMedHopQA benchmark tests LLM reasoning in biomedical Q&A◆arXiv — cs.CLRouters learn geometry of sparse mixture-of-experts◆arXiv — cs.AIStudy audits how LLMs generate political discourse during crises◆arXiv — cs.AIClassifier Context Rot: Monitor Performance Degrades with Context◆arXiv — cs.AIExecutable Agentic Memory for GUI Agent◆arXiv — cs.CLLongMemEval-V2: Evaluating Long-Term Agent Memory Toward Experienced◆arXiv — cs.AISparse-to-dense rewards improve language model post-training◆arXiv — cs.AIAI-native mobility dataset advances 6G handover and beam management◆
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§ tools · cluster

vLLM v0.18.0

Mar 20 · · primary fetch1 sourcecluster e1df2712updated Mar 20 ·

vLLM v0.18.0 Known issues Degraded accuracy when serving Qwen3.5 with FP8 KV cache on B200 (#37618) If you previously ran into `CUBLAS_STATUS_INVALID_VALUE` and had to use a workaround in `v0.17.0`, you can reinstall `torch 2.10.0`. PyTorch published an updated wheel that addresses this bug. Highlights This release features 445 commits from 213 contributors (61 new)! gRPC Serving Support: vLLM now supports gRPC serving via the new `--grpc` flag (#36169), enabling high-performance RPC-based serving alongside the existing HTTP/REST interface. GPU-less Render Serving: New `vllm launch render` command (#36166, #34551) enables GPU-less preprocessing and rendering, allowing separation of multimodal preprocessing from GPU inference.

NGram GPU Speculative Decoding: NGram speculative decoding now runs on GPU and is compatible with the async scheduler (#29184), significantly reducing spec decode overhead. KV Cache Offloading Improvements: Smart CPU offloading that stores only frequently-reused blocks (#35342), plus FlexKV as a new offloading backend (#34328) and support for multiple KV groups in offloading spec (#36610). Elastic Expert Parallelism Milestone 2: NIXL-EP integration (#35627)…

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  1. github.comvllm v0.18.0primary