July 2026 monthly summary for jeejeelee/vllm: Implemented Reasoning Token Suppression for the Chat API. Introduced an include_reasoning parameter for Chat Completions and Responses APIs to suppress reasoning tokens in outputs, while preserving tokens necessary for model quality. This reduces network traffic without impacting output fidelity. The work covers both streaming and non-streaming paths with comprehensive end-to-end and unit tests.

June 2026 monthly summary for mistralai/llm-d-inference-scheduler-public: Delivered multi-prompt completion scoring for string array prompts, enabling independent scoring per prompt and boosting inference scheduler accuracy and throughput. Refactored tokenization and data models to PerPromptTokens, replacing TokenIDs and dropping FlatTokenIDs, improving memory efficiency and clarity in multi-prompt workflows. Hardened reliability with fail-open behavior on partial string-array tokenization, strengthened tests with multi-prompt coverage, and fixed a range of scoring and payload assembly issues to stabilize operation. Achievements include hashing prompts independently in the approximate prefix scorer, delegating multi-string prompt assembly to completionsPayload, returning per-prompt token arrays from Render interface, and performance improvements such as avoiding concat allocations in prerequest path. These changes deliver business value by enabling flexible, lower-latency multi-prompt workloads and reducing operational risk.

February? No, month is 2025-11 per input. This monthly summary covers IBM/vllm work for 2025-11. Key focus: a single feature delivery for ARC eviction policy in the CPU offloader, with tests and integration into the offloading stack. Key features delivered: - Adaptive Replacement Cache (ARC) eviction policy integration for the CPU offloader in vLLM. This balances recency and frequency to improve cache management and CPU offloading performance under varying workloads. Implemented as part of the CPU offloading manager and aligned with existing cache KV structures. Major bugs fixed: - No major bugs reported for IBM/vllm in this period based on available data. The work included tests to validate ARC eviction behavior, helping prevent regressions. Overall impact and accomplishments: - Improved cache efficiency and predictability of CPU offloading, reducing cache misses and improving throughput for large language model inference workloads. - Demonstrated end-to-end capability to introduce cache eviction policies in a high-performance offloader, with tests and production-ready integration. Technologies/skills demonstrated: - Cache eviction policy design (ARC) and integration with CPU offloading workflow - KV cache eviction policy implementation and testing - Git-based change management and traceability (commit bac904565f170ba198c2398a0f627b38f9cb8e18, PR reference #27039) - Test-driven validation of new eviction policy