2026-03 Monthly Summary — microsoft/onnxruntime-genai Key features delivered: - No new features released this period; focus on correctness and stability in OGA decoding. Major bugs fixed: - Fixed handling of past_seq_length in OGA decoding to ensure it is used as input to the model (Continuous Decoding #2008). Commit: 6e200ea0515de7efa4c60f66cb3069f0723f5a42. Overall impact and accomplishments: - Increased reliability and correctness of OGA decoding, reducing edge-case failures and improving downstream inference quality. - Strengthened model robustness with minimal churn and clear commit traceability. Technologies/skills demonstrated: - Debugging and root-cause analysis in model decoding pipelines - Version control discipline with precise commit traceability - Domain knowledge of OGA/GenAI decoding behavior and input handling

November 2025 monthly summary: Delivered an Olive-based post-training quantization (PTQ) workflow for the Facebook Segment Anything Model (SAM), enabling efficient QNN deployment and reduced inference costs in edge/resource-constrained environments. Implemented an Olive recipe and artifacts including configuration files, model patches, and a mask generator script to streamline PTQ integration and deployment. The work is recorded in commit 00cc1953d2b6abb413d7c29a6f2a767ebba0c7e1 (Olive Recipe for Facebook Segment Anything Models), co-authored by Shiva Chilukamari.