
Qianyy contributed to the hao-ai-lab/hao-ai-labhub.io.git repository by developing and documenting the D3LLM diffusion-based language model framework, focusing on speed and accuracy improvements through pseudo-trajectory distillation and multi-block decoding. Using JavaScript and HTML, Qianyy enhanced data visualization and front-end rendering, updating figures, tags, and mobile styling to support clearer performance benchmarks. They addressed reliability in evaluation by fixing HumanEval statistics reporting, ensuring accurate metrics. In addition, Qianyy improved the clarity and accessibility of research references by refining documentation and citations, demonstrating depth in technical writing and research integration throughout the project’s lifecycle over the two-month period.
January 2026 (2026-01) monthly summary for hao-ai-lab/hao-ai-labhub.io.git. Focused on documentation improvements for d3LLM citations; no major bugs fixed this month. Primary deliverable was enhanced accuracy, clarity, and accessibility of research references.
January 2026 (2026-01) monthly summary for hao-ai-lab/hao-ai-labhub.io.git. Focused on documentation improvements for d3LLM citations; no major bugs fixed this month. Primary deliverable was enhanced accuracy, clarity, and accessibility of research references.
December 2025 monthly summary: Delivered the D3LLM diffusion-based framework with speed and accuracy improvements via pseudo-trajectory distillation and multi-block decoding, accompanied by comprehensive documentation and visuals. Reworked blog/project visuals, rendering, tags, and ensured performance-documented results. Fixed HumanEval statistics reporting to ensure reliable metrics. Strengthened repo quality through updated docs, figures, and render assets to support faster onboarding and data-driven decisions.
December 2025 monthly summary: Delivered the D3LLM diffusion-based framework with speed and accuracy improvements via pseudo-trajectory distillation and multi-block decoding, accompanied by comprehensive documentation and visuals. Reworked blog/project visuals, rendering, tags, and ensured performance-documented results. Fixed HumanEval statistics reporting to ensure reliable metrics. Strengthened repo quality through updated docs, figures, and render assets to support faster onboarding and data-driven decisions.

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