
Kahaan developed and refined advanced evaluation and visualization workflows for the CMBAgents/cmbagent repository, focusing on Vision-Language Model (VLM) judging pipelines and agent-driven plot analysis. Leveraging Python, Jupyter Notebooks, and GLSL, Kahaan implemented a structured verdict schema, automated batch experiments, and integrated shader-based map rendering to improve both performance and reproducibility. The work included refactoring agent interactions to centralize verdict processing, enhancing maintainability and testability. By standardizing evaluation outputs and automating experiment workflows, Kahaan addressed challenges in reproducibility and data integrity, demonstrating depth in AI integration, context management, and scientific data visualization within a complex, research-driven codebase.

Concise monthly summary for 2025-08 focusing on CMBAgents/cmbagent: key feature delivered, impact, and skills demonstrated.
Concise monthly summary for 2025-08 focusing on CMBAgents/cmbagent: key feature delivered, impact, and skills demonstrated.
July 2025 monthly summary for CMBAgents/cmbagent: Delivered a cohesive VLM-based judging workflow with scalable routing, verdict management, and hands-on review tooling. Established a structured, machine-readable verdict schema and mandatory fields to ensure consistent handoffs. Enhanced the VLM evaluation harness with expanded evaluators, failure categorization, and domain-specific rubrics, plus injection-based perturbations to stress-test robustness. Produced agent-facing instructions and notebook examples to enable reproducible VLM-as-a-judge experiments. Implemented AstroVizBench automation to drive UID-based batch experiments, accelerating experimentation and benchmarking. Conducted notebook and ground-truth standardization work to align evaluation plots with CMB tasks. Resolved key repository hygiene issues, including merge conflicts in the one-shot work directory, improving stability for ongoing development.
July 2025 monthly summary for CMBAgents/cmbagent: Delivered a cohesive VLM-based judging workflow with scalable routing, verdict management, and hands-on review tooling. Established a structured, machine-readable verdict schema and mandatory fields to ensure consistent handoffs. Enhanced the VLM evaluation harness with expanded evaluators, failure categorization, and domain-specific rubrics, plus injection-based perturbations to stress-test robustness. Produced agent-facing instructions and notebook examples to enable reproducible VLM-as-a-judge experiments. Implemented AstroVizBench automation to drive UID-based batch experiments, accelerating experimentation and benchmarking. Conducted notebook and ground-truth standardization work to align evaluation plots with CMB tasks. Resolved key repository hygiene issues, including merge conflicts in the one-shot work directory, improving stability for ongoing development.
June 2025 performance highlights for CMBAgents/cmbagent: Key features delivered, major bugs fixed, and sustained business value through robust evaluation pipelines and visualization improvements.
June 2025 performance highlights for CMBAgents/cmbagent: Key features delivered, major bugs fixed, and sustained business value through robust evaluation pipelines and visualization improvements.
Overview of all repositories you've contributed to across your timeline