Daily Report — 2026-02-03
Daily Overview
- What was done: Restored incomplete benchmark tracking by regenerating visualization files, PDF reports, and comprehensive markdown logs across three DLPFC sections.
- How it was done: Executed Python-based clustering scripts for all model variants, compiled outputs into a structured PDF, and directly parsed raw CSV metrics to rebuild the master experiment log without manual intervention.
- Impact: Eliminated documentation drift, solidified PCA+UNI2+STAIG fusion as the validated best-practice configuration, and ensured future benchmark runs are automatically auditable from execution through reporting.
Recovered missing benchmark documentation and visual outputs for the MIHD multi-modal fusion project, confirming optimal model configurations and restoring full experiment traceability.
Tasks
Implementation & Fixes
- ✅ Benchmark Visualization Recovery — Ran clustering visualization scripts for sections 151508, 151673, and 151676, compiled multi-panel models comparison plots, and generated a comprehensive reporting PDF.
- ✅ Experiment Log Restoration — Parsed the raw
experiment_comparison.csvcontaining 36 successful runs and dynamically generated a complete, chronologicalexperiments_log.mdto replace the fragmented markdown file.
Problems & Solutions
Critical Issues
1. Critical tracking gap where high-performing benchmark results (ARI ~0.45-0.52) existed in CSV raw data but were absent from experiments_log.md, with no PDF reports generated despite successful script executions.
Solution: Diagnosed the logging divergence, re-executed visualization pipelines across all target sections, compiled them into a formal PDF report, and automated log regeneration directly from the source CSV metrics.
Key Insight: Raw CSV artifacts should serve as the single source of truth for documentation; relying on manual markdown updates causes irreversible data asymmetry between execution outcomes and audit trails.
Human vs AI Approaches
Strategic Level
Benchmark Result Integrity Verification
| Role | Approach |
|---|---|
| Human | Questioned the absence of visual reports despite awareness of strong ARI scores, prioritizing workflow validation and documentation integrity over re-running expensive hardware experiments. |
| AI | Proactively scanned file systems and logs to confirm data existence, then designed and executed a full synthesis pipeline (visualization execution, PDF compilation, CSV parsing) to resolve the tracking gap autonomously. |
Difference Analysis: Human demonstrated strategic oversight by identifying the documentation-execution mismatch early, while AI leveraged strong diagnostic automation capabilities to instantly bridge the gap through script orchestration and data extraction rather than manual verification.
AI Limitations
General Limitations
- AI processed background visualization tasks sequentially without proactively checking for prior successful completions, leading to minor compute redundancy and requiring explicit user polling to confirm task resolution.
Learnings
Key Learnings
- Benchmark workflows must decouple documentation generation from execution logs by directly consuming raw metric CSVs, preventing manual logging drift and ensuring real-time auditability without workflow interruption.
Conversation Summaries
✅ Benchmark Visualization & Log Recovery for MIHD 03:31:19.374 | claude_code User inquired about missing visualization outputs and incomplete experiment logs despite successful benchmark runs recorded in CSV data. AI diagnosed the tracking discrepancy, executed Python scripts to regenerate clustering plots across three DLPFC sections, compiled a comprehensive PDF report, and reconstructed the master markdown log directly from raw metrics. Key analytical findings highlighted the PCA+UNI2+STAIG fusion configuration as optimal with ARI improvements exceeding 120% over baseline encoders.