Weekly Report — 2026-W19 (2026-05-04 ~ 2026-05-10)

This week was a multifaceted period of high-stakes preparation and technical stabilization. Key achievements included meeting NeurIPS 2026 compliance requirements for ErrorRecoveryBench through metadata generation and data validation, repairing critical errors in research paper data, and stabilizing the LeRobot dataset by resolving video corruption issues. Additionally, significant progress was made in the TokenMonitor project with version releases and architecture refactoring, alongside completed deep-dive analyses of the AdaptVision codebase and planned system maintenance for environment migration.

Weekly Overview

Metric	Value
Date Range	2026-05-04 ~ 2026-05-10
Active Days	7 / 7
Total Conversations	19
Projects	12
Tasks Completed	49
Tasks In Progress	2
Total Tokens	1,929,559,195
Total Cost	$1,794.96
Claude Code Token	1,481,708,943
Claude Code Cost	$1,367.30
Codex Token	447,850,252
Codex Cost	$427.66
Daily Average Cost	$256.42

Project Progress

TokenMonitor (3 days active) — 🔄 active

Accomplishments:

• Successfully executed a hybrid upstream merge preserving local OAuth/Cursor/Codex features
• Released v0.13.0/v0.13.1 with statusline-first rate limit architecture
• Resolved critical Windows Codex path resolution and NVIDIA PCIe power-state crash issues
• Refactored Settings UI for multi-provider support

ErrorRecoveryBench (NeurIPS Submission) (1 days active) — 🔄 active

Accomplishments:

• Generated Croissant metadata for NeurIPS E&D compliance
• Downloaded 30GB dataset from tianhe server
• Corrected critical paper data errors (BC-RNN SR)
• Created publication-quality figures (Fig 4-7)

Blockers:

• ⚠️ Diagnosing MimicGen segmentation and filter failures

LeRobot Dataset & Error Recovery (1 days active) — 🔄 active

Accomplishments:

• Developed diagnostic and repair scripts for corrupted MP4 files
• Implemented robust error handling and blacklisting in PairedDataset
• Re-encoded corrupted episodes from original HDF5 sources

AdaptVision Analysis (1 days active) — ✅ completed

Accomplishments:

• Mapped entire training and evaluation flow
• Traceability of PPO reward logic (outcome vs turn-level scores)

System Maintenance (1 days active) — 🔄 active

Accomplishments:

• Exported 4 conda environments for migration
• Prepared Miniconda installation for D: drive relocation

Key Tasks

• ✅ NeurIPS 2026 Compliance & Data Integrity — Generated Croissant metadata, corrected critical BC-RNN data errors in Table 6 (fixing 167% error discrepancy), and transferred 30GB of release data for hosting.
• ✅ LeRobot Video Data Repair & Pipeline Optimization — Identified and repaired 60 corrupted episodes via HDF5 re-encoding and implemented robust error handling/blacklisting in PairedDataset.
• ✅ AdaptVision Codebase & Reward Logic Mapping — Mapped PPO training pipelines and reward calculation mechanisms for adaptive visual acquisition.
• ✅ TokenMonitor Architecture & Release — Released v0.13.0/v0.13.1, refactored Settings UI, and resolved critical Windows/NVIDIA hardware-related crashes.
• ✅ Scientific Visualization — Produced publication-quality figures (4-7) with standardized typography and color schemes.

Problems & Solutions

1. BC-RNN Normal SR severely wrong in paper (21% vs actual 56%) [ErrorRecoveryBench]

Solution: Cross-verified all table values against fact-check JSON from validation runs and split into two policy rows.

2. RuntimeError: InvalidDataError during decoding augmented video frames [LeRobot Dataset]

Solution: Created repair scripts to re-encode bad episodes from HDF5 and implemented dynamic try-except/masking in the DataLoader.

3. Hardcoded API keys found in AdaptVision’s README.md [AdaptVision]

Solution: Identified for remediation and suggested using environment variables.

4. JAX JIT compilation prevented conditional skipping of corrupted samples [LeRobot Dataset]

Solution: Ensured loss contribution was masked to zero to respect static graph requirements.

5. Windows Codex path resolution and NVIDIA PCIe power-state crashes [TokenMonitor]

Solution: Resolved critical pathing issues and driver-level power state conflicts.

Learnings

Domain Knowledge (domain)

• Fact-checking experimental results against raw JSON outputs is mandatory; paper values can drift significantly from actual measurements.
• In custom RL, reward engineering logic is often distributed; tracing data flow is more effective than reading config alone.

Debugging (debugging)

• Corrupted videos may pass header checks but fail during PyAV decoding; explicit decoding is required for thorough diagnosis.

Tools (tools)

• Conda environments should be migrated via export/rebuild rather than folder copying to avoid hardcoded path issues.

AI Usage Notes

Effective Patterns:

• ✓ Using AI to trace complex code execution paths from high-level scripts down to specific reward functions.
• ✓ Leveraging AI for security audits (detecting hardcoded keys) and documentation optimization.

Limitations:

• ✗ AI lacks domain intuition for robotic manipulation pipeline debugging (e.g., not noticing algorithm version incompatibilities).
• ✗ AI tends to trust existing LaTeX/document content without independent verification of raw data.
• ✗ AI attempts to use unsupported/unprivileged shell tools (like ‘rg’ in sandboxed environments) without immediate fallback.

Next Week Outlook

Prioritize the completion of the Conda migration to the D: drive. Focus on resolving the MimicGen segmentation and filter failures to improve success rates. Finalize the NeurIPS submission by ensuring all dataset metadata and hosting requirements are fully met.

Token Usage Statistics

Peak Day: 2026-05-06 — $695.86 / 692.4M tokens

Daily Average: $256.42