Weekly Report — 2026-W21 (2026-05-18 ~ 2026-05-24)
This week was characterized by a high-intensity research phase focusing on Vision-Language-Action (VLA) models, transitioning from deep literature analysis to strategic roadmap synthesis. While significant progress was made in identifying architectural trends for precision and edge deployment, technical progress on the TokenMonitor project was bifurcated: successfully conducting a protocol audit but ultimately being halted by enterprise authentication blocks in the latter part of the week.
Weekly Overview
| Metric | Value |
|---|---|
| Date Range | 2026-05-18 ~ 2026-05-24 |
| Active Days | 5 / 7 |
| Total Conversations | 8 |
| Projects | 6 |
| Tasks Completed | 10 |
| Tasks In Progress | 0 |
| Total Tokens | 334,871,102 |
| Total Cost | $232.32 |
| Claude Code Token | 331,828,580 |
| Claude Code Cost | $228.56 |
| Codex Token | 3,042,522 |
| Codex Cost | $3.76 |
| Daily Average Cost | $46.46 |
Project Progress
VLA Research & Strategy (3 days active) — 🔄 active
Accomplishments:
- Analyzed top-tier VLA papers (SOMA, DISC, Dexora, etc.) focusing on spatial memory and architectural decoupling.
- Generated three distinct research directions: hybrid OT-based sensory fusion, layered extreme quantization for MCUs, and latency-adaptive frameworks.
- Developed a comprehensive technical writing and review guide based on high-impact paper patterns.
TokenMonitor (2 days active) — ⏸️ paused
Accomplishments:
- Conducted a protocol validation audit, identifying discrepancies between documentation and implementation in hook enforcement.
- Resolved critical Python import hijacking issues caused by sibling repository naming conflicts.
Blockers:
- ⚠️ Enterprise policy blocking Claude Code subscription access, preventing further coding tasks.
Key Tasks
- ✅ VLA Paper Analysis & Deep Research — Screened 80 papers and performed deep analysis on top-tier VLA architectures to extract reusable techniques and structural strengths. (2026-05-22)
- ✅ Protocol Validation Audit — Verified AGENTS.md claims against implementation to expose gaps in spec enforcement and verification gates. (2026-05-20)
- ✅ Strategic Research Direction Generation — Synthesized paper insights into actionable research paths covering spatial memory and architectural decoupling. (2026-05-22)
- ✅ VLA Precision Manipulation Research — Identified Spacetime Optimal Transport (SO-TA) and geometric safety manifolds as core methodologies for sub-millimeter control. (2026-05-23)
- ✅ Lightweight VLA Edge Deployment Research — Analyzed compression techniques like FTerViT and modular architectures for embedded/MCU deployment. (2026-05-23)
- 🚫 Resolve Claude Code Authentication — Investigate and obtain Anthropic API key or admin approval to bypass organization-level subscription blocks. (2026-05-24)
Problems & Solutions
1. Import hijacking where Python pulled code from a sibling repo instead of the target project. [TokenMonitor] (2026-05-20)
Solution: Switched from direct script execution to python -m module.submodule to ensure correct context resolution.
2. Documentation inaccuracy regarding ‘verify + review’ hook enforcement. [TokenMonitor] (2026-05-20)
Solution: Identified missing hook configuration in .claude/settings.json and recommended explicit review_generator invocation.
3. Observation leakage in existing VLA architectures. [VLA Research] (2026-05-22)
Solution: Adopted the DISC approach of using hypernetworks to generate task-specific parameters, decoupling language from state.
Learnings
Architecture (architecture)
- VLA models are shifting from performance-only benchmarks toward structural decoupling (perceiving vs. acting), persistent 3D spatial memory, and extreme quantization (ternary) for edge deployment.
Debugging (debugging)
- Always verify Python import sources via
sys.pathin multi-repo workspaces to prevent silent hijacking by sibling repositories.
Tools (tools)
- When encountering persistent error messages in enterprise software, check administrative policy settings before attempting iterative prompt-based fixes.
AI Usage Notes
Effective Patterns:
- ✓ Using ‘max’ effort mode to force deep reasoning for complex research synthesis.
- ✓ Synthesizing disparate technical domains (precision vs. lightness) into a single cohesive architecture via human-guided prompting.
Limitations:
- ✗ AI generated truncated JSON when handling high volumes of paper evaluation data.
- ✗ Search noise in broad ML terms (e.g., ‘compression’) requiring domain-specific refinement (e.g., ‘TinyVLA’).
Next Week Outlook
The primary priority is resolving the Claude Code authentication block via API key acquisition to resume TokenMonitor development. Once access is restored, efforts will pivot to implementing the proposed VLA research trajectories, specifically focusing on hybrid cloud-edge architectures and testing extreme quantization strategies on simulated edge environments.
Token Usage Statistics
Peak Day: 2026-05-20 — $107.05 / 152.8M tokens
Daily Average: $46.46