Weekly Report — 2026-W38 (2026-09-14 ~ 2026-09-20)

This week was defined by intensive technical troubleshooting and the transition of several projects from unstable development into research-ready phases. Major achievements include resolving a critical 0% success rate in pi0.5 VLA model quantization by identifying and fixing RMSNorm precision loss, establishing a robust ‘Oracle’ visual prompt pipeline for RoboMemory, and performing large-scale data reorganization and Git history purging for cleaner repository management. While significant hardware-level and quantization-related bottlenecks were cleared, ongoing challenges remain in stabilizing long-running training processes and resolving distributed data synchronization issues.

Weekly Overview

Metric	Value
Date Range	2026-09-14 ~ 2026-09-20
Active Days	6 / 7
Total Conversations	35
Projects	27
Tasks Completed	28
Tasks In Progress	13
Total Tokens	965,666,942
Total Cost	$826.15
Claude Code Token	927,413,556
Claude Code Cost	$753.89
Codex Token	38,253,386
Codex Cost	$72.26
Daily Average Cost	$165.23

Project Progress

pi0.5 VLA & Quantization Research (5 days active) — 🔄 active

Accomplishments:

• Identified root cause of W4A4 success rate collapse (RMSNorm precision loss)
• Established W4A8 as the optimal delivery standard for NPU/QAIRT deployment
• Implemented AIMET TF-Enhanced optimized norm ranges
• Completed intensive bitwidth benchmarking on Dragonwing NPU

Blockers:

• ⚠️ Maintaining execution stability in near-uniform A4 bitwidth environments

RoboMemory / RoboMME (4 days active) — 🔄 active

Accomplishments:

• Developed ‘Oracle v2’ visual prompt specification for 16 tasks
• Integrated scene-anchored visual markers to improve memory robustness
• Identified and fixed catastrophic training loss caused by normalization stat mismatches

Blockers:

• ⚠️ Multi-task 16-episode evaluation is currently blocked pending new checkpoint stability

AI Companion Toolchain (3 days active) — 🔄 active

Accomplishments:

• Refactored core skills to support ‘authorize-and-proceed’ autonomy
• Implemented D33 progress reporting standards
• Mapped out complex security guardrail and refusal logic

Blockers:

• ⚠️ Interception of tool calls by IDE hooks/guards

TokenMonitor (3 days active) — 🔄 active

Accomplishments:

• Synchronized fork with upstream via strategic cherry-picking
• Resolved CI Node version mismatches
• Implemented custom period windows and Grok Bot pricing logic

Blockers:

• ⚠️ SSH usage data synchronization bugs across devices

MIHD & Data Management (1 days active) — ✅ completed

Accomplishments:

• Reorganized 25GB of research outputs and synchronized paths via regex-based rewriting
• Purged companion/idea metadata from Git history across multiple repos

Key Tasks

• ✅ VLA Quantization Root Cause Analysis — Isolated A4 activation errors in RMSNorm outputs as the cause of zero-success W4A4 performance in Libero tasks via ablation studies and tensor probing. Data pinpointed 10% noise per layer due to outlier-stretched ranges; fixed via per-tensor optimal clipping using AIMET TF-Enhanced. Resolved on 2026-09-19 after initiation on 2026-09-14.
• 🔄 RoboMemory Training Correction — Re-running ‘all16_base’ training to resolve astronomical loss (2e11) caused by zero-standard deviation in gripper dimensions within the imitation2 subset. Requires re-computation of norm_stats on the full 16-task dataset.
• ✅ Oracle Visual Prompt Specification — Defined high-fidelity, trainable visual prompt rules (icons, rings, badges) for 16 RoboMME tasks to facilitate visual memory augmentation training. Focused on ’training-robustness’ against random cropping and rotation.
• ✅ Git History Purge & Repo Management — Used git filter-repo to remove developer-specific metadata (.companion, ideas, config) from historical commits for LiveCaption, TokenMonitor, and other core repositories to ensure clean public releases.

Problems & Solutions

1. pi0.5 Libero goal suite reported 0% success rate. [pi0.5 VLA] (2026-09-17)

Solution: Verified float baseline achieved 100% success, isolating the issue to quantization-induced precision loss in the action head. Used ablation to identify RMSNorm as the culprit.

2. 4-bit activation (W4A4) significantly slower/worse than FP16 on NPU. [pi0.5 VLA] (2026-09-14)

Solution: Identified absence of native INT4 activation kernels in NPU, causing overhead from tensor expansion. Recommending W4A8 as the optimal balance.

3. RoboMemory training loss reached 2e11 (astronomical). [RoboMemory] (2026-09-17)

Solution: Identified that normalization stats were computed on a subset with zero std in the gripper dimension; re-computed stats on the full 16-task dataset.

4. AI Hub automation broke due to ‘qnn_context_binary’ deprecation. [AI Hub] (2026-09-14)

Solution: Updated submission scripts to explicitly pass ‘–target_runtime qnn_dlc’ and updated all related documentation.

5. TokenMonitor fork merge failed due to 66+ conflicts from rewritten history. [TokenMonitor] (2026-09-17)

Solution: Abandoned direct merge in favor of cherry-picking the 10 core feature commits onto the upstream base.

Learnings

Architecture (architecture)

• For VLA models, W4A8 is the ‘Sweet Spot’: weight 4-bit is highly tolerable, while A4 activation causes catastrophic precision collapse in the backbone.
• Visual prompts must prioritize ’training-robustness’ (surviving crop/rotate) and ‘policy-utility’ (scene-anchored) over aesthetic clarity.

Debugging (debugging)

• Zero-standard deviation in a training subset can cause catastrophic failure in multi-task normalization statistics.
• In high-concurrency environments, worker limits must account for ‘grandchild’ processes; optimization follows a U-shaped curve.

Tools (tools)

• When fork history is rewritten, ‘git merge’ is often unusable; cherry-picking is a more reliable reconstruction method.

Domain Knowledge (domain)

• The ‘Target Hit Rate’ is a dangerous metric for memory tasks as it conflates object detection accuracy with actual temporal memory of history.

AI Usage Notes

Effective Patterns:

• ✓ Using multi-dimensional cross-verification (closed-loop vs. open-loop vs. tensor-level) to isolate math errors.
• ✓ Implementing ‘authorize-and-proceed’ autonomy models for agent workflows to reduce decision fatigue.
• ✓ Leveraging browser automation (Playwright) for academic synchronization workflows.

Limitations:

• ✗ Agent ‘decision fatigue’ resulting in repetitive approval prompts.
• ✗ Inability to independently handle long-running benchmarks due to sandbox/guard constraints.
• ✗ Tendency for AI to provide circular self-verifying reports without objective proof.

Next Week Outlook

Priorities include: 1) Completing the full 50-episode evaluation for the TF-Enhanced pi0.5 model to verify the A4/A8 stability; 2) Finalizing the RoboMemory ‘all16_oracle’ training run and implementing the visual prompt sidecar data generation; 3) Resolving the SSH data synchronization bug in TokenMonitor; and 4) Investigating potential memory ’error cascades’ in chained-memory prompting architectures.

Token Usage Statistics

Peak Day: 2026-09-15 — $390.75 / 364.4M tokens

Daily Average: $165.23