Weekly Report — 2026-W29 (2026-07-13 ~ 2026-07-19)

This week marked a critical inflection point for the GR00T VLA project, transitioning from experimental setup to validated deployment configurations. The most significant achievement was identifying and correcting infrastructure artifacts that produced false ‘100% success’ metrics, leading to the establishment of W8A8 as the optimal quantization schema (98.5% accuracy, 410ms latency reduction). Concurrently, the ErrorRecoveryBenchmark data strategy was rigorously defined and sanitized, revealing a severe scarcity of qualified human demos (<2% yield) which necessitates new collection strategies. On the software engineering front, significant storage hygiene was achieved on Tianhe3 HPC, cross-repo bugs were resolved, and desktop tools (LiveCaption, TokenMonitor) were stabilized across Windows/macOS environments.

Weekly Overview

Metric	Value
Date Range	2026-07-13 ~ 2026-07-19
Active Days	6 / 7
Total Conversations	29
Projects	26
Tasks Completed	43
Tasks In Progress	3
Total Tokens	371,664,005
Total Cost	$349.76
Claude Code Token	48,302,670
Claude Code Cost	$113.42
Codex Token	323,361,335
Codex Cost	$236.34
Daily Average Cost	$58.29

Project Progress

GR00T VLA Model Quantization (6 days active) — ✅ completed

Accomplishments:

• Validated quantization pipelines on Qualcomm IQ-9075 EVK hardware.
• Identified and fixed silent infrastructure failures (ZMQ port collisions, PyTorch property masking) that caused false high-success metrics.
• Established W8A8 SeqMSE as the optimal deployment configuration balancing 98.5% accuracy with ~47% latency improvement over FP16.
• Confirmed that activation bit-width is the primary driver of NPU latency improvements.

Blockers:

• ⚠️ Complex ONNX export constraints and AI Hub compatibility issues requiring manual hook patching.
• ⚠️ Initial dependency on gated HuggingFace models pending token approval.

ErrorRecoveryBenchmark (5 days active) — 🔄 active

Accomplishments:

• Defined canonical data specifications (1,360 scenes for Training/Eval) resolving documentation/code drift.
• Executed large-scale HDF5 audits on Tianhe3 and performed safe, hash-verified pruning of ~142GB of invalid data.
• Discovered that fewer than 2% of raw human demos qualified as high-fidelity training data.

Blockers:

• ⚠️ Severe shortage of qualified ‘recovery’ demos; existing candidates fail strict provenance and success-streak criteria.
• ⚠️ Schema discrepancies in human-demo datasets requiring flexible validation logic.

Robotic Learning Baselines (Action-Sketcher/RoboMME) (4 days active) — ✅ completed

Accomplishments:

• Deployed Action-Sketcher on Tianhe3 and RoboMME environments.
• Validated hypothesis that action-only adaptation fails completely (0% success) on memory tasks without visual sketch conditioning.
• Established pi0.5 baselines (34%/26%) for comparison.

Blockers:

• ⚠️ Domain shift between LIBERO (delta-EE) and RoboMME (joint-angle) breaking pre-trained policy weights.
• ⚠️ LIBERO environment step-limits (~580 steps) preventing full cycle evaluation of planning agents.

Infrastructure & Tooling (Tianhe3/Gadget/Desktop) (6 days active) — 🔄 active

Accomplishments:

• Reclaimed ~1.7TB of HPC storage on Tianhe3 through strategic checkpoint archiving.
• Resolved cross-repo bugs in LifeCopilot, ai-companion, and Gadget (12 critical fixes).
• Ported LiveCaption to Windows with WSL-vLLM streaming ASR; stabilized TokenMonitor UI.

Blockers:

• ⚠️ SSH connectivity issues between Windows IDEs and HPC clusters requiring complex tunneling workarounds.
• ⚠️ Realtek NIC driver causing BSODs on desktop workstation.

3D World Model-Guided VLA (Research) (1 days active) — 🔄 active

Accomplishments:

• Synthesized literature to define a novel research architecture focusing on counterfactual path evaluation.

Key Tasks

• ✅ Correct GR00T Evaluation Infrastructure (2026-07-17) — Diagnosed ZMQ port collisions and PyTorch property masking that yielded fake 100% success metrics; implemented sanity checks to ensure valid benchmarking.
• ✅ Define ErrorRecoveryBenchmark Qualification Criteria (2026-07-16) — Established strict counts (1,360/1,360) and provenance requirements; executed audits revealing <2% data yield from raw sources.
• ✅ GR00T W8A8 Quantization Validation (2026-07-19) — Finalized latency/accuracy trade-offs on Qualcomm hardware, selecting W8A8 SeqMSE as the production standard.
• ✅ Tianhe3 Storage Audit & Cleanup (2026-07-14) — Audited and archived OpenPI checkpoints, reclaiming ~1.7TB of critical HPC storage space.
• ✅ Action-Sketcher Baseline Validation (2026-07-17) — Executed action-only baselines confirming the necessity of visual sketch conditioning for memory tasks in robotics.
• ✅ Cross-Repository Bug Resolution (2026-07-15) — Fixed 12 critical issues across LifeCopilot, ai-companion, and Gadget, including report path mismatches and orphaned code.
• ✅ LiveCaption Windows Port (2026-07-18) — Implemented WASAPI audio capture and WSL-vLLM streaming for native Windows real-time captioning.

Problems & Solutions

1. Silent infrastructure failures (ZMQ port collisions, PyTorch property masking) produced false 100% success rates in GR00T quantization benchmarks. [GR00T VLA Model Quantization] (2026-07-17)

Solution: Implemented dynamic port assignment and direct submodule targeting with sanity checks (zero-action/null-input) to validate environment integrity.

2. Severe data scarcity: <2% of raw human demos on Tianhe3 met the strict qualifications (success streak, provenance) for the ErrorRecoveryBenchmark. [ErrorRecoveryBenchmark] (2026-07-16)

Solution: Defined canonical datasets and executed hash-verified pruning; concluded that new collection/augmentation strategies are required rather than relying on existing pools.

3. Action-Sketcher action-only baseline achieved 0% success on RoboMME memory tasks due to domain shift (delta-EE vs joint-angle) and lack of visual context. [Robotic Learning Baselines] (2026-07-17)

Solution: Confirmed that sketch conditioning is mandatory; prioritized Phase 1 sketch-adaptive training over pure action expert tuning.

4. Cross-repo communication failures caused LiveCopilot to read from empty report directories while Gadget wrote to different paths. [Infrastructure & Tooling] (2026-07-15)

Solution: Unified all report paths to a single standard outputs/reports/ and cleaned legacy references across three repositories.

5. Windows live caption deployment required cross-platform audio access which WSLg lacks for speaker output. [Infrastructure & Tooling] (2026-07-18)

Solution: Architected a hybrid system using Windows PYAudiowpatch for loopback capture and WSL vLLM for inference, bridged via NDJSON pipes.

6. HPC storage quotas were critical; global free space masked project-specific exhaustion on Tianhe3. [Infrastructure & Tooling] (2026-07-14)

Solution: Used dust for audit and Python scripts reading only HDF5 attributes (to avoid metadata latency) to identify 1.1TB of OpenPI checkpoints for archiving.

7. Quantization benchmarks revealed W4A8 was insufficient for GR00T’s LLM backbone accuracy. [GR00T VLA Model Quantization] (2026-07-19)

Solution: Evaluated multiple schemas and established W8A8 as the optimal trade-off (98.5% accuracy), noting that activation width drives NPU latency more than weight width.

Learnings

Architecture (architecture)

• Quantized model evaluation pipelines must include ‘zero-action’ or ’null-input’ sanity checks to detect fake metrics caused by silent infrastructure failures.
• For NPU deployments like Qualcomm IQ-9075, activation bit-width (A16->A8) dominates latency improvements over weight bit-width; W8A8 is often superior to W4A8 for accuracy retention.

Domain Knowledge (domain)

• Robotics VLA memory tasks are a distinct failure mode from action execution; baselines without sketch/memory-conditioned observations fail completely on counting tasks.
• Data quality for scientific claims requires strict provenance; relying on ‘usable’ data counts without auditability is insufficient for canonical benchmarks.

Tools (tools)

• On Lustre-based HPC clusters, large-scale dataset auditing requires reading only high-level attributes (not iterating nested groups) to avoid metadata latency bottlenecks.
• Cross-repo audits are most effective when split into parallel sub-agents with strict output contracts, combined with external model validation to catch blind spots like path mismatches.

Debugging (debugging)

• In complex devops setups, IDE-specific SSH implementations (like Tabby) may diverge from standard OpenSSH behavior, requiring direct configuration overrides rather than config inheritance.

AI Usage Notes

Effective Patterns:

• ✓ Using Claude Code for structured, constraint-heavy tasks (bulk localization, JSON refactoring) yielded high fidelity results.
• ✓ Parallel sub-agent audits successfully identified cross-repo bugs that linear review missed.
• ✓ Strategic ‘sanity check’ prompts effectively caught AI hallucinations about successful infrastructure states.

Limitations:

• ✗ AI struggled to anticipate silent PyTorch property masking and ZMQ port collisions without explicit user skepticism and deep domain guidance.
• ✗ Initial AI suggestions for quantization strategies ignored NPU-specific latency characteristics, requiring manual correction to focus on activation bits.
• ✗ AI had difficulty maintaining strict formatting/constraints during complex multi-step string escaping or batch translation tasks.

Next Week Outlook

The immediate priority is addressing the ErrorRecoveryBenchmark data scarcity, as existing archives cannot fill the 1,360-scene gap; new collection or augmentation logic must be designed. For GR00T, focus will shift to deploying the W8A8 models on target edge hardware and optimizing the visual sketch module for Action-Sketcher Phase 1. Research should begin prototyping the 3D World Model-guided VLA architecture proposed mid-week.

Token Usage Statistics

Peak Day: 2026-07-14 — $223.03 / 246.1M tokens

Daily Average: $58.29