Weekly Report — 2026-W10 (2026-03-02 ~ 2026-03-08)

This week was defined by major architectural transitions and high-performance stabilization across robotics (VLA), bioinformatics (spatial genomics), and macOS desktop ecosystems. Key achievements included a 60% increase in GPU utilization through a new batched inference server, the optimization of LLaVA training via ZeRO-2 reducing ETA from 13.5 to 2 days, and the successful decoupling of spatial transcriptomics workloads into a high-efficiency two-stage pipeline. Significant structural improvements were also made via global symlink removal and repository refactoring, while critical macOS sandbox and rendering barriers were resolved through native window compositing and entitlement patching. Despite challenges with GPU OOM errors, Docker rendering failures, and network restrictions, the week concluded with stabilized training foundations, improved evaluation metric integrity, and a more robust, agent-friendly infrastructure.

Weekly Overview

Metric	Value
Date Range	2026-03-02 ~ 2026-03-08
Active Days	7 / 7
Total Conversations	29
Projects	27
Tasks Completed	34
Tasks In Progress	6
Total Tokens	580,063,466
Total Cost	$290.85
Claude Code Token	554,458,620
Claude Code Cost	$282.81
Codex Token	25,604,846
Codex Cost	$8.04
Daily Average Cost	$41.55

Project Progress

Error-Recovery VLA Benchmark & Robotics Training (Phoenix/FLARE/CALVIN) (7 days active) — 🔄 active

Accomplishments:

• Deployed BatchedVLAServer with TCP queuing to resolve GPU starvation and increase utilization from 10% to 60%+.
• Optimized LLaVA training via ZeRO-2 and local weight conversion, reducing ETA from 13.5 to ~2 days.
• Corrected MuJoCo cvel memory layout (angular/linear) and physical trigger failures.
• Aligned observation key mappings for BC-RNN and Pi0.5 policy evaluation and executed batch evaluations across nine MimicGen tasks.
• Developed multi-stage MimicGen data preparation and training orchestration suites.
• Implemented pre-error trajectory replay by persisting raw state histories in NPZ payloads.
• Resolved RoboTwin Docker rendering failures through NVIDIA driver/ICD alignment.
• Resolved Pi0.5 OOM errors via two-GPU FSDP sharding and mapped isolated conda caches for offline dependency resolution.

Blockers:

• ⚠️ Initial architectural mismatches caused zero-success rates in perturbed environments.
• ⚠️ Cluster proxy/network blockages for remote weight fetching.
• ⚠️ MuJoCo/EGL device misalignment in Slurm environments.

MIHD Spatial Genomics Pipeline (ContraVAE/STHD) (5 days active) — 🔄 active

Accomplishments:

• Implemented a two-stage pipeline (embedding caching and fusion evaluation) reducing compute overhead by 85%.
• Validated multi-modal fusion strategies (AdaLN/Q-Former/SCAN) and STAIG fusion superiority with 96% accuracy.
• Executed cross-sample RM-IDEAL benchmarks on Visium HD datasets.
• Repaired scGPT checkpoint loading failures (+44.4% ARI gain).
• Executed a six-phase repository cleanup, removing ~250K lines of dead code.

Blockers:

• ⚠️ Coordinate mapping failures in Uni2/HIPT prior to decoupling.
• ⚠️ Memory-intensive loading of large spatial matrices causing OOM.

CalendarPro Autonomous Butler System & macOS Desktop Apps (6 days active) — 🔄 active

Accomplishments:

• Completed Phase 1-3 architecture overhaul, including session-aware intent classification and token-budget context compression.
• Refactored screensaver/wallpaper rendering engine using NSWindow and .behindWindow compositing.
• Resolved macOS sandbox permission errors and network requests via entitlement patching and secure-scoped bookmark lifecycles.
• Implemented periodic background task scheduling with JSONL persistence.
• Expanded CI coverage to 360+ passing test cases.
• Externalized semantic routing via JSON with mismatch-driven auto-augmentation.
• Engineered dynamic web wallpaper engine with native browse mode.

Blockers:

• ⚠️ Hardware-accelerated transparency regressions in SwiftUI layers.
• ⚠️ Context degradation in long-horizon multi-session interactions.

Gadget CLI & Infrastructure Refactoring (3 days active) — ✅ completed

Accomplishments:

• Executed global symlink removal and migration to absolute project roots to ensure predictable dependency resolution.
• Restructured reporting CLI with atomic file writes and intelligent sync-skipping.
• Fixed API cost tracking bugs for Claude-Opus variants by implementing a version-tolerant fallback dictionary.
• Automated Hugo Pages deployment pipeline.
• Standardized import paths and updated Makefile/YAML configurations (validated via 127+ unit tests).

Key Tasks

• 🔄 Pi0.5 VLA Inference & Training Optimization — Deployed BatchedVLAServer to resolve GPU starvation and optimized LLaVA training via ZeRO-2/FSDP sharding to bypass proxy/memory bottlenecks.
• ✅ MIHD Spatial Transcriptomics Benchmarking & Repair — Adapted RM-IDEAL pipeline to Visium HD and fixed scGPT checkpoint Transformer attribute losses to restore metric integrity.
• ✅ CalendarPro Autonomous Architecture & Context Routing — Redesigned intent classification using session-aware signals, token-budget compression, and dynamic mismatch learning.
• 🔄 Self-Reflection Framework Orchestration — Designing a six-stage training blueprint for 8x A800 deployment, including HDF5 data route synchronization.
• ✅ Global Repository & Symlink Migration — Systematic migration from relative symlinks to absolute project roots to ensure predictable cross-project dependency resolution.

Problems & Solutions

1. VLA inference pipelines suffered extreme GPU starvation due to Action Chunking idle gaps and WebSocket bottlenecks. [Error-Recovery VLA Benchmark] (2026-03-02)

Solution: Engineered a BatchedVLAServer with timer-based queuing and subprocess workers to mask serialization gaps.

2. MuJoCo simulation instability caused by inverted cvel memory layout and misassigned EEF targets. [Error-Recovery VLA Benchmark] (2026-03-03)

Solution: Corrected raw pointer slicing to [angular(3), linear(3)] and implemented per-step re-application loops for force injection.

3. LLaVA/Phoenix training duration overestimated due to ZeRO-3 overhead and network proxy blocks. [Robotics & VLA Training] (2026-03-07)

Solution: Downgraded to ZeRO-2 for better memory balance, converted weights to local safetensors, and preloaded HDF5 to CPU RAM.

4. Headless Docker container rendering failures caused by Vulkan ABI mismatches. [Robotics & VLA Training] (2026-03-06)

Solution: Aligned NVIDIA drivers with ICD configurations and used dedicated environment variables for library mapping.

5. macOS sandbox blocking WKWebView network requests and window routing conflicts. [macOS Desktop Apps] (2026-03-07)

Solution: Added ’network.client’ entitlements and elevated NSWindow levels to the ‘.normal’ interactive tier.

6. Silent failures in normalization pipelines due to missing data keys in ’norm_stats’. [Spatial Transcriptomics] (2026-03-05)

Solution: Patched compute scripts to dynamically inject keys into running statistics to prevent scale mismatches.

7. Automated API cost tracking returned $0 for specific Claude-Opus variants due to version suffix discrepancies. [Gadget CLI] (2026-03-03)

Solution: Traced LiteLLM mapping and implemented a version-tolerant fallback pricing dictionary.

Learnings

Architecture (architecture)

• Decoupling high-latency feature extraction into cached staging directories and using phased architectural specifications with explicit gates prevents circular dependencies and controls multi-modal compute costs.

Domain Knowledge (domain)

• Foundation models in bioinformatics require advanced downstream fusion techniques and strict feature parity; similarly, large-VLA models require explicit model parallelism (ZeRO-2/FSDP) to overcome memory tiers.

Debugging (debugging)

• Physics engine memory layouts often contradict documentation requiring direct pointer validation; furthermore, silent observation/key mismatches can corrupt evaluation baselines before strategic conclusions are drawn.

Tools (tools)

• macOS hardware-accelerated layers require independent NSWindow compositing; for infrastructure, while symlinking saves space, hardcoding ‘sys.path’ is critical post-refactoring to maintain predictable evaluation trees.

AI Usage Notes

Effective Patterns:

• ✓ Using AI for deep structural diagnostics and rapid execution of boilerplate implementation (e.g., JSONL persistence, registry patterns).
• ✓ Leveraging AI to translate strategic architectural boundaries into functional Python server logic.
• ✓ Parallel agent orchestration for bulk codebase refactoring (symlink removal).
• ✓ Mismatch-driven auto-augmentation for conversational intent training.
• ✓ Agentic planning for architectural optimization and SLURM-ready script generation.

Limitations:

• ✗ Convention bias: AI defaults to standard HPC scheduling/JAX heuristics unless explicitly overridden.
• ✗ Relative path blindness in SSH/HPC environments requiring frequent manual CWD corrections.
• ✗ Fragility in maintaining long-horizon context across massive sequential CLI/CSV data streams.
• ✗ Over-reliance on standard system paths in isolated conda runtimes.
• ✗ Lack of proactive intuition for platform-specific native constraints (macOS sandbox/window tiers).
• ✗ Tendency toward over-engineering infrastructure before recognizing minimalist/constraint-driven boundaries.

Next Week Outlook

Prioritize the active Self-Reflection Framework orchestration for 8x A800 deployment, focusing on HDF5 data route synchronization and checkpoint management. We will monitor the optimized LLaVA training rollouts and validate new policy results for generalizability. For CalendarPro, the focus shifts toward long-horizon memory persistence and proactive task discovery, while the spatial transcriptomics pipeline will be refined with newly repaired scGPT checkpoints and expanded datasets.

Token Usage Statistics

Peak Day: 2026-03-07 — $123.56 / 219.1M tokens

Daily Average: $41.55