Monthly Summary β 2026-02
February was characterized by intense architectural transitions and rigorous debugging across diverse domains, including spatial transcriptomics, robotics, and productivity tooling. Key efforts centered on stabilizing the MIHD multimodal pipeline through coordinate correction and E2E architecture refactoring, scaling the robotics error recovery benchmark with VLA/Pi0 integration and expanded scene databases, and professionalizing cross-device documentation workflows. Despite significant hurdles involving HPC cluster connectivity, enterprise proxy interference, and silent data/weight inconsistencies in deep learning models, the month successfully transitioned several high-stakes projects from critical troubleshooting toward scalable, production-ready evaluation frameworks.
Monthly Overview
| Metric | Value |
|---|---|
| Active Days | 23 / 28 |
| Total Conversations | 94 |
| Projects | 49 |
| Tasks Completed | 131 |
| Tasks In Progress | 15 |
| Total Tokens | 732,715,027 |
| Total Cost | $304.16 |
| Claude Code Token | 475,397,347 |
| Claude Code Cost | $217.52 |
| Codex Tokens | 252,664,071 |
| Codex Cost | $83.61 |
| Daily Average Cost | $13.22 |
Project Progress
MIHD Multimodal Pipeline & Spatial Transcriptomics (24 days active) β π active
Focus on alignment with STAIG architecture, resolving ARI regressions, and implementing E2E fusion training. Transitions included moving from monolithic scripts to a high-efficiency two-phase architecture (extraction vs. evaluation), integrating high-resolution Visium HD data, and repairing critical checkpoint corruption in scGPT models.
Key Milestones:
- Restoration of ARI metrics to >0.45
- Implementation of unified GCN+UNI2+STAIG_fusion configuration
- Transition to End-to-End GCN architecture design
- Implementation of two-phase extraction/evaluation pipeline
- Completion of 7-phase enhancement roadmap
- Successful fusion benchmark ablation
- Correction of spatial coordinate metadata (X/Y swap)
- Integration of self-supervised clustering modules (STEGO, SCAN)
- Establishment of embedding cache infrastructure
- Integration of Visium HD data with dynamic spatial scaling
QueST Metric Porting & Validation (2 days active) β π active
Reverse-engineering and porting the RM-Ideal (Wasserstein graph kernel) metrics to benchmark MIHD embeddings against spatial ground truth.
Key Milestones:
- Successful pseudo-code formulation of Wasserstein WWL kernels
- Integration of RM-Ideal scores into the MIHD evaluation framework
Robotics: Error Recovery & VLA Evaluation (16 days active) β π active
Scaling the robotics failure analysis framework from synthetic injection to natural error capture via VLA/Pi0 policy integration. Includes multi-task evaluation on HPC (Tianhe) focusing on image-mode parity and SLURM orchestration.
Key Milestones:
- v4.1 infrastructure completion
- VLA Policy Server integration (TCP inference for Pi0/Phoenix)
- Scale-up to 118 MimicGen scenes (surpassing M5 target with 454 scenes)
- Implementation of fault-tolerant episode collectors
- BC-RNN training config reconstruction for pixel-based observations
- Launch of unified Python-based SLURM orchestration pipeline
CalendarPro (5 days active) β π active
Productivity tool refinement focusing on production stability, batch operations, and intelligent task organization.
Key Milestones:
- Batch delete implementation
- OAuth scope optimization
- Recurring task system launch
Gadget/Summarize & Documentation Toolkit (10 days active) β β completed
Refactored the daily report tool into a two-phase multi-device architecture supporting local export, rclone cloud sync, and LLM-based structured report generation.
Key Milestones:
- Two-phase export/merge architecture design
- rclone cloud sync integration
- Open-source repository initialization
- Automated GitHub Pages deployment
macOS Desktop Video Wallpaper (1 days active) β π active
Developing UI features including history navigation and state persistence, while managing complex multi-display synchronization logic.
Key Milestones:
- Identification/resolution of multi-screen race conditions
Key Achievements
- MIHD Benchmark Recovery & Efficiency Breakthrough (MIHD Multimodal Pipeline) β Resolved massive ARI drops and coordinate swaps while implementing a two-phase architecture that reduced redundant computations by 62%.
- Robotics Error Detection & VLA Integration (Error Recovery Benchmark) β Built a TCP inference server for Pi0 models and overhauled detection logic, enabling natural error capture across 450+ MimicGen scenes.
- scGPT Checkpoint & Parameter Restoration (Spatial Transcriptomics: scGPT/Visium HD) β Patched Flash Attention key remapping to resolve catastrophic silent weight loss, restoring 9.4M parameters.
- Unified HPC Robotic Execution Framework (Robotics: BC-RNN & Pi0.5 VLA Evaluation) β Launched a SLURM orchestration pipeline for distributed rollout tracking across multiple MimicGen tasks.
- Internal Tooling & Pricing Integrity (Internal Tooling) β Diagnosed and patched a 13x cost underestimation bug in LiteLLM (ccusage) by implementing a fallback pricing mechanism.
- Secure Automated Reporting Infrastructure (MIHD Multimodal Pipeline) β Engineered a production-ready CI/CD relay system using GitHub Actions and NDJSON validation for secure benchmark submissions.
- Multi-Device Gadget Architecture (Gadget/Summarize Toolkit) β Redesigned the summarization tool to support a portable ‘Export-Sync-Merge’ workflow for cross-device collaboration.
Recurring Problems
1. Environmental, Dependency, and Configuration Mismatches (13 occurrences)
Dates: β Root Cause: Reliance on implicit conda environments, discrepancies between recording metadata (HDF5) and replay environments, and enterprise proxy interference with TCP/WebSocket traffic. Status: π§ Workaround
2. AI/LLM Output Instability and Formatting Errors (6 occurrences)
Dates: β Root Cause: LLMs returning prose, markdown, or truncated JSON due to token limits or nested environment triggers. Status: π§ Workaround
3. Documentation and Metric Divergence (3 occurrences)
Dates: β Root Cause: Asynchronous updates between raw CSV experiment results and auto-generated markdown logs. Status: β Resolved
4. Architectural/Mathematical Drift (3 occurrences)
Dates: β Root Cause: Deviations in activation functions, weight normalization, or preprocessing sequences compared to academic baselines. Status: β Resolved
5. Inconsistent Naming Conventions (Robotics) (4 occurrences)
Dates: β Root Cause: Discrepancies between MuJoCo, Robosuite, and LeRobot regarding coordinate ordering and body suffixes. Status: π§ Workaround
6. Silent Data and Weight Inconsistency (4 occurrences)
Dates: β Root Cause: Discrepancies in training/testing environment specs and silent failures in deep learning parameter mapping. Status: β Resolved
7. HPC/Remote Environment & Network Instability (6 occurrences)
Dates: β Root Cause: Network latency, SSH disconnects, and strict SLURM/PAM/Proxy security policies on shared nodes. Status: β Resolved
Human-AI Collaboration Trends
- Human-initiated insights: 34 items
- AI limitation patterns: Failure to proactively validate environmental/hardware states (CUDA/Conda/Proxy) before execution
- AI limitation patterns: Inability to detect semantic flaws in metric masking or macro-level data anomalies (e.g., low ARI) back to CSV columns
- AI limitation patterns: Over-reliance on standardized DL defaults and inability to propose major architectural shifts
- AI limitation patterns: Failure to respect domain-specific physical or naming constraints (e.g., robot controller dynamics or Robosuite suffixes)
- AI limitation patterns: Reactive debugging behavior (fixing symptoms instead of auditing infrastructure stubs)
- AI limitation patterns: Struggle with isolated workspace/path navigation and truncated log-based verification
- Improvement areas: Proactive environmental state auditing (hardware ACLs, proxies, and network)
- Improvement areas: Deep-dive analysis of mathematical implementation vs. semantic intent
- Improvement areas: Static analysis of configuration propagation paths and multi-context state synchronization
- Improvement areas: Strategic resource management (GPU scheduling) and proactive infrastructure auditing
- Improvement areas: Cross-referencing multiple directory sources for ground-truth validation
Monthly Learnings Digest
Architecture (architecture)
- For large-scale complex workloads, split pipelines into Phase 1 (heavy encoding with caching) and Phase 2 (lightweight fusion/evaluation) to prevent $O(N^3)$ redundancy. Two-phase (export-then-merge) designs are superior for distributed, multi-device workflows to ensure idempotency. (Source: 2026-02-14, 2026-02-17, 2026-02-19)
Domain Knowledge (domain)
- In spatial transcriptomics, foundation models are highly sensitive to pixel-to-spot alignment; metadata must be verified prior to extraction. In robotics, high-gain OSC controllers can mask errors; true visibility requires breaking the control loop via neutral-action injection. Robotic evaluation requires strict parity between training and testing (image-mode). (Source: 2026-02-15, 2026-02-17, 2026-02-18, 2026-02-26, 2026-02-28)
- Extreme dimension compression in GCNs can trigger silent representation collapse; always perform variance/dimension inspection. In structural similarity, metrics must preserve target-class candidates in the search pool to remain valid. (Source: 2026-02-08, 2026-02-16)
- HPC workflows must explicitly manage/unset enterprise proxy variables to allow peer-to-peer communication (WebSockets) and use multiplexers (tmux/salloc) for session persistence. (Source: 2026-02-27, 2026-02-28)
Debugging (debugging)
- Downstream failures often trace to silent upstream tensor dimension mismatches or serialization gaps. When detectors fail, prioritize auditing infrastructure/stub code over parameter tuning. Silent weight skipping in DL models requires immediate parameter count validation after loading. (Source: 2026-02-06, 2026-02-08, 2026-02-13, 2026-02-20, 2026-02-23, 2026-02-28)
Tools (tools)
- Production-grade AI systems must implement multi-layer JSON parsing (regex/fallback) to handle LLM instability. Large-scale GPU workloads require defensive I/O, explicit checkpoint resumption, and episode-level fault tolerance to survive cluster preemptions. (Source: 2026-02-02, 2026-02-03, 2026-02-14, 2026-02-19, 2026-02-23)
Token Usage Statistics
Peak Day: 2026-02-08 β $48.16 / 139.7M tokens
Daily Average: $13.22