Weekly Report — 2026-W09 (2026-02-23 ~ 2026-03-01)

This week was characterized by significant technical breakthroughs in multi-modal spatial omics pipelines and the architecture of a robust robotic evaluation framework. Key achievements include resolving catastrophic weight corruption in scGPT models, optimizing fusion workflows via vectorization, and establishing a highly resilient HPC training/evaluation pipeline for BC-RNN and Pi0.5 VLA models. We successfully navigated complex cluster security and networking constraints, moving from fragile, error-prone scripts to a deterministic, cross-device orchestration system that supports large-scale, long-horizon training and evaluation.

Weekly Overview

Metric	Value
Date Range	2026-02-23 ~ 2026-03-01
Active Days	5 / 7
Total Conversations	16
Projects	14
Tasks Completed	19
Tasks In Progress	3
Total Tokens	159,792,860
Total Cost	$68.82
Daily Average Cost	$13.76

Project Progress

Error Recovery Benchmark (BC-RNN & Pi0.5) (5 days active) — 🔄 active

Accomplishments:

• Architected unified BC-RNN multi-task pipeline with SLURM orchestration
• Implemented Pi0.5 Phoenix LoRA training with explicit checkpoint recovery
• Resolved observation dimension mismatches (object state keys) and modality alignment (CHW/HWC)
• Established parallel training across 9 MimicGen tasks on A800 partitions

Blockers:

• ⚠️ HPC cluster permission/QOS constraints
• ⚠️ Enterprise proxy hijacking WebSocket/TCP traffic

MIHD Spatial Omics Pipeline (4 days active) — 🔄 active

Accomplishments:

• Restored scGPT checkpoint fidelity by fixing Flash Attention key remapping
• Integrated Visium HD support with dynamic coordinate scaling
• Achieved 100x-500x speedups via STAIG fusion vectorization
• Validated architectural superiority of high-dimensional feature concatenation

Blockers:

• ⚠️ Coordinate space mismatches between metadata and high-res images

Gadget & CalendarPro Dev (2 days active) — 🔄 active

Accomplishments:

• Developed cross-device log aggregation with a two-phase finalize/merge state machine
• Implemented async task engine for recurring scheduling in CalendarPro
• Resolved macOS multi-screen race conditions and power-state routing

Desktop Video Wallpaper (1 days active) — ✅ completed

Accomplishments:

• Implemented History UI with persistent state and thumbnail caching
• Automated Xcode versioning and bypassed sandbox restrictions for manifest updates

Key Tasks

• ✅ MIHD Spatial Omics Pipeline Optimization — Patched scGPT checkpoint attribute persistence and implemented dynamic coordinate scaling for Visium HD, recovering ARI from near-zero to >0.54 across 11 DLPFC sections. vectorized edge weights for massive speedups.
• 🔄 BC-RNN Multi-Task Baseline Pipeline Architecture — Designing and launching a unified SLURM-orchestrated system for training and evaluating 9 diverse robotic tasks, managing VRAM fractions and dependency injection.
• ✅ scGPT Checkpoint Restoration — Diagnosed and resolved silent weight corruption caused by missing Fast Transformer attributes during checkpoint loading.
• ✅ HPC Resource & Environment Management — Resolved critical blockers including enterprise proxy interference with WebSockets, SLURM partition access, and GPU VRAM monopolization by zombie/idle processes.
• ✅ Robotic Observation Fixes — Corrected zero-success rates in MimicGen tasks by injecting missing ‘object’ state keys and aligning HWC/CHW tensor modalities.

Problems & Solutions

1. Silent scGPT weight corruption during checkpoint loading due to Flash Attention key remapping failure. [MIHD]

Solution: Injected explicit init attribute assignment for fast_transformer to ensure proper PyTorch key mapping.

2. Vision encoder ARI collapse caused by misapplying low-res coordinates to high-res images. [MIHD]

Solution: Implemented automatic scale-factor adjustment in VisionExtractor to align coordinate spaces.

3. Zero success rates in BC-RNN Coffee tasks due to missing 57-dimensional object state keys in configs. [Error Recovery Benchmark]

Solution: Patched YAML generation scripts to inject per-task ‘object’ observation overrides.

4. HPC WebSocket connectivity failures caused by inherited enterprise HTTP/HTTPS proxy variables. [Error Recovery Benchmark]

Solution: Explicitly unset proxy environment variables during job initialization to enable direct P2P routing.

5. GPU VRAM monopolization by idle inference servers or eager JAX/PyTorch backend loading. [Error Recovery Benchmark]

Solution: Used CUDA_VISIBLE_DEVICES="" and JAX_PLATFORMS=cpu to force CPU execution for non-training tasks, and utilized PID-based termination for zombie processes.

Learnings

Domain Knowledge (domain)

• Vision-only models struggle with heterogeneous biological data; multi-modal fusion is mandatory for accurate spatial transcriptomic mapping.

Architecture (architecture)

• Strict checkpoint loading in deep learning can silently mask attribute omissions; proactive state persistence verification is required.
• Cross-framework integration (robosuite/robomimic) necessitates strict adherence to tensor shapes (CHW vs HWC) and modality alignment.

Debugging (debugging)

• HPC environments require active environmental scoping (unsetting proxies, managing VRAM via PID) rather than just simple package isolation.

Tools (tools)

• AI-generated structured data (JSON) requires programmatic defense mechanisms (markdown stripping, schema enforcement) to survive parser crashes.

AI Usage Notes

Effective Patterns:

• ✓ Using AI for automated dependency mapping during complex refactoring
• ✓ Leveraging AI for translating high-level architectural constraints into robust SwiftUI/Swift code
• ✓ Utilizing AI for rapid code scaffolding and CLI/YAML configuration generation

Limitations:

• ✗ AI inability to predict eager device backend initialization (JAX/CUDA) without framework-level flags
• ✗ Sandbox restrictions preventing direct low-level shell manipulation
• ✗ Inability to autonomously discover assets in shared storage volumes without explicit human guidance

Next Week Outlook

Priorities for next week involve scaling the Pi0.5 VLA evaluation across the full 9-task MimicGen suite, completing the human-in-the-loop (M15) pivot for error-recovery training, and finalizing the Gadget cross-device synchronization deployment. We will focus on monitoring the long-horizon training runs for convergence stability and ensuring the multi-modal fusion experiments for MIHD are fully documented.

Token Usage Statistics

Peak Day: 2026-02-28 — $24.99 / 57.1M tokens

Daily Average: $13.76