Weekly Report — 2026-W08 (2026-02-16 ~ 2026-02-22)

This week successfully bridged multimodal spatial transcriptome research with robotic error recovery benchmarks, transitioning from theoretical planning to validated execution pipelines. Significant progress was made in the MIHD pipeline by resolving critical coordinate swap bugs and implementing self-supervised clustering (STEGO, SCAN) to restore data integrity. Simultaneously, the Error Recovery Benchmark surpassed M5 goals through massive scene expansion and the implementation of a VLA dual-server infrastructure. While the team encountered cluster-level permission hurdles and model-specific training challenges (BC-RNN), the establishment of robust data conversion frameworks (HDF5-to-LeRobot) and automated reporting infrastructure has stabilized the foundation for large-scale multi-policy evaluation and multimodal fusion training.

Weekly Overview

Metric	Value
Date Range	2026-02-16 ~ 2026-02-22
Active Days	7 / 7
Total Conversations	32
Projects	18
Tasks Completed	41
Tasks In Progress	6
Total Tokens	229,294,726
Total Cost	$107.08
Daily Average Cost	$15.30

Project Progress

Error Recovery Benchmark (7 days active) — 🔄 active

Accomplishments:

• Achieved M5 milestone by expanding scene database from 251 to 454 (227% of target) via pose_perturb and friction injectors.
• Implemented VLA dual-server infrastructure (Pi0 and Pi0.5) on tianhe.
• Completed M6 milestone preparation with multi-policy evaluation framework (Random, BC-RNN, VLA_Pi0, VLA_Pi0.5).
• Scaled dataset construction using MimicGen augmentation and developed HDF5-to-LeRobot conversion pipelines.
• Implemented resume-capable episode collectors and M14 baseline evaluation scripts.
• Engineered tmux-based automation for background job monitoring.

Blockers:

• ⚠️ BC-RNN policy shows 0% success rate due to undertrained checkpoints rather than framework bugs.
• ⚠️ Cluster GPU node access restrictions (SLURM/PAM permission issues).
• ⚠️ VLA server port binding conflicts.

MIHD Research & Benchmarking (7 days active) — 🔄 active

Accomplishments:

• Identified and fixed a critical X/Y coordinate swap bug in spatial transcriptomics pipeline, restoring UNI2 visual ARI from 0.065 to ~0.25.
• Implemented self-supervised clustering (STEGO, BYOL+GAT, SCAN) and validated ARI/NMI gains.
• Conducted ablation studies and established QFormer Enhanced and STAIG GCN+STAIG fusion as superior methods.
• Integrated embedding caching into the benchmark pipeline to reduce redundant computation.
• Initiated re-running vision-dependent fusion experiments (core_multimodal_fast) with corrected embeddings.
• Documented metric calculations and vision encoder architectures.

Blockers:

• ⚠️ Requires extensive re-run of 286+ experiments to validate corrected spatial data.
• ⚠️ Numerical instability (NaN/divergence) in dense similarity matrices.

Daily Report Tooling & Infrastructure (2 days active) — ✅ completed

Accomplishments:

• Developed a production-ready two-phase (export -> merge) architecture for multi-device log synchronization using rclone.
• Implemented robust 4-stage JSON parsing with LLM-powered repair fallbacks.
• Created automated Hugo/GitHub Pages deployment pipeline for bug journaling.

VLA/Pi0 Fine-Tuning Readiness (1 days active) — 🔄 active

Accomplishments:

• Inventoried existing model weights and established a centralized symlink-based checkpoint management system.
• Assessed MimicGen dataset readiness and identified a 500-demo expansion requirement.

Blockers:

• ⚠️ Lack of task-specific checkpoints for Robosuite environments.

Project Infrastructure & Documentation (3 days active) — ✅ completed

Accomplishments:

• Refactored and compressed core project documentation (CLAUDE.md/README.md).
• Standardized remote SSH/SLURM workflows across heterogeneous cluster environments.
• Unified research plans into a consolidated strategic document.

Key Tasks

• ✅ Fix MIHD load_spatial_coordinates() X/Y coordinate swap bug — Corrected CSV column mapping to ensure vision patches are extracted from correct spatial coordinates, resolving a root data integrity issue affecting all fusion benchmarks.
• ✅ Diagnose and fix ccusage claude-opus-4-6 zero-cost billing bug — Identified 13x cost underestimation caused by LiteLLM model name mismatches and implemented a fallback pricing mechanism.
• ✅ Generate tianhe M5 non-impulse error scenes — Expanded benchmark database to 454 scenes (227% of target) using automated augmentation.
• ✅ VLA Policy Server Integration & E2E Rollout — Built TCP inference server for Pi0/Phoenix models and executed 59+ rollouts.
• ✅ Self-Supervised Clustering & Vision Refinement Implementation — Built custom PyTorch modules for spatial contrastive losses; resolved InfoNCE divergence via L2 normalization and LogSumExp stabilization.
• ✅ Embedding Cache I/O Integration — Integrated CacheManager into run_benchmark.py to eliminate redundant encoder instantiation and speed up benchmarking.
• ✅ Multi-Task LeRobot Data Conversion & OpenPI Training — Engineered HDF5-to-Parquet conversion for 9 MimicGen tasks and updated training configurations for foundation model fine-tuning.

Problems & Solutions

1. MIHD vision encoder extraction using transposed X/Y coordinates caused extremely low ARI (~0.065). [MIHD] (2026-02-17)

Solution: Corrected CSV column mapping (pxl_col/pxl_row) and coordinate ordering, purging all corrupted spatial caches.

2. ccusage failed to track costs for claude-opus-4-6 due to LiteLLM name mismatch, causing 13x underestimation. [Infrastructure] (2026-02-17)

Solution: Implemented a local _FALLBACK_PRICING dictionary in the summary tool to handle zero-cost anomalies.

3. tianhe non-impulse injectors crashed due to direct MuJoCo API calls bypassing robosuite abstractions. [Error Recovery Benchmark] (2026-02-17)

Solution: Replaced mj_name2id with robosuite model.body_name2id/geom_name2id and implemented a name-suffix resolver.

4. LLM-generated structured reports frequently produced malformed or truncated JSON. [Infrastructure] (2026-02-17)

Solution: Implemented a 4-stage parsing fallback chain including code block extraction, brace matching, and LLM-based repair.

5. SSH connections to tianhe cluster frequently timed out during long-running tasks. [Infrastructure] (2026-02-17)

Solution: Implemented a layered defense including TCP keep-alive, tmux session persistence, and SSH connection multiplexing.

6. Pretrained pathology models exhibit distribution shifts in fine-grained cortical layer identification. [MIHD] (2026-02-19)

Solution: Adopted target-domain self-supervised adaptation (STEGO, BYOL) instead of using frozen encoders.

7. InfoNCE loss convergence to NaN due to dense similarity matrix overflow in float32. [MIHD] (2026-02-19)

Solution: Applied L2 normalization, LogSumExp stabilization, and enforced float64 precision buffering.

8. BC-RNN policy crashes due to 65-dim vs 37-dim observation mismatch. [Error Recovery Benchmark] (2026-02-20)

Solution: Fixed _to_robosuite_obs() key mapping and ensured raw robot observations were passed rather than compressed state_info.

9. LeRobot-HuggingFace library incompatibility causing metadata TypeErrors. [Error Recovery Benchmark] (2026-02-22)

Solution: Pinned huggingface/datasets to versions below 4.0 to restore compatibility.

10. Windows OpenSSH incompatibility causing connection drops to compute nodes. [Infrastructure] (2026-02-19)

Solution: Purged stale socket files and disabled conflicting multiplexing parameters in .ssh/config.

Learnings

Domain Knowledge (domain)

• Data bugs (e.g., metadata/coordinate swaps) are more destructive than model bugs because code and tests can pass while results are fundamentally invalid. Verification must trace from macro-anomalies back to raw data source.
• Pure vision-only spatial clustering yields low ARI; fine-grained morphological features require multimodal fusion or domain-specific self-supervised alignment.
• VLA/foundation models lack robust zero-shot transfer between simulation environments (LIBERO to Robosuite); success requires expanding behavioral diversity (e.g., >500 demos via MimicGen) before fine-tuning.

Architecture (architecture)

• Distributed workflows require a two-phase architecture (local extraction + centralized merging) to handle multi-device environments and prevent redundant API costs.
• Policy evaluation pipelines must maintain original observation modalities; internal state extractors often drop keys required by pre-trained adapters.

Debugging (debugging)

• Framework correctness (passing unit tests) is distinct from model quality (behavioral success). GPU integration tests and video visualization are required to detect ‘hovering’ vs ‘grasping’ issues.
• High-temperature cosine similarity matrices in float32 rapidly saturate; mathematical stabilization (LogSumExp/L2) is mandatory for contrastive objectives.

Tools (tools)

• Third-party tool integration requires defensive programming (e.g., fallback pricing, name remapping) to account for upstream schema or naming mismatches.
• Framework migrations (e.g., LeRobot/HuggingFace) require strict dependency pinning to prevent silent schema corruption.

AI Usage Notes

Effective Patterns:

• ✓ Two-phase architecture for distributed data processing.
• ✓ Multi-stage fallback parsing for LLM-generated JSON.
• ✓ Layered defense for remote connection stability.
• ✓ Using AI for low-level PyTorch module implementation and automated unit test generation.
• ✓ Leveraging multi-agent task spawning for large-scale repository refactoring.

Limitations:

• ✗ Inability to independently diagnose high-level data-source bugs (relied on user’s statistical intuition).
• ✗ Lack of awareness regarding project-specific data silos and environment-specific config requirements.
• ✗ Inability to assess policy quality via video analysis without human intervention.
• ✗ AI’s tendency to propose redundant/isolated scripts instead of integrating with existing infrastructure.
• ✗ Inability of AI to autonomously navigate cluster permission/SLURM hurdles.
• ✗ Occasional misattribution of visualization artifacts due to lack of deep diagnostic context.

Next Week Outlook

Priorities include: 1) Securing cluster GPU node access through administrative coordination to unblock M14 baseline evaluations; 2) Executing the massive re-run of MIHD vision-dependent experiments with corrected coordinates; 3) Expanding the MimicGen dataset to the >500 demo threshold to enable Pi0 fine-tuning in Robosuite; 4) Completing the M6 multi-policy evaluation for the Error Recovery Benchmark (addressing BC-RNN training); 5) Transitioning MIHD from self-supervised refinement to full multimodal fusion training using the STAIG GCN architecture; and 6) Moving toward automation of the daily report deployment pipeline.

Token Usage Statistics

Peak Day: 2026-02-16 — $31.31 / 57.0M tokens

Daily Average: $15.30