Daily Report — 2026-02-18

Daily Overview

• What was done: Debugged vision encoder coordinate swapping in spatial-omics benchmarks and audited multi-user model checkpoint repositories across two machine environments.
• How it was done: Automated cache clearing, long-running process polling, Python script generation for rollout rendering, recursive file system auditing, and academic literature cross-referencing via integrated web tools.
• Impact: Recovered significant benchmark metrics (+86% ARI), established a reproducible visualization pipeline for policy comparison, and precisely identified the data expansion gap required before proceeding with VLA fine-tuning.

DCC

• What was done: Fixed tissue_positions_list.csv column mapping errors in the MIHD pipeline and monitored extended STAIG fusion training runs.
• How it was done: Patched coordinate ordering in run_benchmark.py and data_loader.py, purged corrupted embeddings, implemented background polling for progress tracking, and queried literature standards for UNI/UNI2 patch extraction.
• Impact: Restored geometrically accurate visual patches, validated consistent ARI/NMI gains across fusion strategies, and aligned pipeline preprocessing with established pathology foundation model practices.

tianhe

• What was done: Inventoryed VLA/BC-RNN weights, created a MuJoCo policy rollout visualization tool, and assessed MimicGen dataset readiness for Pi0 fine-tuning.
• How it was done: Executed targeted file searches to catalog checkpoints, wrote visualize_policy_rollout.py, updated configuration paths and Makefile targets, verified GPU rendering pipelines, and analyzed HDF5 demo distributions across shared storage.
• Impact: Unified checkpoint access via symlinks, enabled visual baseline evaluation of policy behaviors, and clarified that only 10 source demos exist, establishing the exact MimicGen expansion threshold needed before fine-tuning can initiate.

Resolved a critical coordinate extraction bug in the MIHD spatial transcriptomics pipeline and analyzed benchmark results, while simultaneously auditing VLA/BC-RNN checkpoint availability, building policy rollout visualization tools, and mapping MimicGen data requirements for robot imitation learning.

Tasks

Architecture & Strategy

• ✅ MIHD Coordinate Bug Fix & Experiment Monitoring — Identified and corrected swapped X/Y coordinate mapping in spatial omics pipeline, cleared corrupted cache, monitored long-running STAIG fusion experiments, and analyzed intermediate benchmark metrics.
• 🔄 MimicGen Data Readiness Assessment for Pi0 Fine-Tuning — Audited HDF5 demo counts, mapped existing conversion pipelines, and identified the required ~500 demo expansion threshold before LeRobot format conversion and LoRA tuning can proceed.

Implementation & Fixes

• ✅ VLA/BC-RNN Checkpoint Inventory & Symlink Management — Cataloged all available model weights across shared HDD pools, created unified symlink structure, verified file formats/sizes, and documented missing task-specific checkpoints.
• ✅ Policy Rollout Visualization & Config Sync — Developed policy rendering script, updated benchmark configs/Makefile with correct paths, synchronized project documentation, and verified GPU rollout generation.

Problems & Solutions

Critical Issues

1. Critical coordinate swap in tissue_positions_list.csv caused transposed vision patch extraction, degrading UNI2 embeddings and benchmark metrics.

Solution: Corrected column mapping to pxl_col/pxl_row, reordered coordinate tuples, purged corrupted caches, and reran baseline experiments to validate fixes.

Key Insight: Foundation model embedding quality is extremely sensitive to raw pixel-to-spot alignment; metadata correctness must be verified before feature extraction begins.

2. Pre-trained VLA model (Pi0 LIBERO) exhibited zero-shot failure on Robosuite PickPlace, and project lacked task-specific fine-tuned checkpoints.

Solution: Constructed visualization workflow to inspect failure patterns, audited storage for existing weights, and mapped the MimicGen 10-to-500 demo expansion pipeline using existing repo scripts.

Key Insight: VLA policies require extensive source behavioral diversity before fine-tuning; direct environment transfers without task-specific data collection will fail consistently.

Human vs AI Approaches

Strategic Level

Literature validation for UNI/UNI2 preprocessing standards versus assumption-driven optimization.

Role	Approach
Human	Demanded cross-referencing with published pathology foundation model guidelines to verify patch sizes, normalization, and coordinate alignment methods before trusting pipeline outputs.
AI	Executed automated web searches and PDF fetches, synthesized technical specifications, but initially treated all findings as direct implementation rules without flagging potential version mismatches.

Difference Analysis: Human enforced external validation to prevent silent configuration drift, while AI efficiently aggregated documentation but lacked critical evaluation of source reliability and version compatibility.

Strategic pivot from exhaustive checkpoint searching to training pipeline readiness.

Role	Approach
Human	Explicitly halted deep file searches and redirected focus toward evaluating MimicGen data availability, asking directly about source demo counts and fine-tuning prerequisites.
AI	Focused on thorough automation, recursive directory scanning, and cataloging all available assets without questioning the immediate utility or compute implications of each step until interrupted.

Difference Analysis: Human provided critical strategic boundaries and domain constraints (prioritize training feasibility over exhaustive inventory), while AI optimized for completeness within a single context window.

AI Limitations

Critical Limitations

• Proposed executing a multi-step pipeline (MimicGen expansion → LeRobot conversion → LoRA fine-tuning) without explicitly flagging prerequisite storage approvals, network constraints, or A800 queue dependencies.

General Limitations

• Failed to reliably fetch full-text PDFs/arXiv papers via WebFetch tool, repeatedly returning 303/403 and ECONNREFUSED errors, forcing manual fallback workarounds.
• Lacked awareness of project-specific data silos (e.g., missed that PickPlace training data was generated offline in a separate MimicGen temp directory), leading to redundant cross-account searches.

Learnings

Key Learnings

• In spatial transcriptomics benchmarking, correcting coordinate extraction metadata yielded an 86% ARI improvement, proving that foundation model embeddings are highly sensitive to raw pixel alignment before feature extraction.
• Vision-Language-Action models like Pi0 cannot transfer zero-shot across simulation environments (LIBERO → Robosuite); reliable imitation learning on new tasks requires expanding source demos >500 via MimicGen before any fine-tuning can succeed.

Conversation Summaries

MIHD Spatial Benchmark

✅ Coordinate Bug Fix & STAIG Fusion Experiment Monitoring 00:01:02.520 | claude_code Identified a critical coordinate swap bug in tissue coordinate CSV processing that corrupted vision patches and degraded embeddings. Patched two downstream files, purged corrupted caches, and monitored long-running STAIG pipeline via background polling. Analyzed intermediate results showing significant ARI/NMI gains post-fix while researching standard UNI/UNI2 patch extraction practices via web search to validate pipeline configuration.

Error Recovery Benchmark

🔍 MimicGen Data Readiness & Pi0 Fine-Tuning Assessment 00:26:49.679 | claude_code Investigated dataset availability for training Pi0/Pi0.5 on the PickPlace task, discovering only 10 source demos with no generated variants or fine-tuned checkpoints. Mapped existing conversion scripts and noted that approximately 500 expanded demos are required before LoRA fine-tuning can commence. Outlined the exact pipeline steps needed to proceed once data generation is authorized.

✅ VLA & BC-RNN Checkpoint Inventory 05:32:29.978 | claude_code Audited cross-directory HDD pools to catalog available model weights across BC-RNN, Robomimic, and OpenPI/VLA variants. Created symlinked checkpoint structure for unified access, verified file formats and sizes, and confirmed absence of task-specific fine-tuned weights. Provided a clear mapping of usable versus empty directories and highlighted storage distribution across shared accounts.

✅ Policy Rollout Visualization & Config Sync 04:52:08.850 | claude_code Developed policy rendering script to visualize Robosuite MuJoCo rollouts as MP4 videos with HUD overlays and success/failure metadata. Updated configuration paths to use new checksum symlinks, extended the Makefile with visualization targets, and documented changes in the project summary markdown. Successfully verified GPU availability and generated baseline random policy rollouts for inspection.

Token Usage