Weekly Report — 2026-W05 (2026-01-26 ~ 2026-02-01)

This week focused on critical troubleshooting of the MIHD spatial transcriptomics pipeline, specifically addressing a significant benchmark accuracy regression. Efforts successfully restored ARI metrics to expected levels by rectifying environment mismatches, realigning training hyperparameters with the original STAIG architecture, and patching GCN activation logic discrepancies.

Weekly Overview

Metric	Value
Date Range	2026-01-26 ~ 2026-02-01
Active Days	1 / 7
Total Conversations	1
Projects	1
Tasks Completed	3
Tasks In Progress	0
Total Tokens	1,675,357
Total Cost	$2.19
Daily Average Cost	$2.19

Project Progress

MIHD Spatial Transcriptomics Pipeline (1 days active) — 🔄 active

Accomplishments:

• Restored benchmark ARI from ~0.13 to >0.45
• Aligned GCN implementation with STAIG architecture
• Validated GCN + UNI2 + STAIG_fusion configuration

Blockers:

• ⚠️ Initial environmental dependency mismatches (rpy2/conda)

Key Tasks

• ✅ Resolve MIHD Benchmark ARI Regression — Diagnosed and fixed root causes including environment mismatch, drifted hyperparameters, and incorrect raw gene expression bypass to restore metric recovery via baseline configurationserrediation.’,‘date’: ‘2026-02-01’ # Note: Date logic per requirements, though grouping is weekly
• ✅ Align MIHD GCN Architecture with STAIG — Identified and patched activation function discrepancies in GCNGeneEncoder to ensure architectural fidelity with the original STAIG framework via layer-wise activation updates.
• ✅ Test GCN + UNI2 + STAIG_fusion Configuration — Configured benchmark runner for GCN gene encoder and successfully generated embeddings and visualizations after fixing torch_geometric/scikit-misc dependencies.

Problems & Solutions

1. Benchmark ARI dropped from ~0.4+ to ~0.13 despite no major feature changes. [MIHD Spatial Transcriptomics Pipeline]

Solution: Identified incorrect conda environment execution and drifted defaults in STAIGTrainer.py; reverted tau, num_epochs, and edge weight methods to verified baseline values.

2. Pipeline update broke STAIG fusion by injecting raw 3000-dim gene expression instead of 50-dim PCA embeddings. [MIHD Spatial Transcriptomics Pipeline]

Solution: Removed the raw_gene_expr bypass logic in run_benchmark_core.py to restore correct low-dimensional embedding flow.

3. MIHD’s GCNGeneEncoder omitted activation functions on the final layer, deviating from STAIG specifications. [MIHD Spatial Transcriptomics Pipeline]

Solution: Updated GCNGeneEncoder.forward() to apply activations across all layers via a new configurable parameter: apply_activation_to_last_layer=True.

Learnings

Domain Knowledge (domain)

• In multimodal fusion benchmarks, switching clustering backends (e.g., R’s mclust vs Python’s kmeans) without accounting for algorithmic differences can artificially suppress metric scores.

Architecture (architecture)

• Applying non-linearities to the final projection layer in channel-expansion GCNs significantly alters feature space geometry, making strict architectural replication essential for reproducibility.

Debugging (debugging)

• Configuration-driven training loops are highly sensitive to explicit parameter inheritance; silent drops in yaml values during nested object initialization cause difficult-to-trace performance degradation.

AI Usage Notes

Effective Patterns:

• ✓ Using AI for efficient structural patching and generating comparative matrices of architectural differences.

Limitations:

• ✗ AI failed to initially link code execution failures to specific environment dependencies (rpy2/conda mismatch).
• ✗ Heavy reliance on iterative bash trial-and-error rather than static analysis of config propagation paths.

Next Week Outlook

With the MIHD benchmark accuracy restored and the GCN architecture aligned, priority should shift to scaling the GCN + UNI2 + STAIG_fusion experiments and performing more rigorous regression testing on new configuration profiles to prevent parameter drift.

Token Usage Statistics

Peak Day: 2026-02-01 — $2.19 / 1.7M tokens

Daily Average: $2.19