Daily Report — 2026-02-01

Daily Overview

• What was done: Investigated and fixed an unexpected clustering metric drop (ARI ~0.4 to ~0.13) in the MIHD benchmark, aligned its GCN implementation with the original STAIG framework, and validated a novel GCN+UNI2+STAIG_fusion configuration.
• How it was done: Cross-referenced git histories and experimental logs to identify environment mismatches and parameter drift; restored baseline conda environments (General); patched STAIGTrainer.py and run_benchmark_core.py; updated GCNGeneEncoder to apply activations across all layers including the final projection layer.
• Impact: Restored benchmark clustering performance to expected levels (ARI >0.45), eliminated architectural drift between MIHD and STAIG codebases, and established a reliable configuration propagation protocol for future multimodal fusion experiments.

Diagnosed and resolved a critical benchmark accuracy regression in the MIHD spatial transcriptomics pipeline by correcting environment dependencies, realigning default hyperparameters with the original STAIG architecture, and patching graph convolution activation logic.

Tasks

Architecture & Strategy

• ✅ Resolve MIHD Benchmark ARI Regression — Diagnosed root causes including environment mismatch (base vs General), drifted training hyperparameters, and incorrect raw gene expression bypass; restored baseline configurations and verified metric recovery.
• ✅ Align MIHD GCN Architecture with STAIG — Identified activation function application discrepancies in GCNGeneEncoder; patched forward pass to match original STAIG’s layer-wise activation and documented cross-project architectural differences.

Implementation & Fixes

• ✅ Test GCN + UNI2 + STAIG_fusion Configuration — Configured benchmark runner to support --gene_encoder gcn; executed test suite, fixed dependency issues (torch_geometric, scikit-misc), and successfully generated visualizations and embeddings.

Problems & Solutions

Critical Issues

1. Benchmark ARI dropped from ~0.4+ to ~0.13 despite no major feature changes.

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

Key Insight: In contrastive multimodal training, minor hyperparameter drift (temperature scaling, epoch counts) and environment-level dependency mismatches severely disrupt convergence and clustering boundaries.

2. A previous pipeline update broke STAIG fusion by injecting raw 3000-dim gene expression instead of pre-computed 50-dim PCA embeddings.

Solution: Removed the raw_gene_expr bypass logic in run_benchmark_core.py; restored correct low-dimensional embedding flow to match original STAIG training dynamics.

Key Insight: Hybrid models with internal GCN layers require strict input dimension alignment; bypassing pre-processed embeddings for raw data fundamentally changes graph topology and breaks contrastive objectives.

3. MIHD’s GCNGeneEncoder omitted activation functions on the final layer, deviating from original STAIG specifications.

Solution: Updated GCNGeneEncoder.forward() to apply activations across all layers by default; added configurable parameter apply_activation_to_last_layer=True for backward compatibility.

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

Human vs AI Approaches

Strategic Level

Root Cause Identification for ARI Drop

Role	Approach
Human	Analyzed git diffs, compared successful vs failed experimental logs early on, and quickly pinpointed environmental (rpy2 dependency) and configuration drift as the primary culprits.
AI	Relied initially on iterative execution and diff analysis; corrected course after human highlighted environment mismatches and cross-referenced historical baseline states to validate fixes.

Difference Analysis: Human provided high-level architectural context and dependency requirements upfront, accelerating diagnosis. AI systematically isolated parameters through execution loops but required targeted prompts to prioritize environmental and historical baseline checks.

Cross-Project GCN Architecture Alignment

Role	Approach
Human	Proposed explicit alignment of activation functions across all GCN layers, using original STAIG net.py as the authoritative reference.
AI	Validated the hypothesis by reading source files, implemented conditional patching in GCNGeneEncoder, and generalized findings into a comparative matrix covering graph efficiency, weight calculation, and layer defaults.

Difference Analysis: Human drove theoretical alignment based on mathematical implementation standards. AI efficiently executed structural patches and abstracted practical differences (e.g., O(n^2) vs KD-tree graph building) into actionable documentation.

AI Limitations

Critical Limitations

• Initially failed to link code execution failures directly to environment dependencies (rpy2/conda mismatch), requiring human intervention to identify the correct runtime context.

General Limitations

• Reliance on iterative bash trial-and-error introduced latency; would have benefited from statically analyzing git blame or config propagation paths earlier to spot parameter drift faster.

Learnings

Key Learnings

• 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, even when encoder pipelines are functionally correct.
• Configuration-driven training loops heavily depend on explicit parameter inheritance; silently dropped yaml values during nested object initialization causes silent performance degradation that is difficult to trace without rigorous diff auditing.

Conversation Summaries

✅ Debugging ARI Drop and Aligning MIHD GCN with STAIG 05:23:52.274 | claude_code Diagnosed a critical performance regression in the MIHD spatial transcriptomics pipeline where clustering accuracy dropped from above 0.4 to approximately 0.13. Root causes were identified as environment mismatch (missing rpy2 in base conda env), drifted default hyperparameters, and an incorrect data pipeline bypass that passed raw gene expression instead of PCA embeddings. Corrections involved restoring baseline configurations, patching STAIGTrainer.py and run_benchmark_core.py, and updating GCNGeneEncoder to apply activations across all layers. The aligned configuration was tested successfully, recovering expected metrics and generating visualizations.

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