Daily Report — 2026-02-05
Daily Overview
- What was done: Evaluated benchmark instability and implemented foundational E2E fusion architecture fixes.
- How it was done: Conducted forensic code comparison against academic baselines, refactored CLI arguments for flexible encoder routing, injected deterministic GPU settings, and resolved package dependencies incrementally.
- Impact: Restored expected clustering fidelity, enabled modular gene encoding within a unified training paradigm, and established a stable execution baseline for scaling multimodal spatial transcriptomics experiments through precise architectural alignment.
Resolved critical architecture and configuration gaps in the MIHD multimodal pipeline by aligning code with the original STAIG baseline, fixed E2E fusion routing errors, stabilized environment dependencies, and advanced planning for a unified end-to-end GCN training framework.
Tasks
Architecture & Strategy
- ✅ Performance Analysis & Reference Baseline Alignment — Evaluated benchmark instability and identified critical hyperparameter and architectural divergences from the original STAIG academic repository.
- ✅ E2E Fusion Pipeline Architecture Fixes — Refactored CLI parsing, function signatures, and preprocessing routing to resolve blocking errors and enable flexible modular encoder inputs.
- 🔄 End-to-End GCN Architecture Design — Drafting unified STAIGTrainerE2E module to transition from two-stage inference to fully trainable spatial fusion networks.
Implementation & Fixes
- ✅ Environment Configuration & Hardware Verification — Resolved recursive package dependencies, verified default GPU execution flows, and enforced deterministic training settings for reproducibility.
Problems & Solutions
Critical Issues
1. Unstable and low clustering accuracy (ARI 0.09–0.35) diverged significantly from baseline expectations due to implementation gaps.
Solution: Conducted forensic alignment with the official STAIG reference, correcting critical deviations in dropout masking, edge weight normalization, feature clipping, and temperature decay.
Key Insight: Mathematical precision in probability normalization dictates cross-domain information flow; minor inversions collapse multimodal convergence stability.
2. Flexible E2E fusion was blocked by rigid CLI constraints and fatal routing errors due to missing dynamic argument handling.
Solution: Updated argparse choices, injected **kwargs into core functions, and modified validation logic to gracefully handle both raw and pre-encoded latent spaces.
Key Insight: Dynamic configuration passing is essential for decoupling gene encoding from fusion strategies, enabling flexible multi-modal experimentation.
Human vs AI Approaches
Strategic Level
End-to-End Architecture Strategy & Baseline Alignment
| Role | Approach |
|---|---|
| Human | Recognized the strategic necessity of unifying two-stage inference into end-to-end training, identified mathematically correct edge weight formulations, and demanded exact baseline equivalence rather than accepting suboptimal metrics as acceptable bugs. |
| AI | Initially defaulted to standardized deep learning conventions and rigid single-modality assumptions; later traced configuration mismatches via file analysis, implemented iterative dependency patches, and applied deterministic GPU flags based on explicit prompts. |
Difference Analysis: Human provided high-level architectural direction, domain-specific mathematical correctness, and strict quality benchmarks; AI executed forensic code tracing, iterative fix implementation, and environment debugging strictly within the scope of human-guided corrections.
AI Limitations
Critical Limitations
- Initially over-relied on standardized deep learning defaults, leading to rigid single-modality assumptions and oversight of critical deterministic GPU settings (CuBLAS/cuDNN), which caused non-reproducible inference. Encountered environmental resolution limits with recursive dependencies, requiring manual intervention.
Learnings
Key Learnings
- Strict architectural and hyperparameter alignment with original academic repositories must precede custom framework debugging, as minor mathematical deviations drastically degrade multimodal convergence. Dynamic configuration passing (e.g., **kwargs) and modular encoder support are essential prerequisites for stable end-to-end fusion pipelines.
Conversation Summaries
✅ Comprehensive Pipeline Alignment, E2E Repair & Architecture Redesign Planning 2026-02-05 (Consolidated) | claude_code Multiple sessions focused on diagnosing benchmark instability and aligning the MIHD framework with the official STAIG academic reference. Critical architecture and configuration fixes were implemented to resolve fusion routing errors and enable flexible encoder support. The workflow advanced from performance gap analysis and hyperparameter correction toward a planned transition to a unified end-to-end GCN+STAIG training paradigm, establishing stable environmental and hardware baselines for scale.