Daily Report — 2026-02-08
Daily Overview
- What was done: Ported the external RM-Ideal algorithm from pseudo-code directly into QueST source files, built standalone evaluation modules in MIHD, and benchmarked spatial embeddings against graph-kernel similarity metrics.
- How it was done: By implementing Wasserstein Weisfeiler-Lehman message passing with optimal transport solvers on pure Python/graph networks, constructing isolated test scripts for algorithm verification, and correcting evaluation mask logic for structural retrieval metrics.
- Impact: Establishes a rigorous ground-truth evaluation paradigm for MIHD embeddings to validate spatial niche capture capability while unifying QueST’s precomputed metrics within its executable pipeline.
Integrated ground-truth RM-Ideal spatial niche comparison into QueST’s native code and developed a new MIHD framework to benchmark embedding accuracy against this structural metric.
Tasks
Architecture & Strategy
- ✅ Build MIHD RM-Ideal Evaluation Framework — Create utilities/rm_ideal.py and scripts/evaluate_rm_ideal.py to benchmark MIHD embeddings against ground-truth spatial structure using WWL graph kernels.
- ✅ Implement RM-Ideal Score in QueST — Add wwl_node_features, _wasserstein_distance, and rm_score functions to QueST utils.py; implement compute_rm_scores in trainer.py.
- ✅ Debug & Fix SameLabel@K Metric Logic — Identified and corrected a logical flaw where target patch spots were excluded from the evaluation mask, artificially zeroing out type-level retrieval metrics.
Implementation & Fixes
- ✅ Execute Layer_3 Niche Benchmark on Section 151508 — Ran RM-Ideal evaluation comparing RMA-Ideal scores vs uni_staig_fusion embedding cosine similarities and generated spatial visualization heatmaps.
Problems & Solutions
Critical Issues
1. “SameLabel@50” metric returned exactly 0.0 during niche retrieval benchmarking because there were no target spots left to rank after mask filtering.
Solution: Revised the evaluation logic to allow querying patches from within the same label set (e.g., comparing Layer_3 to Layer_3) while explicitly excluding self-matches only for Precision@K structural ranking comparisons.
Key Insight: Type-level retrieval accuracy and structure-level rank agreement must use distinct evaluation masks; removing target classes entirely from a candidate pool trivializes type-matching metrics.
2. Python execution failed due to heavy environmental dependencies (e.g., missing ‘umap’ module) during core algorithm testing.
Solution: Extracted target functions standalonely and executed via isolated scripts or conda run, successfully bypassing framework-level import overhead while verifying mathematical correctness (RM=1.0 for identical self-matches).
Key Insight: Algorithmic logic should be validated structurally in independent environments first before integrating with heavy frameworks like scanpy/scikit learn.
Human vs AI Approaches
Strategic Level
Cross-Project RM-Ideal Benchmark Design and Metric Definitions
| Role | Approach |
|---|---|
| Human | Defined the requirement to port QueST’s graph-kernel RM evaluation to MIHD to directly benchmark embedding fidelity. Critically identified and rectified a fundamental evaluation bias where niche targets were incorrectly excluded from retrieval candidates, fundamentally altering benchmark validity. |
| AI | Translated architectural requirements into multi-file code implementations (utils/rm_ideal.py, spatial_utils), handled complex dependency isolation by writing test harness scripts, generated spatial visualizations comparing structural ground truths vs embedding gradients, and performed path resolution for legacy environments. |
Difference Analysis: The human drove the strategic evaluation design, caught subtle logical flaws in metric masking that AI overlooked, and dictated the exact porting scope between projects; the AI handled complex mathematical graph implementations (Wasserstein distance optimization), environmental isolation, and code scaffolding.
AI Limitations
Critical Limitations
- Initially evaluated SameLabel retrieval rates using rigid spatial exclusion masks that removed all candidate niches matching the query type, indicating a lack of semantic awareness for edge-case metric behaviors in graph queries.
General Limitations
- Encountered persistent failures in direct Python execution within heavily configured or mismatched conda environments (e.g., missing core libraries like
umap), necessitating inefficient fallbacks to isolate scripts and complex environment activation loops.
Learnings
Key Learnings
- When benchmarking space-aware models, structural similarity metrics (RM-Ideal) and cell-type matching metrics (SameLabel@K) evaluate orthogonal capabilities; type-matching must strictly allow retrieval of target-class spots to avoid trivial zero-out scenarios.
Conversation Summaries
QueST
✅ Internal RM-Ideal Integration and Verification
04:42:56 | claude_code
The session focused on porting the external, pre-computed RM-Ideal score algorithm directly into QueST’s source code (src/utils.py and trainer.py). Although initial exploration revealed a partially existing implementation spanning WWL message passing and Wasserstein optimal transport solvers, comprehensive standalone unit tests were written to mathematically verify correctness. The AI successfully validated that the self-similarity scores reach 1.0 and structural dissimilarities drop toward 0.0, confirming mathematical alignment with the project’s pseudo-code specifications.
MIHD
✅ MIHD Embedding Framework using RM-Ideal Ground Truth
05:36:39 | claude_code
The user directed the development of a novel evaluation pipeline within MIHD to benchmark embedding capabilities against ground-truth spatial structures. The AI implemented ported modules (utils/rm_ideal.py), extended spatial utilities without external framework dependencies (squidpy-free), and created a CLI tool for niche query retrieval. Upon executing benchmarks on section 151508, the user identified that the SameLabel@K metric was incorrectly nullified when querying single-class niches due to restrictive masking logic. The AI corrected the candidate pool inclusion strategy, reran the benchmark, and generated comparative visualizations demonstrating moderate rank agreement (Spearman r=0.42) between embedding gradients and actual structural ground truths.