Daily Journal — 2026-02-06

Today’s Overview

• What was done: Comprehensively diagnosed the ARI performance gap of staig_fusion in the MIHD project (0.21 → target 0.56), identified and quantified five key implementation differences, and completed a strict-alignment override refactor; also fixed a report rendering bug in the benchmark tool, implemented a full GitHub Pages automated publishing pipeline, and added CLI interactive upload functionality
• How it was done: By line-by-line code comparison (ripgrep/sed/codex toolchain), cross-repository parameter tracing (original STAIG notebook, train_img_config.yaml, adata_processing.py), and patch-based changes; MIHD changes covered 4 core files, and benchmark changes were verified end-to-end locally
• Why it matters: staig_fusion is now strictly aligned with original STAIG semantics (default mclust + HVG features + STAIG hyperparameters); the coordinate scale mismatch and other key differences have been quantified and are pending fixes; the benchmark tool has undergone a key upgrade from local script to publicly runnable + auto-publishing tool, allowing users to submit results to a public leaderboard with one click

DCC

• What was done: Completed all MIHD diagnostics and implementation work on the HPC cluster: identified 5 root causes of the STAIG performance gap, implemented strict-alignment override refactor, fixed mclust errors and missing tqdm, and deep-diagnosed four key differences including coordinate scale mismatch in slide 151508
• How it was done: Read the original STAIG notebook and config files, compared them one-by-one against the MIHD implementation, measured image dimensions (13332×13332) and coordinate ranges, and edited code directly in the /hpc/group/yizhanglab/zt81/MIHD directory
• Why it matters: Completed the core engineering work for staig_fusion’s strict semantic alignment, confirmed coordinate scale mismatch as the highest-priority fix, and laid the code foundation for subsequent ARI improvements

TzJsDesktop

• What was done: Fixed the bar chart rendering bug in the benchmark HTML report, and implemented end-to-end GitHub Pages automated publishing and CLI interactive upload functionality
• How it was done: Pinned the Plotly JS CDN version and forced numeric lists to fix rendering issues; implemented a public submission pipeline via three GitHub Actions workflows and a relay architecture; added an interactive upload prompt to the CLI
• Why it matters: The benchmark tool completed its key upgrade from a local script to a publicly runnable + auto-publishing website

Across DCC cluster and TzJsDesktop, systematically diagnosed and quantified the five root causes of MIHD staig_fusion’s performance gap versus the original STAIG, while delivering the benchmark tool’s bar chart fix, GitHub Pages automated publishing pipeline, and CLI interactive upload functionality.

Today’s Tasks

Architecture & Strategy

• ✅ Diagnose the ARI/NMI performance gap between STAIG fusion and the original STAIG — Systematically compared the complete code paths of the original STAIG notebook (ARI=0.562) and the MIHD benchmark (ARI=0.21/0.4849), ultimately identifying five key differences: ① full-resolution image coordinate scale mismatch (most critical — coordinates incorrectly compressed from x:2579-11821 to x:386-1773); ② mclust unavailable on HPC, silently falling back to kmeans; ③ reversed gene preprocessing order (MIHD: HVG first, then normalize/log/scale; original STAIG: the reverse); ④ pseudo-label cluster count 300 vs 80; ⑤ hyperparameter and image transform differences
• ✅ Implement GitHub Pages automated publishing pipeline — Added scripts/ingest_submissions.py (validation/deduplication/sanitization), scripts/submit_result.py (relay/dispatch submission), three GitHub Actions workflows (accept-submission, daily-publish, pages-deploy), and data/ queue files to enable end-to-end public data collection and automated publishing
• 🔄 Implement strict-alignment override for STAIG fusion — Modified models/STAIGTrainer.py, scripts/run_benchmark.py, and 2 other files to enforce strict STAIG semantics: default mclust, HVG raw features, STAIG hyperparameter profile, spatial majority-vote refinement, pseudo-label cluster count 300/80. Syntax validation passed, but encountered an mclust dimension error at runtime; full validation is still pending
• ✅ Fix benchmark report bar chart rendering bug — Fixed the short-bar issue for high-scoring entries: pinned Plotly JS CDN to 3.3.1, changed DataFrame Series to Python list, set y-axis rangemode=‘tozero’
• 🔄 Fix mclust “dimension is zero” runtime error — mclust threw ‘svd(data, nu=0): a dimension is zero’; added embedding shape guards in Python (2D check, non-zero rows/cols, sample count ≥ cluster count); root cause (which upstream step produces an empty embedding) is pending confirmation after the next run
• ✅ Add interactive upload prompt to benchmark CLI — Added post-run logic in benchmark/cli.py to ask the user whether to upload results, supporting –upload, –no-upload, –relay-url, and BENCHMARK_RELAY_URL environment variable; defaults to no-upload in non-interactive mode

Implementation & Fixes

• ❌ Diagnose low GPU utilization — Both CPU and GPU utilization were low; analyzed as a single-threaded I/O wait bottleneck (single-threaded patch extraction, batch_size=32, empty_cache() called per batch); proposed three solutions: increase batch_size, multi-process DataLoader, pre-fetched patch cache; awaiting user decision
• ✅ Remove Historical Trends section from report and fix AttributeError — Removed the trend chart generation call from benchmark/report.py, added a compatibility stub method to prevent AttributeError, and fully cleared the call path
• ✅ Generate AGENTS.md contributor guide — Generated standard-format AGENTS.md files for both the MIHD repository (310 words) and the benchmark repository (329 words), covering project structure, build commands, code conventions, testing guidelines, and commit conventions
• ✅ Add tqdm progress bar to UNI/UNI2 image encoding — Added tqdm to the batch inference loop in scripts/run_benchmark.py; also discovered that strict STAIG mode forces the visual encoder to UNI regardless of the user’s choice (e.g., UNI2), an override behavior that is opaque to users

Problems & Solutions

Critical Issues

1. MIHD staig_fusion’s ARI (0.21/0.4849) is far below the original STAIG notebook (0.562), with unknown root cause

Solution: Systematic comparison of the two code paths identified five root causes: ① full-resolution image (13332×13332) coordinates are still multiplied by the hires scale factor (0.15), causing patch sampling points to severely miss tissue regions (most critical); ② mclust unavailable on HPC, silently falling back to kmeans; ③ reversed gene preprocessing order (MIHD: HVG first, then normalize/log/scale; original STAIG: the reverse); ④ pseudo-label cluster count 300 vs 80; ⑤ incomplete hyperparameter alignment and image transform differences

Key Insight: The name staig_fusion itself promises “equivalent to STAIG” semantics; allowing the default behavior to differ significantly from the original method causes a large volume of silent errors; coordinate scale mismatch is the highest-priority fix

2. Plotly bar chart rendering error: high scores show short bars, visually inverted logic

Solution: Pinned Plotly JS CDN version to match the Python installation version (3.3.1), changed Series to list, set rangemode=‘tozero’

Key Insight: Plotly 6.x uses binary-encoded array serialization; when incompatible with the plotly-latest CDN version, it causes data parsing errors. Strict version consistency is required.

3. mclust clustering throws ‘svd(data, nu=0): a dimension is zero’, and silently falls back to kmeans when mclust is unavailable on HPC

Solution: Added embedding shape validation in Python (2D check, non-zero rows/cols, sample count ≥ cluster count); root cause is pending confirmation after the next run; need to install rpy2 and R mclust package to eliminate the silent fallback

Key Insight: R-side error messages are unintuitive in Python; thorough pre-validation with clear error messages including shape info should be added on the Python side; when R packages like mclust are unavailable, silent fallback must emit a prominent warning instead of silently changing the clustering method

General Issues

4. Strict STAIG mode silently overrides visual encoder to UNI; user unaware (thought they were using UNI2, tqdm not appearing)

Solution: Add a clear encoder-override log message; UNI2 progress bar not showing is because UNI is actually running

Key Insight: Global override behavior that is opaque to users must be explicitly logged; otherwise users will waste time debugging problems that don’t exist

5. Low GPU utilization (CPU and GPU both low)

Solution: Not fully resolved. Analyzed as a single-threaded I/O wait bottleneck: patch extraction is the main bottleneck; proposed batch size adjustment, multi-process DataLoader, and pre-fetched patch cache

Key Insight: Both CPU and GPU being low indicates the program is waiting in single-threaded I/O, not that CPU is the bottleneck; performance bottlenecks in visual feature extraction are typically in data preprocessing, not the model’s forward pass

6. create_trend_chart AttributeError: method was commented out but still called in generate_html

Solution: Added a stub method returning an empty chart, and fully removed the calling code

Key Insight: Commenting out a function definition is not the same as deleting it; all callers must also be removed

Human Thinking vs AI Thinking

Strategic Level

Diagnosing STAIG Performance Gap: Direction and Decisions

Role	Thinking
Human	Directly identified the problem (STAIG fusion scores far below original STAIG), explicitly provided example.ipynb as ground truth, and decided “staig_fusion’s intended semantics is to align with STAIG — override directly without preserving old behavior”; continuously asked “why is performance worse” to drive deeper analysis; the observation “CPU is also low” immediately ruled out CPU as the bottleneck
AI	Used systematic code tracing (measuring image dimensions 13332×13332, computing coordinate compression ratio, line-by-line comparison of both codebases) to identify 5 quantified discrepancies across multiple tool calls; however, required explicit user direction to proceed with implementation

Analysis: Human decisions were more strategic (name implies semantics, override directly); AI excelled at systematic technical detail comparison and quantification. AI’s code tracing uncovered the coordinate scale issue that the human didn’t directly point out, but efficiency depended on multiple rounds of tool calls.

GitHub Pages Public Submission Architecture Design

Role	Thinking
Human	Proactively proposed using an interactive prompt to ask users whether to upload results, and explicitly required a relay intermediary layer to protect repository write access
AI	Contributed the complete queued architecture (relay → dispatch → queue file → daily batch processing → commit/push → Pages), along with strict schema validation, hash-based deduplication, and IP anonymization to prevent abuse

Analysis: The human’s core intuition was “confirm consent before upload” and “security isolation”; AI translated that intuition into a concrete, actionable technical architecture.

Identifying Global Override Logic and Gene Preprocessing Order

Role	Thinking
Human	Quickly noticed the logs showed ‘UNI’ instead of ‘UNI2’, directly identified the problem; drove analysis purely by asking “why is performance worse”
AI	Discovered the reversed order of normalize/log/scale vs HVG selection by line-by-line comparison of MIHD prepare_gene_features vs original STAIG adata_processing.py; but missed the existing global override logic when implementing tqdm

Analysis: Humans have a clearer picture of their own runtime environment and expected behavior, spotting log anomalies at a glance; AI can systematically compare code details but tends to miss existing global logic, requiring user observations to compensate.

AI Limitations

Critical Limitations

• Repeatedly incomplete actions: when fixing the mclust dimension error, only added guard checks without tracing the upstream root cause; when removing the trend chart, initially only removed the call without handling the function definition, requiring a second fix; cross-codebase systematic comparison was inefficient — gap analysis was identified incrementally over multiple tool calls rather than producing a complete structured list at once

General Limitations

• Tends to overlook existing global override logic: when adding UNI2 tqdm, failed to recognize that strict STAIG mode forces the visual encoder override; before implementing the STAIG alignment refactor, did not proactively check for existing uncommitted dirty files, requiring the user to explicitly inform before proceeding
• Did not pre-check available modules in the HPC environment (e.g., scanpy), causing ModuleNotFoundError during Python script validation, forcing indirect validation methods and increasing debugging cycles; overly optimistic about local tool call reliability (conda run on Windows fails intermittently)
• Offered multiple solution options for low GPU utilization but did not proactively suggest profiling tools (e.g., py-spy, nvprof) to precisely locate the actual bottleneck; instead made educated guesses based on code reading

Today’s Takeaways

Core Takeaways

• MIHD and the original STAIG have five quantifiable key implementation differences (priority-ordered): ① full-resolution image coordinate scale mismatch (most critical — coordinates incorrectly compressed from x:2579-11821 to x:386-1773, causing patch sampling to severely miss tissue regions); ② mclust unavailable on HPC, silently falling back to kmeans; ③ reversed gene preprocessing order (HVG first vs normalize/log/scale first); ④ pseudo-label cluster count 300 vs 80; ⑤ hyperparameter and image transform differences. The ARI gap (0.21 → 0.56) is primarily driven by ①②③
• The correct layered architecture for GitHub Pages static publishing with public data collection: public user → relay (validation/rate limiting/anonymization) → repository_dispatch → queue file → daily batch workflow (deduplication/sanitization/CSV append) → commit/push → Pages auto-deploy
• Interface names must strictly match actual behavior: the name ‘staig_fusion’ inherently promises “equivalent to STAIG” semantics; allowing major differences in default behavior creates a large volume of silent errors; global override behaviors (such as forcing the encoder to UNI) must be explicitly logged
• Plotly 6.x defaults to binary-encoded array serialization; when incompatible with the older CDN (plotly-latest), bar lengths are rendered incorrectly. Solution: pin CDN version to match the Python package version, and always pass Python list instead of Series

Practical Takeaways

• R interface errors (e.g., mclust) are unintuitive in Python; add thorough pre-validation on the Python side with clear error messages including shape information; when R packages like mclust are unavailable, silent fallback must emit prominent warnings instead of silently changing the clustering method

Session Summaries

MIHD

🔄 Complete Diagnosis of MIHD STAIG Fusion Performance Gap and Strict Alignment Implementation 23:02:06.469 | codex Completed full-pipeline diagnosis and refactoring of MIHD STAIG fusion across multiple sessions: generated AGENTS.md contributor guide; systematically compared the original STAIG notebook against the MIHD implementation, identifying 5 key differences (clustering, gene input, hyperparameters, FFT preprocessing, pseudo-label cluster count), with user decision “override directly”; modified 4 core files to enforce strict alignment (default mclust + HVG features + STAIG hyperparameters), syntax validation passed; deep-diagnosed slide 151508 and found coordinate scale mismatch (most critical difference), reversed gene preprocessing order, and mclust unavailability on HPC; also handled runtime mclust dimension error, missing UNI2 tqdm, and low GPU utilization.

benchmark

✅ Benchmark Report Bug Fixes and Complete GitHub Pages Automated Publishing Pipeline 04:04:15.843 | codex Starting with generating the AGENTS.md contributor guide, fixed the bar chart rendering inversion bug (Plotly 6.x CDN version pinning), removed the Historical Trends section, fully implemented the GitHub Pages auto-update pipeline (relay architecture + three GitHub Actions workflows + strict validation/deduplication/sanitization scripts), added interactive upload prompt to CLI (–upload/–no-upload flags), and fixed create_trend_chart AttributeError. All changes verified locally; code is ready to push.

Token Usage

Claude Code

Overview

Metric	Value
Total Tokens	200,112
Input Tokens	18
Output Tokens	12
Cache Created	44,167
Cache Read	155,915
Cache Hit Rate	77.9%
Total Cost (USD)	$0.0709

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-haiku-4-5-20251001	18	12	44,167	155,915	$0.0709	100.0%

Codex

Overview

Metric	Value
Total Tokens	51,436,067
Input Tokens	51,253,314
Output Tokens	182,753
Reasoning Tokens	95,065
Cache Read	48,179,456
Total Cost (USD)	$16.3692

Model Breakdown

Model	Input	Output	Reasoning	Cache Read	Cost	Share
gpt-5.2-codex	9,350,444	20,842	7,770	8,641,408	$0.0000	0.0%
gpt-5.3-codex	41,902,870	161,911	87,295	39,538,048	$1.2266	7.5%