Daily Journal — 2026-02-20

Today’s Overview

• What I did: Completed targeted STAIG fusion comparison experiments and added embedding cache read/write support to run_benchmark.py
• How I did it: Isolated staig_fusion testing via a temporary script, then refactored using CacheManager to reuse the pipeline’s existing caching infrastructure
• Why it matters: Eliminates the risk of bugs introduced by standalone evaluation scripts; run_benchmark.py can now load cached embeddings directly (near-instant), while also supporting custom cache names (e.g., scan_uni2) for full fusion comparisons

Ran targeted tests on STAIG fusion for the MIHD project, discovered a double-normalization bug in eval_scan_fusion.py, and introduced pipeline-level embedding caching into run_benchmark.py.

Today’s Tasks

Architecture & Strategy

• ✅ Investigated STAIG fusion preprocessing discrepancy — Confirmed that staig_fusion had never been formally run on 151673; found that eval_scan_fusion.py was not passing staig_alignment_config, causing STAIGTrainer to apply StandardScaler internally again, resulting in a double-normalization bug.
• ✅ Added embedding cache read/write to run_benchmark.py — Introduced CacheManager to check embeddings_cache/ before gene/vision encoding; on cache hit, loads directly (skipping encoder instantiation); on miss, extracts and saves to cache. Supports override to force re-extraction, accepts arbitrary vision_encoder names (relaxed argparse choices), and splits vision cache into three variants: standard/freq/staig_strict.
• ✅ Ran targeted STAIG fusion comparison on 151673 — Killed the 2-hour long-running task and ran staig_fusion × {UNI2_raw, SCAN(UNI2)} separately. Results: ARI of 0.3929 / 0.3880 respectively — nearly identical. Root cause: STAIG’s internal StandardScaler+PCA preprocessing cancels out SCAN’s optimization benefits.

Problems & Solutions

Key Issues

1. Double-normalization bug in staig_fusion within eval_scan_fusion.py (staig_alignment_config not passed)

Solution: After identifying the root cause, decided to use run_benchmark.py directly (which correctly passes staig_alignment_config) rather than fixing the standalone script

Key insight: Reusing a correct existing implementation is more reliable than patching a bug; what should be cached is raw embeddings before normalization — post-encoder normalization should run after loading, which is the correct order

2. run_benchmark.py re-instantiates the encoder and re-extracts embeddings every run, even when the pipeline already has cached results

Solution: Integrated CacheManager from pipeline/cache_manager.py — checks cache before encoding, writes to cache after extraction

Key insight: The caching infrastructure from the pipeline’s two-stage architecture can be reused directly — no need to reinvent the wheel

General Issues

3. eval_scan_fusion.py long-running task ran for 2 hours without finishing — impractical to wait for results

Solution: Killed the long-running task, created a temporary script _test_staig_scan.py to test staig_fusion in isolation — results in ~30 seconds

Key insight: Breaking down the full comparison (18 combinations) into a single-method targeted test enables rapid hypothesis validation

Human Thinking vs. AI Thinking

Strategic Level

Strategy for fixing the eval_scan_fusion.py bug

Role	Approach
Human	Why not just use the original benchmark script? Proposed using the existing run_benchmark.py instead of fixing the standalone script
AI	Planned to fix the staig_alignment_config passing issue in eval_scan_fusion.py and prepared to add a build_effective_staig_profile call

Analysis: The human thought architecturally, prioritizing reuse of a validated implementation; the AI focused on fixing the specific bug in the existing script without stepping back to reconsider the approach

Relationship between run_benchmark.py and pipeline caching

Role	Approach
Human	Expected run_benchmark.py to support cache reads just like the pipeline — saw this as a reasonable design expectation
AI	Initially treated run_benchmark.py and the pipeline as independent systems, not seeing a need for caching in run_benchmark.py

Analysis: The human had a clearer system design expectation (unified caching); the AI only recognized the design gap and implemented it after the human explicitly pointed it out

AI Limitations

Significant Limitations

• After discovering the staig_fusion bug, defaulted to fixing the standalone script rather than recommending the use of the existing correct implementation — required human initiative to shift the approach
• Did not proactively identify the design gap of run_benchmark.py lacking pipeline-level caching support — only began implementing it after the human explicitly pointed it out

General Limitations

• Waited nearly 2 hours before alerting the user that the task might not finish — should have identified the likely timeout earlier and suggested interrupting sooner

Today’s Takeaways

Core Learnings

• STAIG fusion’s internal StandardScaler+PCA preprocessing absorbs the gains from external embedding optimization (ARI difference between SCAN and UNI2_raw was only 0.005), demonstrating that STAIG’s robustness to vision embeddings stems from its built-in normalization pipeline
• In complex experimental systems, always prioritize reusing existing, validated tool paths (e.g., run_benchmark.py) — standalone scripts are prone to subtle bugs like preprocessing inconsistencies
• Embedding caches should store raw embeddings before normalization; post-encoder normalization should run after loading — this is the correct pipeline design pattern and ensures consistent results across different invocation paths

Session Summaries

🔄 STAIG fusion targeted testing and preprocessing discrepancy investigation 18:06:33.022 | claude_code The session began by continuing to run eval_scan_fusion.py, but the long-running task (2+ hours) was interrupted in favor of testing staig_fusion in isolation. Results showed nearly identical ARI for UNI2_raw vs SCAN(UNI2) (0.393 vs 0.388), with STAIG’s internal preprocessing canceling out SCAN’s optimization. Further investigation revealed a double-normalization bug in eval_scan_fusion.py and confirmed that staig_fusion had never been formally run on 151673. The human proposed a better approach: use run_benchmark.py directly and add caching support to it. The session ended with a plan in place.

✅ Implemented embedding cache read/write for run_benchmark.py 21:22:59.238 | claude_code Implemented embedding caching support in run_benchmark.py as planned: introduced CacheManager, checks cache before gene/vision encoding and loads directly on hit; extracts and writes to cache on miss. Vision cache supports three variants (standard/freq/staig_strict), argparse choices for vision_encoder were relaxed, and the override parameter was passed at both call sites. Syntax validation passed, CacheManager import succeeded, and the cache already contains pca/mlp/scgpt gene and uni2/hipt/resnet50/uni vision embeddings.

Token Usage

Overview

Metric	Value
Total Tokens	19,315,584
Input Tokens	42,559
Output Tokens	5,886
Cache Creation	1,847,937
Cache Read	17,419,202
Cache Hit Rate	90.4%
Total Cost (USD)	$10.0013

Model Breakdown

Model	Input	Output	Cache Creation	Cache Read	Cost	Share
claude-haiku-4-5-20251001	27,101	1,112	1,208,807	10,978,447	$2.6415	26.4%
claude-opus-4-6	15,453	4,769	620,974	6,409,204	$7.2822	72.8%
claude-sonnet-4-6	5	5	18,156	31,551	$0.0776	0.8%

Usage by Device

Device	Total Tokens	Input	Output	Cost
DCC	2,363,322	11,314	159	$0.5498
tianhe	16,952,262	31,245	5,727	$9.4515