Weekly Report — 2026-W06 (2026-02-02 ~ 2026-02-08)

This week focused on stabilizing the MIHD multimodal pipeline through rigorous architectural alignment with academic baselines, resolving critical benchmark documentation gaps, and engineering a secure automated submission infrastructure. Significant progress was made in establishing a robust evaluation paradigm by porting the QueST RM-Ideal metric into MIHD, enabling high-fidelity spatial niche benchmarking, and resolving foundational issues in metric masking and environment reproducibility across HPC clusters.

Weekly Overview

Metric	Value
Date Range	2026-02-02 ~ 2026-02-08
Active Days	5 / 7
Total Conversations	10
Projects	10
Tasks Completed	19
Tasks In Progress	1
Total Tokens	228,397,828
Total Cost	$79.84
Claude Code Token	10,833,847
Claude Code Cost	$4.70
Codex Token	212,910,372
Codex Cost	$72.11
Daily Average Cost	$15.97

Project Progress

MIHD Multimodal Pipeline (5 days active) — 🔄 active

Accomplishments:

• Aligned architecture and hyperparameters with STAIG baseline
• Implemented E2E fusion pipeline architecture fixes
• Recovered missing benchmark documentation and visualization outputs
• Built RM-Ideal evaluation framework for spatial structure benchmarking

Blockers:

• ⚠️ Initial benchmark instability due to architectural divergence
• ⚠️ Documentation drift between raw CSV data and markdown logs

QueST Framework (2 days active) — 🔄 active

Accomplishments:

• Integrated Wasserstein WWL graph kernel formulations
• Corrected SameLabel@K metric logic to prevent target exclusion

Blockers:

• ⚠️ Heavy environmental dependencies causing import overhead

Infrastructure & CI/CD (2 days active) — ✅ completed

Accomplishments:

• Deployed secure, consent-driven automated benchmark reporting pipeline
• Established bidirectional SSH tunnels for remote API routing

Key Tasks

• ✅ MIHD STAIG Alignment & Runtime Diagnostics — Consolidated forensic alignment patching, preprocessing sequence reconciliation, and upstream tensor crash resolution to enforce strict benchmark parity and stabilize inference execution.
• ✅ Automated Benchmark Reporting & Public Submission Architecture — Unified chart rendering fixes, CLI opt-in consent flows, and GitHub Actions dispatch automation into a secure relay system for external benchmark ingestion.
• ✅ Error Recovery Benchmark v4.0 MVP Development — Implemented core validators including tip_over logic, rewrote analysis pipelines, and integrated dynamic validator selection.
• ✅ QueST RM-Ideal Metric Reverse Engineering & Validation — Extracted the Wasserstein WWL graph kernel formulation from repository embeddings and drafted a complete pseudo-code implementation.
• ✅ Build MIHD RM-Ideal Evaluation Framework — Created utilities/rm_ideal.py and scripts/evaluate_rm_ideal.py to benchmark MIHD embeddings against ground-truth spatial structure.
• ✅ 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.

Problems & Solutions

1. Benchmark results claiming ~0.45 ARI were absent from markdown logs and output files were fragmented. [MIHD Multimodal Pipeline]

Solution: Used shell audit commands to locate raw CSV logs and treated them as the single source of truth, rebuilding markdown logs from CSV data.

2. Unstable clustering accuracy (ARI 0.09-0.35) due to implementation gaps from academic baselines. [MIHD Multimodal Pipeline]

Solution: Conducted forensic alignment with the official STAIG reference, correcting dropout masking, edge weight normalization, and temperature decay.

3. “SameLabel@50” metric returned 0.0 because target spots were excluded from the evaluation mask. [QueST Framework]

Solution: Revised logic to allow querying patches within the same label set while only excluding self-matches for structural ranking.

4. Runtime crashes due to upstream zero-dimensional tensors and Plotly serialization issues. [Infrastructure & CI/CD]

Solution: Implemented early tensor shape validation and enforced numeric coercion with zero-clamped y-axes.

5. Network restrictions prevented external API access and generic proxy exports failed in restricted clusters. [Infrastructure & CI/CD]

Solution: Constructed explicit bidirectional SSH tunnels (ssh -R/-L) with manual SOCKS5 forwarding and forced curl proxy overrides.

Learnings

Architecture (architecture)

• Benchmark workflows must decouple documentation from execution logs by consuming raw CSVs directly to prevent version drift.
• Secure public submission ecosystems must decouple consent, ingestion, validation, and deployment layers to maintain repository integrity.

Debugging (debugging)

• Downstream evaluation failures typically trace back to silent upstream tensor mismatches or serialization gaps, requiring early dimensional guards.
• Achieving strict benchmark parity requires validating implicit defaults and upstream preprocessing chains rather than just exposed API flags.

Domain Knowledge (domain)

• Type-matching metrics must strictly allow retrieval of target-class spots to avoid trivial zero-out scenarios in space-aware model benchmarking.

Tools (tools)

• Hardware states and API routing require explicit runtime verification; conda activation and generic proxy exports are insufficient in isolated HPC environments.

AI Usage Notes

Effective Patterns:

• ✓ Systematic directory auditing and CSV parsing to verify claims against documentation
• ✓ Forensic code comparison against academic repositories to identify semantic divergences
• ✓ Automated synthesis of documentation (AGENTS.md) by analyzing project structure

Limitations:

• ✗ Initial reliance on incomplete markdown summaries rather than raw data
• ✗ Difficulty simulating downstream execution flows (biological/rendering) through static analysis
• ✗ Contextual blindness to framework-level overrides during cross-repo debugging

Next Week Outlook

Prioritize the completion of the unified End-to-End (E2E) GCN training framework (STAIGTrainerE2E) to transition from two-stage inference to fully trainable spatial fusion networks. Scale the newly established RM-Ideal evaluation framework to larger datasets and validate the automated benchmark submission pipeline with real-world external contributions.

Token Usage Statistics

Peak Day: 2026-02-08 — $48.16 / 139.7M tokens

Daily Average: $15.97