Daily Report — 2026-06-04

Daily Overview

• What was done: Recovered lost MIHD experimental data through bug fixes and reruns while verifying that MIHD’s StaigFusionTrainer is equivalent to the original STAIG implementation. Designed a portable ‘Vendor-Tier-1’ architecture for AI Companion to resolve portability issues caused by absolute path dependencies. Performed full-scope onboarding of the Gadget Python project, producing a 667-item refactoring plan and executing safe code cleanup.
• How it was done: Utilized Python scripting for model accuracy comparisons, static code audits, and empirical testing protocols for STAIG equivalence. Analyzed TypeScript vs. Python system architectures and leveraged deterministic AST scanning with LLM-assisted classification (throttled for rate limits) to inventory the Gadget repo. Implemented ECL feature guards and characterization tests to verify behavioral preservation.
• Impact: Confirmed TEDDY+Uni2 as the top performing model for Visium HD and proved MIHD’s fidelity to original STAIG algorithms. Established a clear ‘clone-and-go’ strategy for AI Companion by vendorizing hooks/gates. Secured the Gadget codebase with a staged execution plan, debunked false-positive bugs to prevent unnecessary churn, and safely applied one low-risk cleanup.

DCC

• What was done: Fixed QueST job failures due to UnboundLocalError, reran HD experiments, and validated STAIG fusion equivalence. Analyzed the architectural divergence between ‘gadget’ workflow and ‘ai-companion’ to resolve portability conflicts.
• How it was done: Debugged benchmark_rm_ideal.py, submitted SLURM jobs for GPU/HPC access, cloned original repos for static audits, and engaged in alignment protocols to define scope boundaries for ECL feature guards.
• Impact: Recovered months of lost experimental data; verified technical correctness of the fusion module; resolved critical design conflicts regarding ‘specs before edit’ gates and absolute path injection.

TzJsDesktop

• What was done: Executed end-to-end onboarding for the Gadget Python repository and analyzed AI Companion migration strategies. Debunked a false-positive bug warning in parsing logic.
• How it was done: Ran Python AST backbone for inventory, used parallel Sonnet agents (throttled) for function classification, wrote characterization tests, and performed git-commit anchoring analysis for gate security.
• Impact: Completed 100% of analysis phases with a staged-execution state; generated a 667-item risk plan; successfully applied one verified fix in parsers.py; clarified that centralized caching was intentional design.

Restored and validated MIHD experimental results (QueST/HD) via STAIG equivalence checks, designed a portable ‘Vendor-Tier’ architecture for AI Companion to replace absolute paths, and executed a comprehensive codebase onboarding of the Gadget project, generating a risk-tiered refactoring plan and resolving architectural conflicts between legacy workflow tools and modern automation gates.

Tasks

Architecture & Strategy

• ✅ MIHD Result Recovery & Validation — Fixed UnboundLocalError in benchmark_rm_ideal.py, reran QueST and HD jobs, verified encoder-path accuracy (PCA+Uni2 + ResNet50 top on DLPFC; TEDDY+Uni2 on Visum HD). Recovered ~2 months of lost code via NetApp snapshot and git recovery.
• ✅ Gadget Project Onboarding via /cconboard — Executed full-scale codebase onboarding for the Gadget project (Phases 0-4, 7-8). Generated a 667-item risk-tiered refactoring plan and ECL feature guards. Staged Phase 5 execution to avoid interfering with uncommitted changes.
• ✅ STAIG Fusion Equivalence Verification — Performed static and empirical comparison between MIHD’s StaigFusionTrainer and original STAIG. Established that bit-identical output is impossible due to RNG but verified equivalence via seeded ARI/NMI comparisons and isolated contrastive cores.
• ✅ AI Companion Portability Architecture Design — Designed the ‘Vendor-Tier-1’ architecture to replace absolute-path dependencies in ai-companion with local relative paths. Vendors Tier-1 tools (hooks, gates, skills) as relative-path resources while keeping heavy engines external but lazily loaded.

Implementation & Fixes

• ✅ Gadget Code Cleanup (OB-515) — Verified and fixed redundant ‘import re as _re’ in parsers.py. Wrote characterization tests to lock behavior, applied the fix, and verified 167 passing tests.
• ✅ HD Job Acceleration — Diagnosed and resolved the HD job’s 24-hour queue stall by switching from an unavailable a6000 GPU to an available 5000Ada.
• ✅ Debunk False Positive Bug in weekly_summary.py — Investigated flagged ‘_cache_dir’ ignoring ‘reports_dir’. Confirmed via cross-file grep and logic review that this is intentional centralized caching design, not a bug. No changes made.

Problems & Solutions

Critical Issues

1. AI Companion workflows break portability across clones due to absolute path injection. Parallel AI agent workflow hit API rate limits (429 errors).

Solution: Proposed hybrid architecture: vendor Tier-1 tools as relative paths, externalize heavy engines. Switched parallel LLM fan-out to throttled sequential background workflow.

Key Insight: Portability is compromised by absolute paths; large-scale parallel LLM usage requires strict rate limiting and human-provided ground truth to recover analysis quality from ‘phantom’ items.

2. Conflict between ‘specs before edit’ gate and existing ECL (DEC-004/001) regarding absolute paths and self-authorization risks (BF-03).

Solution: Analyzed threat models: automated pre-edit gates cannot rely on AI-generated state. Implemented a design where hooks prevent Claude edits and git pre-commit anchors verify human intent.

Key Insight: Automated gates must anchor to immutable signals (git history) rather than internal flags to mitigate self-authorization attacks.

3. QueST job failed due to UnboundLocalError: ‘sections’ was used before assignment in benchmark_rm_ideal.py. HD Slurm job stalled for over 24 hours.

Solution: Moved variable assignments to appropriate scopes and rerun jobs. Switched from unavailable a6000 GPUs to available gc500Ada cards via variant script.

Key Insight: Python variable scoping requires strict sequential pre-assignment; heterogeneous GPU partitions can cause deadlocks if specific hardware types are strictly requested but unavailable.

Human vs AI Approaches

GPU Partition & STAIG Equivalence Design

Role	Approach
Human	Identified stuck HD job and requested general GPU check; suggested verifying STAIG equivalence but acknowledged RNG makes bit-identical comparison impossible.
AI	Queried specific Slurm node states to pinpoint available 500Ada cards; translated the user’s equivalence constraint into a rigorous protocol using seeded ARI/NMI and isolated contrastive cores.

Difference Analysis: User provided high-level intent and constraints (RNG impossibility); AI provided granular diagnostics and statistically sound experimental design.

Architectural Alignment & Gate Security

Role	Approach
Human	Provided key insight that ‘human edits need git-commit-like records’ to address F-02 limitations in automation gates.
AI	Integrated this insight to refine the gate design into a two-layer system (PreToolUse for AI, git pre-commit for humans) and debunked false-positive bugs via contextual consistency checks.

Difference Analysis: Human’s intuition about immutable logs resolved architectural deadlocks; AI’s deeper inspection prevented unnecessary churn on intentional design patterns.

AI Limitations

General Limitations

• Initial Slurm GPU queries did not distinguish between total allocation and available GPU types, leading to inaccurate availability assessments for specific hardware strings.
• AI’s initial flagging of _cache_dir as a bug was a false positive based on superficial parameter usage, requiring deep contextual analysis to correct.
• Parallel AI agent workflows hit organization API rate limits, causing incomplete analysis; initial function name lookups were unreliable without explicit context, creating ‘phantom’ items.

Learnings

Key Learnings

• MIHD’s StaigFusionTrainer is virtually identical to original STAIG on the default path; divergence stems from n_clusters source differences, not architecture. Merging AI Companion’s pipeline with gadget’s portable installation approach resolves core conflicts.
• To make TypeScript CLI tools portable, they must vendor dependencies or use npx-style resolution. The ‘/cconboard’ phased approach is highly effective for large Python repos but requires staging execution if the working tree is dirty.
• For ‘specs before edit’ gates, self-authorization attacks (BF-03) are real; gates must anchor to external state (git/ask) rather than internal flags. Heterogeneous GPU partitions require precise node-state diagnostics to avoid queue stalls.

Conversation Summaries

MIHD (DCC)

✅ Recovering MIHD Results & Verifying STAIG Equivalence 01:30:00 | claude_code Restored ~2 months of lost code and fixed benchmark_rm_ideal.py UnboundLocalError. Reran QueST/Visium HD jobs, confirming TEDDY+Uni2 as top performer for Visium HD. Conducted a deep audit proving MIHD’s StaigFusionTrainer is mathematically equivalent to original STAIG via seeded metrics.

AI Companion (Desktop)

✅ Aligning Gadget Workflow with AI Companion Portability Goals 01:53:47 | claude_code Analyzed structural differences between ‘gadget’ and ‘ai-companion’. Identified critical conflict with ECL decisions regarding absolute paths. Designed a vendor-tier architecture to achieve ‘clone-and-go’ portability and resolved scope for ‘specs before edit’ gates using git-commit anchoring.

Gadget Repo Onboarding (Desktop)

✅ /cconboard Execution and Refactoring Plan Generation 02:30:00 | claude_code Executed full /cconboard workflow on Gadget repo. Completed analysis phases with a 667-item risk-tiered plan. Debunked false-positive bugs and successfully executed one safe cleanup in parsers.py with full characterization test verification.

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