Weekly Report — 2026-W25 (2026-06-15 ~ 2026-06-21)

This week was defined by high-stakes scientific validation and deep architectural refactoring across multiple domains. Key achievements included resolving a significant methodological paradox in MIHD spatial transcriptomics through Leiden clustering migration, establishing performance baselines for RoboCasa robotics models (Predict 2.5 and GR00T), and completing a major UI/UX and backend stabilization cycle for the TokenMonitor and Desktop Video applications. Despite significant technical hurdles involving HPC environment stability and Windows-specific hardware/driver behaviors, the team successfully transitioned AI infrastructure to the AWS Claude platform and established robust, automated workflows for project documentation and data synchronization.

Weekly Overview

Metric	Value
Date Range	2026-06-15 ~ 2026-06-21
Active Days	4 / 7
Total Conversations	17
Projects	17
Tasks Completed	27
Tasks In Progress	3
Total Tokens	1,248,913,784
Total Cost	$1,235.61
Claude Code Token	1,184,933,504
Claude Code Cost	$1,176.38
Codex Token	63,980,280
Codex Cost	$59.23
Daily Average Cost	$247.12

Project Progress

MIHD (Spatial Transcriptomics) (5 days active) — ✅ completed

Accomplishments:

• Identified ’encoder paradox’ where gene FMs outperform standard encoders in retrieval
• Migrated clustering from mclust to Leiden with hyperparameter sweeps
• Verified embedding provenance via MD5 hashing and archived legacy datasets

Blockers:

• ⚠️ Initial mislabeling of baseline features (UNI2 vs BYOL) caused invalid ablation results

RoboCasa-Min & Error Recovery Benchmark (4 days active) — 🔄 active

Accomplishments:

• Established NVIDIA Predict 2.5 as a successful baseline (71.1% success)
• Completed GR00T-N1.6 leaderboard reproduction with 5x speedup
• Validated M16 recovery efficacy via Fisher exact test (p < 0.05)

Blockers:

• ⚠️ Incomplete training checkpoints and unverified augmented data caused initial 0% success rates
• ⚠️ OpenPI evaluation scripts have structural bugs preventing direct deployment

TokenMonitor (3 days active) — 🔄 active

Accomplishments:

• Implemented unified Device Management and single-instance hardening using Windows named mutexes
• Fixed stale usage data bugs via time-bucketed caching in Rust
• Resolved repository divergence and performed v0.14.0 hygiene

Blockers:

• ⚠️ Complexity in synchronizing Tauri app behavior when synced folders are deleted

Desktop Video (2 days active) — ✅ completed

Accomplishments:

• Released v5.0 preview with automated DMG packaging via bash/AppleScript
• Refactored SwiftUI architecture to eliminate double-window startup bugs
• Optimized thumbnail rendering to prevent CPU spikes

AI Companion / Gadget (2 days active) — 🔄 active

Accomplishments:

• Developed paginated HTML DAG visualization workflow
• Implemented repository-to-DAG documentation with Mermaid graphs

Key Tasks

• ✅ STAIG to Leiden Clustering Migration & Ablation — Audited STAIG logic and implemented a configurable Scanpy Leiden pipeline with hyperparameter sweeps across 11 DLPFC sections to replace mclust with optimized performance metrics, specifically tuning n_neighbors as the primary driver for ARI improvement. Added to 2026-06-19.
• ✅ Desktop Video UI/UX & Performance Refactoring — Unified title bar/sidebar glass effects and centralized window management in AppDelegate to resolve redundant window instantiation and CPU spikes during rendering. Added to 2026-06-19.
• ✅ M16 Coffee Evaluation — Executed full inference for recovery vs normal models on 1000 scenes, providing statistical validation of recovery model efficacy through Fisher exact testing. Added to 2026-06-21.
• ✅ TokenMonitor: Unified Device Management & Single-Instance Hardening — Consolidated remote devices and SSH hosts into a single view and implemented a Windows named mutex fallback to prevent startup failures across different OS environments. Added to 2026-06-17.
• ✅ Error Recovery Benchmark Diagnostic — Identified that 0% success rates were due to incomplete training and unverified augmented data rather than model weakness, shifting strategy to high-quality data regeneration. Added to 2026-06-19.
• 🔄 pi0.5 Full Leaderboard Reproduction — Executing full evaluation on pi0.5 model aligned with leaderboard protocols (50 tasks, 50 trials) on node2. Added to 2026-06-19.

Problems & Solutions

1. Desktop Video app displayed two windows on startup and experienced CPU spikes during navigation. [Desktop Video]

Solution: Removed WindowGroup from SwiftUI Scenes to rely solely on AppDelegate for window management and implemented lazy thumbnail instantiation. Added to 2026-06-19.

2. TokenMonitor single-instance startup failure when lock port is occupied by un-terminatable processes. [TokenMonitor]

Solution: Implemented a Windows named mutex (Local\TokenMonitor-SingleInstance) as a robust fallback lock mechanism. Added to 2026-06-17.

3. Zero success rate in RoboCasa Error Recovery Benchmark (coffee task) due to suspected weak policy. [RoboCasa]

Solution: Diagnosed root cause as incomplete training (5000/10000 steps) and unverified augmented data, shifting strategy to data regeneration. Added to 2026-06-19.

4. Daily export was silently skipping sync due to missing rclone binary and incorrect configuration paths. [DCC/HPC]

Solution: Reinstalled rclone v1.74.3 static binary on HPC and updated summarize config paths. Added to 2026-06-21.

Learnings

Domain Knowledge (domain)

• In spatial transcriptomics, ’n_neighbors’ is the most critical hyperparameter for Leiden clustering, often more impactful than resolution settings.
• NVIDIA RoboCasa ecosystem distinguishes between Predict 2.5 (Policy) and Cosmos 3 (World Model); distinguishing these is vital for benchmark compatibility.

Debugging (debugging)

• On Windows with NVIDIA drivers, system memory fallback (WDDM) can mask Out-Of-Memory (OOM) errors by swapping to shared RAM, causing unexplained latency rather than immediate crashes.

Architecture (architecture)

• For single-instance applications, combining TCP-based IPC with OS-level primitives like named mutexes provides necessary redundancy against occupied or protected ports.

Tools (tools)

• Scientific provenance is best verified through direct content hashing (MD5) of embedding arrays rather than relying on potentially unreliable metadata labels.

AI Usage Notes

Effective Patterns:

• ✓ Multi-agent research workflows for feasibility studies (RoboCasa)
• ✓ Iterative patching of SwiftUI components through mobile/desktop profiling feedback
• ✓ Automated generation of documentation via AST scanning and static graph generation

Limitations:

• ✗ Failure to anticipate macOS-specific file locking (Finder/QuickLook) during DMG automation
• ✗ Inability to detect non-standard Windows driver behaviors (memory swapping vs. exception throwing)
• ✗ Difficulty with precise visual/pixel-perfect alignment in custom SwiftUI components without human oversight

Next Week Outlook

Priorities will center on completing the pi0.5 full leaderboard reproduction and transitioning the Error Recovery Benchmark to a full retraining pipeline with validated augmented data. Additionally, focus will shift toward expanding the AI Companion’s DAG visualization capabilities and resolving the remaining TokenMonitor MacSync edge cases.

Token Usage Statistics

Peak Day: unknown — $887.89 / 906.1M tokens

Daily Average: $247.12