Daily Report — 2026-06-22

General Overview

• Completed Work: Completed the repair of the key infrastructure for the AI Companion dual-agent ecosystem, performed a deep code audit on desktop video functions, conducted basic research on spatial transcriptome clustering, and deployed/verified the Tianhe-2 robot model benchmark environment.
• Implementation Methods: Implemented TypeScript adapter to coordinate the hook pattern between Claude Code and Codex; used automated adversarial scripts combined with manual verification for desktop video repair; aggregated ARI metrics for FM and training models; deployed the NVIDIA GL library in HPC containers that only support computing functions to achieve GPU rendering.
• Impact: Enables seamless use of AI Dev Companion across different agents, stabilizes candidate versions of desktop video, clarifies the “FM paradox” in transcriptome data, and removes the obstacle of waiting for model weights acquisition in Cosmos3-Nano inference deployment.

DCC

• Completed Work: No important interaction sessions were recorded.
• Implementation Methods: None
• Impact: None

MacBook

• Completed Work: Restored interrupted desktop video adversarial review; executed local proxy and network diagnosis for remote infrastructure settings; established skill symbol links for Claude Code.
• Implementation Methods: Manually fixed observer leaks and security range errors in SlideshowController; monitored Tianhe-2 deployment using SSH tunnel; corrected symbol link path generation script.
• Impact: Resolved 10 serious desktop video errors; achieved global Claude Code skill installation despite initial configuration errors; maintained continuity of remote access.

TzJsDesktop

• Completed Work: Deployed the wm_detect code library onto Tianhe-2; diagnosed and fixed hook failures in Codex for AI Companion; provided initialization guidance for new projects.
• Implementation Methods: Used bash/SSH for file integrity verification and environment repair; implemented TypeScript adapter for Claude/Codex load normalization; debugged stdin/BOM processing issues.
• Impact: Ensured GPU rendering support for the Tianhe-2 execution environment; restored AI Companion functionality in Codex; clarified project startup processes.

athena.egr.duke.edu

• Completed Work: Established global Claude Code skills; used in MIHD analysis for benchmark metric comparison.
• Implementation Methods: Installed “ponytail” plugin and 6 global skills via corrected symbol links; accessed baseline logs to achieve reproducible results.
• Impact: Clarified the core contributions of MIHD research (FM retrieval and clustering) and enabled unified skill usage across hosts.

This phase focused on solving cross-platform hook compatibility issues for the AI Companion framework, completing desktop video adversarial security review, establishing global Claude Code skills, and advancing spatial transcriptome (MIHD) analysis and robot environment setup (Tianhe-2/Cosmos3-Nano) work.

Tasks

Architecture and Strategy

• ✅ Completed desktop video adversarial review and repair — After verifying 7 new features against adversarial tests, fixed observer leaks, security range errors, and playback controller issues in SlideshowController.
• ✅ Solved AI Companion dual-agent hook compatibility issues — Implemented TypeScript adapter for Claude/Codex hook pattern, fixed Windows stdin/BOM issues, and updated installation scripts to support both agents.
• ✅ Deployed RoboCasa/wm_detect onto Tianhe-2 A800 Cluster — Packaged the repository, verified integrity on remote servers, installed vllm==0.19.1 and enabled Cosmos3 plugin, achieved GPU OpenGL rendering through staged NVIDIA libs.
• ✅ MIHD spatial transcriptome FM and training model analysis — Aggregated ARI metrics, generated comparison charts, showing that scGPT/TEDDY performed similarly to PCA in clustering but better in retrieval, while the original HVG dominated in clustering.
• ✅ FM and training model analysis — Compared basic models (scGPT/TEDDY) with PCA and HVG baselines using MIHD metrics, generating comparison bar charts and tables.
• ✅ Solved Codex PostToolUse hook failures — Diagnosed and fixed serious errors caused by tool name mismatch (apply_patch vs Edit/Write) and path corruption, which prevented Codex from executing PostToolUse hooks.
• ✅ Found labeled pi0.5 dataset on Tianhe-2 — Found 33,419 labeled pi0.5 playback videos in shared_deps/openpi/eval_results, verified labels based on summary JSON, and generated a balanced list of 80 episodes.
• ✅ Enabled GPU OpenGL/EGL rendering on Tianhe-2 — Downloaded NVIDIA driver 535.104.12, extracted GL libs, and configured staged deployment via glvnd supplier to bypass llvmpipe software rendering in MuJoCo.
• 🔄 QueST model reproduction plan — Fixed script for running two slides, submitted SLURM jobs on DLPFC slides; fixed hard-coded batch size assumptions for small datasets.
• 🔄 QueST model reproduction plan — Planned and started reproduction of QueST model on DLPFC slides 151673/151675, including environment setup and script writing.
• ✅ Implemented dual-agent hook compatibility layer — Created a unified TypeScript adapter to handle Claude Code and Codex event load, including Windows-specific stdin issues (BOM, TTY status).
• 🔄 Installed Cosmos3-Nano service environment — Installed vllm==0.19.1 on Tianhe-2 and built vllm-cosmos3 git plugin. Import verification succeeded.

Implementation and Fixes - ✅ Deployed robocasa-test onto Tianhe-2 — Packed local git repository, transferred via SCP to HDD_POOL/tangzijia/robocasa-test on Tianhe-2, and verified file integrity.

• ❌ Downloaded Cosmos3-Nano model weights — Started downloading 35GB of nvidia/Cosmos3-Nano weights, but stalled due to upstream failure of the local proxy (Clash).
• ✅ Verified CUDA 12.8 compatibility on A800 — Ran Torch CUDA 12.8 matmul test, confirming 535 drivers support cu128 framework despite the host being on 12.2 driver.
• ✅ Updated CLAUDE.md for wm_detect package — Analyzed new wm_detect/ and cosmos_eval_scaffold/ directories, and updated CLAUDE.md to reflect the “World-Model Error Detection MVP” architecture.
• ✅ Installed global Claude skills — Fixed symbol link path errors, successfully installed 6 AI auxiliary skills (ccdebug, ccdiscuss, etc.) as global links in ~/.claude/skills/.
• ✅ Re-generated ablation plots — Re-generated 11 slide ablation plots, corrected panel labels to (STAIG_posted/Original), and removed the ‘(none-enc)’ suffix.
• ✅ Installed global skills — Fixed symbol link path errors, successfully installed 6 AI auxiliary skills (ccdebug, ccdiscuss, etc.) as global links in ~/.claude/skills/.
• ✅ Verified AGENTS.md status in gadget repository — Checked if there is a contributor guide in the gadget repository to avoid covering critical documents.
• ✅ Updated installation and registration scripts — Modified CLI and script installer, configured skills/commands for Claude and Codex, allowing users to target specific proxies.
• 🔄 Solved CI/CD test timeout issues — Found npm run test stalled due to vitest watch mode; switched to npx vitest run to pass all 1687 tests.

Issues and Solutions

Key Issues

1. Codex PostToolUse hook failed due to architectural mismatch with Claude hooks, especially in tool name (apply_patch vs Edit/Write) and path handling.

Solution: Implemented TypeScript adapter layer to standardize load; updated hook matcher; fixed Windows stdin BOM/TTY issues and removed outdated configuration items.

Key Insight: Different LLM clients use different internal architectures to handle tool execution hooks; a unified adapter layer is needed to support multiple proxies, with special attention to operating system-level input stream differences.

2. SlideshowController in Desktop Video suffers from observer leaks and security range errors during rapid video switching.

Solution: Added explicit observer removal logic, fixed security range bookmark release timing, and repaired parallel review script execution using thunk instead of promises.

Key Insight: Event-driven UI requires strict observer lifecycle management; if promise objects are directly passed to functions expecting delayed calls, asynchronous workflow scripts often silently fail.

3. Ambiguity in spatial transcriptome results lies in whether the basic model (scGPT/TEDDY) is superior to traditional baselines.

Solution: Aggregated ARI metrics by chapter, used a unified encoder to generate fair comparisons; confirmed “FM paradox”: the basic model performs well in cross-sample retrieval but worse in single-slide clustering compared to the original HVG.

Key Insight: The embedded basic model has unique advantages: superior in broad data understanding (retrieval), but inferior in local structure accuracy (clustering) compared to engineered baselines like HVG.

4. MuJoCo rendering degrades to CPU (llvmpipe) on Tianhe-2 only compute nodes, hindering evaluation.

Solution: Installed NVIDIA GL/EGL user-space libraries in stages, configured glvnd supplier path to enable GPU rendering in containers lacking complete drivers.

Key Insight: Computing containers usually separate CUDA computation from OpenGL graphics; if the host driver version supports it, manually install user-space graphics libraries to achieve GPU rendering.

5. QueST training script crashes due to hard-coded batch size assumptions on small datasets.

Solution: Checked source code, identified hardcoded logic, and repaired export scripts to explicitly set batch_num for two-slide operation.

Key Insight: Public science models typically contain implicit dataset size assumptions; source code inspection before integration is crucial to avoid crashes during small subset execution.

General Issues

6. Claude Code automatic skill installation failed due to invalid symbol link path placeholders.

Solution: Fixed bash loop in symbol link generation script, removed invalid suffixes, and ensured valid file links.

Key Insight: Automated script generation may introduce minor string format errors; manual verification of file integrity after instantiation is essential.

7. Hugging Face model download still stays at 0 KB/s when the progress indicator shows activity.

Solution: Found HF_HOME directory configuration error causing token authentication bypass; fixed by exporting HF_TOKEN and verifying login status.Key Insight: Custom root directories typically change the credential lookup path; when performing authentication downloads in non-standard environments, explicit injection of environment variables is required.

Human and AI Approaches

AI-Enhanced Hook Architecture and Execution Environment

Role	Approach
Human	Users clearly recognize the strategic need for dual agent support (Claude/Codex) and provide debugging constraints; manually refactor the UI of Desktop Video to prioritize user experience purity over traditional contract compliance.
AI	AI designs technical solutions: implement TypeScript adapters to standardize cross-platform architectures, diagnose Python/Node execution environments, and maintain architectural consistency of Desktop Video’s backend after UI changes.

Difference Analysis: Humans define high-level requirements and prioritize user experience/results; AI fills the gap by handling low-level protocol differences, cross-platform edge cases (Windows BOM), and maintaining backend stability.

Spatial Transcriptome Model Evaluation Strategy

Role	Approach
Human	Users lead comparative analysis strategies and question equivalence between models; correct the semantic definitions of “Original” and “Posted” STAIG results to ensure data integrity.
AI	AI performs metric aggregation, visualization generation, and source code inspection. AI correctly identifies that FM ≈ training model in clustering, but differs in retrieval scenarios.

Difference Analysis: Humans provide experimental design and semantic foundations; AI provides statistical validation and code-level integration support, including fixing initial label confusion issues.

Tianhe-2 Deployment and Resource Optimization | Role | Approach |

|——|——| | Human | Despite version risks, users still use Cosmos3-Nano; initially thought it was necessary to reproduce the RoboCasa environment. | | AI | AI verified driver compatibility, identified existing labeled pi0.5 data (saving a lot of computation time), and resolved GPU rendering hardware limitations via lib staging. |

Difference Analysis: Humans accept architectural risks to pursue new models; AI optimizes resource usage by finding existing data and solving infrastructure obstacles, but users ensure strict benchmark matching requirements are met.

AI Limitations

Key Limitations

• The first run of the adversarial review script failed because AI incorrectly passed a Promise object to the parallel execution helper instead of a Thunk function, resulting in silent failures during the validation phase.
• String sorting of version numbers during PyPI exploration was too simple, preventing detection of vllm 0.19.1 version.

General Limitations

• Scripts patched via scp occasionally fail to execute due to intermittent SSH tunnel interruptions, leading to “no output” states, making direct state inspection difficult for debugging.
• Unable to independently resolve network bottlenecks (local Clash proxy); users need to intervene at home to restore upload speed of large model weights.
• Claude Code’s automatic generation of symbolic links contains string format errors (%/), requiring manual investigation and correction of bash logic.
• Initial PDF parsing cannot extract specific experimental settings of QueST; AI must manually inspect the text stream and metadata, rather than relying on direct PDF rendering.

Lessons Learned

Key Experiences

• The base model (scGPT/TEDDY) performs well in cross-sample niche retrieval, but is inferior to the original HVG in single slice clustering—this is a key finding in spatial transcriptome analysis.
• When using a hook system to support multiple LLM clients, rely on official SDK documentation to verify the tool_input structure, as their naming conventions and data formats often differ significantly.
• The Tianhe-2 A800 node can run CUDA 12.8 on 535 drivers (CUDA 12.2) via minor-version compatibility, confirmed by successful Torch matmul tests.
• Containerized HPC environments typically separate compute and graphics drivers; GPU rendering can be enabled on compute-only nodes without root privileges by locally installing the NVIDIA GL user space library.
• The async/await mode of the adversarial review script’s orchestration logic requires robust error handling; failing to encapsulate asynchronous calls as thunks may result in reports that appear “successful” or silent.
• QueST’s adversarial batch removal requires a certain number of batches/donors to run correctly; using only 2 slices from the same donor causes code crashes due to hardcoded shapes.
• Semantic version strings require numerical comparison logic rather than dictionary order for handling major version changes (e.g., 0.9 vs. 0.19).

Practical Experience

• Always use non-interactive flags (e.g., vitest run) in automated test scripts to prevent process suspension in CI/AI environments.
• ARI metric clustering is very sensitive to random seeds (mclust EM); results must be evaluated as a distribution across multiple seeds, not a single point.

Dialogue Summary

AI Development Auxiliary Tools / Mini-tools

✅ Dual-Agent Hook Compatibility and Initialization Guide 02:26:31 | claude_code/codex By implementing a TypeScript adapter layer, the Hook load is unified with Claude Code standards, resolving key PostToolUse failures on Codex. Fixed Windows-specific stdin handling (BOM/TTY) and updated installation scripts for cross-agent support. Also provides guidance on initializing new projects using the framework’s slash command workflow.

Desktop Video

✅ Anti-auditing Review and Bug Fixes for Seven New Features 20:43:55 | claude_code Completed anti-auditing reviews and stability checks for seven new features. Fixed 10 critical errors, including observer leaks in SlideshowController, improper security range handling, and playback controller issues. All fixes were tested with xcodebuild and documented.

MIHD - QueST Query Results and Reproducibility

• Base Model Analysis and QueST Setup 00:18:31 | claude_code Analyzed MIHD’s QueST-style retrieval results and confirmed the “FM Paradox” phenomenon: FMs perform better in retrieval than in clustering. ARI metrics were plotted using corrected seeds/tags. By fixing the batch size limit of small datasets, QueST models are starting to be reproduced on DLPFC slices.

RoboCasa Min Benchmark / wm_detect

🔄 Tianhe-2 Deployment and Cosmos3-Nano Environment Setup 00:41:00 | claude_code Deployed the wm_detect code library on the Tianhe-2 A800 cluster. Enabled GPU rendering on compute-only nodes by installing the NVIDIA GL library in stages, and verified CUDA 12.8 compatibility. Installed vllm==0.19.1 with the Cosmos plugin. Existing pi0.5 evaluation data was found and did not need re-generation. Model weight download is currently blocked due to proxy issues.

Token Usage