Daily Report — 2026-06-19
Tasks
Architecture and Strategy
- • Full pi0.5 leaderboard reproduction — Comprehensive evaluation of the pi0.5 model according to the leaderboard protocol (50 tasks, 50 runs). Atomic tasks are being processed on node2. Estimated completion time: approximately 18–22 hours.
- ✅ GR00T-N1.6 full leaderboard reproduction — Optimized via parallel inference, 5x faster, smoke testing and full evaluation completed on node4 (4 GPU cores). Results have been compared with target benchmarks.
- ✅ NVIDIA Predict 2.5 baseline deployed for RoboCasa — Clarified the ambiguity between Cosmos 3 and Predict 2.5 models; a Slurm-based pipeline was implemented (baseline success rate: 71.1%). Interactive download issues in batch mode were resolved, and HF authentication was verified.
- ✅ MIHD ablation study: STAIG baseline correction — Identified and corrected the incorrect label “original STAIG” (using UNI2 instead of BYOL). Real STAIG data was regenerated using BYOL embeddings, the HVG baseline was added, and accurate ablation plots were generated. A discrepancy was found between published data (approximately 0.70 ARI) and reproduction data (approximately 0.54 ARI).
- ✅ TokenMonitor refresh vulnerability fix (desktop app) — Fixed a 5-hour old window and Codex rate-limiting display vulnerability. Three issues were resolved: adding a periodic timer at the frontend, implementing time-bucket cache keys in the Rust backend for rolling window processing, and fixing the JSON-RPC parser used for staggered notifications.
- 🔄 ErrorRecoveryBenchmark: parallel enhancement and retraining pipeline — 1. Diagnosed a CPU limit issue due to simulated enhancement, switching from serial to parallel execution on Tianhe2 HPC and implementing chunking. 2. Resolved disk quota and EGL rendering errors in LeRobot conversion. 3. Started the first step of the retraining pipeline (LeRobot conversion) using validated enhanced data.
- ✅ Development of Universal Seed-Search clustering pipeline — A Python pipeline for Leiden hyperparameter scanning across 367 embeddings was built. Blocked SLURM array and Manifest generation tools were used for robust clustering evaluation.
Issues and Solutions
Key Issues
1. The initial Cosmos deployment attempt targeted the wrong model architecture; Predict 2.5 is the relevant baseline for RoboCasa, not Cosmos 3.
Solution: In-depth technical analysis was conducted to map the action space and weights. Strategy was adjusted toward Predict 2.5, and users were informed about the difference between the world model and strategy.
Key Insight: The new base model may not be directly compatible with specific benchmarks; checking specific strategy checkpoints is crucial.
2. The “original” STAIG panel incorrectly marked UNI2 features as BYOL, making ablation comparisons invalid.
Solution: Code paths were reviewed to verify feature origins, real BYOL embeddings were found, and evaluation was reverted. It was confirmed that published STAIG scores cannot be reproduced without specific shared optimizations.
Key Insight: Assumptions about baseline features must be verified through code checks; high-level labels may be misleading.
3. TokenMonitor UI data became invalid due to missing frontend timer and static backend cache keys for rolling windows.
Solution: Time-bucket cache keys were implemented in Rust, disk cache for the 5-hour view was disabled, and a refresh logic was added. Codex JSON-RPC parsing was fixed to handle staggered notifications.
Key Insight: Rolling time windows require dynamic cache keys; static keys would freeze time data.
4. ErrorRecoveryBenchmark enhancement failure was due to disk quota limits and EGL rendering errors in headless HPC.
Solution: Quota allocation was separated from physical space; corrupted files were cleaned up, the parallel loop was restarted, and chunking was adopted. LeRobot conversion issues were fixed by exporting MUJOCO_GL=egl and adjusting PYTHONPATH to adapt to mixed environments.
Key Insight: HPC quotas differ from disk usage; headless rendering requires clear backend configuration.
General Issues
5. RoboCasa asset downloader failed in Slurm batch jobs due to interactive stdin prompts.
Solution: Setting scripts were modified to suppress interactive prompts and handle headless downloads automatically.
Key Insight: Simulation environments typically contain old downloaders assuming an interactive terminal; these must be separated from HPC deployment.
Human and AI Approaches
Strategic Level
Model Selection and Architecture Direction
| Role | Approach |
|---|---|
| Human | Users initially requested Cosmos 3 deployment, but received a link to Predict 2.5; accepted AI’s correction to align with the correct baseline. |
| AI | AI conducted gap analysis, identified compatible weights (Predict 2.5), and implemented the pipeline while explaining model differences to users. |
Gap Analysis: AI actively corrected architecture direction based on resource constraints and feasibility.
Effectiveness of Baselines in Scientific Analysis
| Role | Approach |
|---|---|
| Human | Users identified the incorrect STAIG baseline (UNI2 vs BYOL) and discovered logical flaws when excluding the “no encoder” HVG baseline. |
| AI | AI initially relied on labels; after user correction, code paths were verified and ablation structure adjusted to include necessary baselines. |
Gap Analysis: Users provided domain-specific methodological knowledge, correcting AI’s implicit assumptions and file management limitations.
Diagnostic Insight into Outdated Data
| Role | Approach |
|---|---|
| Human | Users provided CLI output showing real-time data, which contradicts the outdated UI state of the application. |
| AI | AI shifted focus from latency to stream parsing, discovering staggered JSON-RPC errors in Codex connectors by checking logs. |
Gap Analysis: Human insights are crucial for revealing internal parser defects by providing external reference data.
AI Limitations
Key Limitations
- Failed to immediately identify the “original STAIG” corresponding to UNI2 features in the code, relying on labels rather than file sources.
General Limitations
- It is difficult to directly access specific original files via network due to API rate limits or cache missing; MCP tools are needed as an alternative.
- An invalid SSH route was attempted by changing key jumpers, and low GPU utilization was misdiagnosed as a CPU-limited workload.
Lessons Learned
Key Experiences
- NVIDIA’s RoboCasa ecosystem clearly separates the Predict 2.5 strategy (baseline) from Cosmos 3 (general world model); specific strategy checkpoints must always be verified to ensure baseline compatibility.
- Real original STAIG results (approximately 0.54 ARI) are significantly lower than published data (approximately 0.70 ARI), indicating no shared optimizations in the original run.
- For rolling time windows, cache keys must include time-bucket components; static keys would retain outdated data.
- Headless HPC environments require explicit export of MUJOCO_GL=egl to prevent GLFW errors in MuJoCo/Robosuite workflows.
Practical Experience — When using Slurm batch jobs on HPC clusters, always check whether the dependency installer/resource downloader has interactive prompts.
Dialogue Summary
Robot Competition Leaderboard and Benchmark Reproduction (pi0.5, GR00T, Predict2.5)
• Multi-model evaluation and benchmark setup 20:40:42.446 | claude_code GR00T-N1.6 leaderboard reproduction (optimized parallel inference) is completed; results are being aggregated. Evaluation of pi0.5 begins (approximately 18–22 hours remaining). Through Slurm pipeline implementation and robust batch processing, NVIDIA Predict 2.5 was confirmed as the correct baseline for RoboCasa, thus bypassing the incompatible Cosmos 3 path.
MIHD Ablation Study
✅ Baseline correction and clustering pipeline 04:46:53.257 | claude_code Key incorrect labels in the STAIG baseline (UNI2 vs BYOL) were corrected, refuting earlier claims that STAIG dominated. A universal seed search clustering pipeline was developed using SLURM arrays for 367 embedding data. It was confirmed that reproduction results differ significantly from published STAIG scores.
TokenMonitor and Error Recovery Benchmark Tests
• Desktop app vulnerability fix and HPC pipeline scheduling 21:47:39.800 | claude_code Key data refresh issues in TokenMonitor (frontend timer, backend cache, JSON-RPC parsing) were resolved. Simultaneously, by diagnosing CPU-intensive enhancement requirements on Tianhe2 HPC, disk quota/rendering issues were resolved, and the LeRobot retraining pipeline conversion was initiated.