Daily Report — 2026-06-23

General Overview

• Completed Tasks: Performed high-difficulty benchmark tests on the VLM reward model and robot simulator; conducted a comprehensive feasibility study on space genomics JEPA, all with negative results; prepared video recognition data pipelines using Cosmos3-Nano; resolved critical hardware instability issues on user desktops.
• Implementation Methods: Achieved unbiased model comparison and literature integration through automated SLURM tasks and multi-agent workflows; diagnosed embedding metrics using statistical probes (anisotropy/PCA); configured cold start training to isolate variable influences; bypassed proxy bottlenecks via direct local download and SSH transfers; performed hardware-level troubleshooting for peripheral software failures.
• Impact: Confirmed that the general VLM lacks zero-shot robot transfer capability and requires fine-tuning; avoided wasting engineering effort on non-transferable world model encoders in MIHD; ensured rigorous experiments by initializing seeds through standardized strategies; established a stable dataset of 90 videos for subsequent recognition tasks.

DCC

• Completed Tasks: Hosted multi-agent workflows for JEPA feasibility and genomic embedding probe research.
• Implementation Methods: Initiated parallel agent literature surveys, executed GeneJEPA embedding extraction, and conducted statistical diagnostics (SVD/correlation analysis) on domain clustering performance.
• Impact: Provided clear negative results for JEPA as a gene encoder, proving that PCA remains superior in small-sample spatial scenarios.

TzJsDesktop

• Completed Tasks: Managed local model downloads, configured cold start Pi0.5 training parameters, and investigated RGB lighting hardware.
• Implementation Methods: Parsed HPC metadata, adjusted training drivers to ensure fair initialization, executed bash/Python scripts, and performed WMI queries/hardware disconnection analysis on OMEN PC.
• Impact: Ensured stability of the policy baseline by aligning initialization seeds; bypassed network proxy restrictions to protect model weights; permanently disabled unstable OEM RGB software through hardware modifications.

athena.egr.duke.edu

• Completed Tasks: Served as the main computing node for RoboCasa evaluation, pi0.5 release, and embedding inference.
• Implementation Methods: Resolved dependency conflicts (mujoco/numpy), executed JAX strategy server with GPU rendering, generated labeled video lists, and hosted model transfer background processes.
• Impact: Established a complete end-to-end evaluation infrastructure, with 90 high-fidelity released videos available for Cosmos3-Nano recognition; verified stability of the simulation pipeline.

A fair baseline for robot success detection and strategy initialization was established; this path was completely closed due to negative JEPA feasibility results in MIHD spaceomics; infrastructure for video-based recognition analysis was built; desktop hardware stability issues were resolved.

Tasks

Architecture and Strategy

• ✅ Roboceptor Success Detection Benchmark — Conducted comprehensive comparative benchmarks on Cosmos-Reason1-7B and RoboReward-8B in RoboCasa and LIBERO, proving that native world models lack zero-shot transfer capability. Reconfigured Pi0.5 error recovery mechanism for fair baseline comparison from basic weight cold start.
• ✅ MIHD JEPA Feasibility Analysis — Conducted extensive literature and code repository surveys on JEPA applications in space genomics; performed GeneJEPA probes on DLPFC data. Negative conclusions: embeddings have anisotropy, the encoding library size rather than structure, and performance is lower than PCA.
• ✅ RoboCasa Evaluation Infrastructure and List Generation — Built the RoboCasa+OpenPI environment on Tianhe2/athena, resolved dependency/proxy issues, generated a list of 90 labeled pi0.5 released videos for subsequent Cosmos3-Nano recognition analysis.

Implementation and Fixes

• 🔄 Automated Rationality Check Pipeline — Developed background scripts to monitor the first checkpoint (step 1000), run limited evaluations during Pi0.5 cold start training to verify pipeline health.
• ✅ Desktop Maintenance (OMEN 45L) — Resolved persistent crashes caused by OMEN Gaming Hub software by providing a hardware disconnection method for chassis RGB lighting system.

Issues and Solutions

Key Issues

1. Despite complex architecture, GeneJEPA embeddings cannot cluster space domains due to high anisotropy and library size bias.

Solution: Conduct SVD and correlation diagnostics; confirmed post-processing (whitening) cannot restore signals. Verified that the base model on single-cell data cannot be transferred to spatial point-level clustering.

Key Insight: Complex base models may perform worse than simple linear projection (PCA) in specific small-sample domains due to domain shift and lack of spatial prior.

2. Infrastructure Bottleneck: Damaged local proxy causes HuggingFace CLI download stalls; dependency conflicts disrupt RoboCasa environment on high-performance clusters.

Solution: Bypass proxies via direct local HTTP download and SSH/SCP transfers; fix specific numpy/mujoco versions and patch configurations to ensure stability.

Key Insight: Large binary transfers are more reliable through direct protocols than tunnels; open-source robot environments usually require explicit version fixes and manual patches.

3. VLM prompt design bias and inconsistent model initialization in strategy tasks compromise evaluation fairness.

Solution: Redesign Cosmos tests using standardized criteria for fair scoring; reconfigure Pi0.5 training from basic weight cold start to ensure differences are attributed to data quality rather than initialization issues.

Key Insight: Rigorous experimental design requires controlling prompt variance in VLM and identical initialization seeds in strategy learning to isolate causal factors.

4. OMEN Gaming Hub software causes system crashes when controlling HP OMEN 45L RGB lighting. The initial AI assumption that the solution depends solely on software ignores user experience constraints.

Solution: Shift to hardware-level troubleshooting; provide a step-by-step guide to physically disconnect the lighting control board power line and offer a stable resource-free alternative.

Key Insight: When OEM software is unstable, verifying the hardware architecture allows reliable manual intervention; user constraints should prioritize avoiding known failure points.

Human and AI Approaches

Strategic Level

Strategic Focus and Experimental Effectiveness| Role | Method |

|——|——| | Human | Continuously guide attention toward strict scientific validity: require standardized criteria for VLM evaluation, cold start initialization for policy comparison, and identify that potential JEPA predictions are covered by STAIG integration. | | AI | Initially proposed broader literature surveys or standard software fixes; only adapted to specific experiments driven by falsification after users provide precise strategy constraints and correct methodological shortcuts. |

Difference Analysis: Humans provide important strategic boundaries and domain intuition (e.g., rejecting ineffective software tools, prioritizing ARI over general functions), forcing AI to abandon inefficient paths and perform specific, high-value diagnostic tests.

AI Limitations

• AI initially overestimated zero-shot transfer capability of the general world model in robot tasks, proposing redundant or irrelevant JEPA applications before human correction.
• AI faces difficulties using reliable remote tools (HTTP download timeouts, proxy tunnel issues) and initial web search restrictions (HTTP 403), requiring manual intervention or tool switching to verify external resources.

Learning Outcomes

Key Learning Outcomes

• Without specialized training, general reasoning VLM cannot reliably detect task success/failure in robot simulators; prediction architectures similar to JEPA cannot provide new advantages for space omics clustering, as their principles are already embedded in graph comparison methods like STAIG.

Practical Learning Outcomes

• Fair policy comparison requires identical initialization; base models pre-trained on cell data do not automatically transfer to spatial transcriptomics due to domain changes, typically performing worse than PCA.

Conversation Summary

RoboReward and Pi0.5 Evaluation

✅ Robot Benchmark and Policy Initialization 17:18:51.887 | claude_code Comprehensive benchmarks show that Cosmos-Reason1-7B lacks zero-shot transfer capability in RoboCasa/LIBERO compared to RoboReward-8B, due to prompt fairness adjustments. Pi0.5 error recovery strategy was reconfigured for cold start from basic weights to ensure fair comparison with normal baselines. Infrastructure for video-based discrimination using Cosmos3-Nano has been established on HPC clusters.

MIHD Space Genomics

✅ JEPA Feasibility and Embedding Analysis 19:44:49.858 | claude_code Studies on using JEPA for MIHD embeddings yielded negative conclusions. GeneJEPA detection shows high anisotropy and library size bias, making it unable to cluster domains better than PCA. Its core principle is considered unrelated to existing STAIG integration methods.

Desktop Hardware Support

✅ HP OMEN 45L RGB Lighting Fix 17:52:20.150 | codex Resolved continuous crashes in OMEN Gaming Hub software by providing a hardware disconnection method for chassis RGB lighting, as the software control is unstable.

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