Daily Report — 2026-07-08
Daily Overview
- What was done: Extracted and transferred 10 representative failure demos from the remote cluster to the local environment for detailed analysis.
- How it was done: Used SSH tunneling, pattern-matching file discovery, and tar streaming to securely copy MP4 failures from the HPC node to the local machine.
- Impact: Validated the ~96% success rate claim and identified that failures primarily occur in multi-object manipulation tasks due to step-budget constraints on subtask recovery.
Collected and analyzed 10 failure demonstration videos from the Action-Sketcher model to validate success rate claims and characterize failure modes.
Tasks
Architecture & Strategy
- ✅ Collect and analyze failure demonstrations for Action-Sketcher — Extracted 10 unassisted failure episodes from the evaluation run and transferred them locally for pattern analysis.
Problems & Solutions
Critical Issues
1. Need to efficiently locate and extract rare failure videos hidden among many task directories on a remote cluster.
Solution: Constructed a targeted SSH bash pipeline with regex matching and piped tar streams for direct local transfer without cluttering the remote filesystem.
Key Insight: Failures are genuinely sparse; automated pattern-matching is essential to surface them from overwhelming success logs.
Human vs AI Approaches
Strategic Level
Failure pattern analysis and demo collection strategy
| Role | Approach |
|---|---|
| Human | Focused on validating the paper’s ~96% success claim by seeking concrete failure evidence rather than relying solely on aggregated metrics. |
| AI | Executed SSH/file-transfer pipeline, auto-categorized failures by task/instruction, correlated findings with theoretical failure modes (long-horizon/dual-object fumbling), and offered follow-up data synthesis. |
Difference Analysis: Human provided the strategic research validation goal; AI handled logistical execution, data categorization, and contextual alignment with academic literature without explicit step-by-step prompting.
AI Limitations
General Limitations
- Automated file discovery and regex matching can occasionally miss or falsely match files if directory structures evolve; relies on accurate prior knowledge of the cluster’s naming conventions.
Learnings
Key Learnings
- Rare but critical failure cases are often hidden in deep directory trees; direct log/video extraction paired with strategic pattern matching is necessary to capture true agent limitations for robust evaluation.
Conversation Summaries
✅ Collect and analyze failure demos for Action-Sketcher 04:07:08.501 | claude_code The session focused on retrieving 10 unassisted failure demonstration videos from the remote HPC cluster to the local machine. The AI constructed and executed a secure SSH pipeline with regex-based file discovery to isolate rare failure cases. Upon retrieval, it automatically categorized the demos by task, correlated them with theoretical failure modes (primarily second-subtask misgrasps in dual-object scenarios), and validated the model’s reported ~96% success rate.