Daily Report — 2026-10-02
Daily Overview
- What was done: The team advanced multiple high-stakes projects: implemented visual gripper/height tracking (I-172) in RoboMemory, fixed critical database integrity and file-processing bugs in WeAgent, formalized 15 strategic decisions and launched parallel subagents for MihD-dev, and automated deep learning homework (RNNs/Transformers) for ECE 661.
- How it was done: Technical approaches included developing wrist-camera event detectors with re-anchoring mechanisms, implementing a ‘SQLite Replay’ strategy for robust WAL merging, utilizing specialized agentic protocols for scientific bookkeeping, and employing AI agents to execute and verify Jupyter notebooks.
- Impact: These efforts resulted in significantly improved robot task success rates, ensured stable and reliable WeChat bot operations, transformed high-level research guidelines into actionable technical specifications, and delivered verified academic reports.
The day involved successful implementation of visual-only proprioception for RoboMemory, resolving critical SQLite corruption and file-reading issues in WeAgent, formalizing project specifications and orchestrating subagents for MihD-dev, and automating the completion of deep learning academic assignments for ECE 661.
Tasks
Architecture & Strategy
- ✅ Fix SQLite WAL Merge Integrity — Implemented a robust ‘SQLite Replay’ approach for decrypted WAL files to ensure integrity and handle complex checkpointing/frame validation rules, replacing error-prone manual merging.
- ✅ MihD-dev Decision Register Update — Record 15 owner decisions, updating the decision table, specification, experiment matrix, and implementation checklist.
- ✅ Improve PDF/Image Reading — Switched PDF processing to PyMuPDF to bypass onnxruntime/DLL issues and implemented long screenshot tiling to prevent resolution loss.
- ✅ ECE 661 HW3 Completion — Automated the implementation of LSTM and GPT-2/LoRA notebooks, including training and generating a comprehensive PDF report.
- 🔄 MihD-dev Round 3 Agent Launch — Managing 13 background agents covering NCL design, cross-patient queries, and bug fixes.
- ✅ Enhance WeChat Message Recognition — Fixed issues where files were identified as ‘Other Messages’ by improving filename lookup and adding WeChat Quote/At-me support.
- • I-172: wrist-camera event detectors — Replacing arm proprioception (gripper/height) with wrist camera visual detection to enable pure visual memory modes.
Implementation & Fixes
- ✅ MIHD Paper Figure Audit — Audited existing figures against the 6-figure manuscript plan and packaged them for the user.
- ✅ Instruction Injection Audit — Audited homework files for hidden model instructions or LLM restrictions to ensure no unintended prompt injections exist.
Problems & Solutions
Critical Issues
1. SQLite WAL merge causing ‘quick_check’ failures and database corruption due to half-written frames or missed atomicity rules.
Solution: Replaced manual merging logic with a ‘SQLite Replay’ approach, using the official engine to play back decrypted WAL files for maximum robustness.
2. Long screenshots were being crushed by AstrBot’s 1280px resize limit, making text unreadable.
Solution: Implemented a tiling system that splits long images into multiple overlapping pieces (max 8) to preserve resolution.
3. AI subagent attempted to cancel critical system-wide SLURM allocation (agent-zt81).
Solution: Clarified permission protocols and explicitly instructed agents via the main agent to never cancel auto-provisioned sessions.
4. ImportError: DLL load failed when reading PDFs via MarkItDown due to missing VC++ runtimes.
Solution: Bypassed MarkItDown for PDFs in favor of PyMuPDF (fitz) and added VC++ runtime installation to the VM setup script.
General Issues
5. Wrong GPU node name in the Round 3 README caused SLURM submission failures.
Solution: Identified correct node (dcc-gehmlab-gpu-ferc-s-z25-18) and issued a global correction to all active agents.
6. The implementation checklist was corrupted due to a greedy regex in the update script.
Solution: Restored the file from a file-system snapshot and reapplied the update with a corrected regex.
7. Wrist camera odometry drift during long executions (e.g., VideoUnmaskSwap).
Solution: Implemented a re-anchoring mechanism that detects significant scale/pose jumps and resets the keyframe tracking.
8. Notebook validation failed due to unsupported ‘id’ properties in markdown cells when exporting/finalizing.
Solution: Implemented a patch to strip ‘id’ fields from cells if the notebook format minor version is below 5.
Human vs AI Approaches
Strategic Level
MihD-dev Scope and Scientific Methodology
| Role | Approach |
|---|---|
| Human | The human defined strict scientific boundaries: no formal equivalence margins, exclusion of UCE from benchmarks, and requiring provenance for all similarity queries. |
| AI | The AI initially managed these as general tasks/features but required structural guidance to translate these specific constraints into the decision register and experiment matrix. |
Difference Analysis: The human provides the scientific ‘guardrails’ and intent; the AI manages the complex administrative and technical orchestration to enforce them.
WeAgent Bug Root Cause Analysis
| Role | Approach |
|---|---|
| Human | The human provided a detailed breakdown of three distinct failure modes in the manual WAL implementation (snapshot mismatch, zero-frame application, and checksum failure). |
| AI | The AI initially attempted to fix the existing logic but ultimately pivoted to the human’s superior architectural suggestion of using SQLite replay. |
Difference Analysis: The human identified the specific logical gaps in the custom implementation that the AI failed to recognize initially.
Implementation Level
Debug Direction for File Reading
| Role | Approach |
|---|---|
| Human | Suggested checking the actual WeChat folder location via UI to verify path accuracy. |
| AI | Analyzed logs to prove the path was already correct and identified the issue as a database field returning ‘None’. |
Difference Analysis: Human focused on physical path verification; AI focused on the logic/data flow between the DB and the file system.
Success Rate Comparison
| Role | Approach |
|---|---|
| Human | N/A |
| AI | Developed visual-proprioception modules and conducted 50-episode closed-loop evaluation. |
Difference Analysis: Achieved a significant boost over the original pure-visual mode by using stable visual cues from the camera.
AI Limitations
Critical Limitations
- Subagent error: Incorrectly attempting to cancel high-level auto-provisioned system jobs.
- The AI struggled to identify the specific edge cases in the manual SQLite WAL merging logic until the human provided a structural analysis of the bugs.
General Limitations
- Incorrectly specified hardware node names in documentation leading to wayfinding/execution failures.
- Could not access the user’s local desktop directly to move files; provided a zip and SCP instructions instead.
Learnings
Key Learnings
- When manually implementing SQLite WAL parsing, every frame must be verified for integrity to avoid corruption during concurrent writes.
- When dealing with complex database internals like WAL, it is safer to decrypt the file and let the official engine handle replay rather than re-implementing the merging logic.
- A shared NCL model applied across tiles (XS-joint) is computationally feasible and potentially superior to per-tile independent models.
Practical Learnings
- Wrist camera finger-pad horizontal position provides a reliable, linear mapping to gripper state, enabling accurate grasp/release event detection without joint encoders.
Conversation Summaries
RoboMemory
• I-172 Implementation and Verification The user and AI worked to replace robot proprioception with wrist-camera visual data. This involved writing a detector, verifying it with offline probes, and running full closed-loop evaluations on tianhe3 to confirm performance gains.
WeAgent
• WeChat Bot Technical Fixes & Database Integrity Extensive debugging session resolving database corruption (WAL merge), PDF reading errors (DLL issues), and image clarity (long screenshot tiling). Implemented a robust solution by replaying decrypted WAL files through the native SQLite engine to ensure data integrity.
MIHD
• Paper Writing and Figure Audit Analyzed the manuscript draft and figure plan. Compared existing figures with the new 6-figure plan, provided a structured writing strategy, and packaged all current figures into a zip file.
MihD-dev
• Decision Registration and Round 3 Orchestration Processed 15 major owner decisions regarding scientific scope, scaling, and benchmarks. Updated all project documentation including the decision table and experiment matrix. Launched 13 parallel subagents to carry out specialized technical tasks.
ECE 661
• Automated Completion of RNN & Transformer HW3 The AI agent autonomously read the assignment PDF, set up a local environment, fixed template bugs, trained LSTM and GPT-2 models (full fine-tuning and LoRA), and produced a comprehensive 11-page PDF report. A security audit also confirmed no prompt injections were present.