Daily Report — 2026-10-01

Daily Overview

• What was done: The work spanned high-level strategic academic planning, rigorous robotic policy evaluation and VLA model optimization, implementation finalization of the RecoverBench framework, and the deployment of the AstrBot-based WeAgent system.
• How it was done: Technical advancements were achieved through deterministic XLA tuning, visual-only memory constraints, and VLA quantization research; meanwhile, software reliability was improved through DB maintenance, plugin migration, and automated VM deployment scripts.
• Impact: These efforts established a stable, reproducible benchmark (RecoverBench), a verified baseline for visual-only robotic control, and a robust, production-ready agent architecture (WeAgent).

The day was characterized by the successful completion of RecoverBench Phase 2 implementation, the strategic restructuring of academic research identities, the optimization of VLA model quantization for hardware exportability, and the migration of WeAgent to the AstrBot architecture.

Tasks

Architecture & Strategy

• 🔄 Academic CV Restructuring — Redesigning the CV to prioritize research impact and a ‘researcher identity’ (problem-solving/benchmark building) over publication quantity.
• ✅ Visual-only Policy Evaluation — Re-running Gemini-based policy evaluation with strict visual-only constraints (no proprioception) to establish a true baseline.
• 🔄 VLA Quantization Optimization — Recovering closed-loop success rates for π0.5 W4A4 models while ensuring QNN exportability via precision management.
• ✅ WeAgent/AstrBot Migration & Deployment — Migrated core logic to AstrBot plugin architecture, fixed SQLite WAL corruption, and created automated VM deployment scripts.
• 🔄 Prepare Gate-B approval request — Finalizing cost estimates and code fixes for the MihD-dev execution plan.
• • Policy Decisions (H1-H7) — High-level decisions required for error definitions, injection validity, retraining scope, GR00T, and historical regeneration.
• • SpaceMouse Demo (H3) — Record one real continuous demo using operator_kit_v2 to qualify the demonstration set.

Implementation & Fixes

• ✅ Cross-device Sync Setup & Research — Evaluated tools (Tailscale, Telegram, OneDrive) for seamless file/text transfer between Windows, Mac, and VMs, concluding Telegram is best for UX.
• ❌ Reverse 1999 Task Automation — Automating daily tasks in the game Reverse: 1999 using computer-use skills.
• • RecoverBench Implementation Phase 2 — Integrated all functional code logic, deterministic numerics (A9, B5), and end-to-end pipelines; verified via final regression sweep (574/575 passed).

Problems & Solutions

Critical Issues

1. Inflated robot policy evaluation success rates due to hidden proprioceptive data usage.

Solution: Implemented DRAWN_MEMORY_VISUAL_ONLY/TRACK flags to force zero proprioception, correcting the success rate to 65.0%.

2. VLA quantization causing loss of success rate during QNN/HTP export preparation.

Solution: Exploring TF-Enhanced on RMSNorm outputs and integer preservation for critical tensors.

3. Database corruption in wechatauto due to incomplete SQLite WAL transactions.

Solution: Fixed the WAL merge logic to handle transactions correctly.

General Issues

4. Infrastructure and environment issues: GPU OOM, Pip ‘Access Denied’, and LocalSend discovery failures.

Solution: Developed bash/PS1 scripts to restart servers on alternative GPUs, used –no-cache-dir for pip, and recommended Tailscale for P2P connectivity.

5. Inconsistent demo results across tools (auditor/augmenter) and game UI automation focus loss.

Solution: Unified tools under a single shared replay routine; advised manual window management for game automation.

6. N-15 pilot failure due to glue code bugs.

Solution: Fixed protocol fingerprint recording and pandas 3 compatibility.

7. Non-determinism in Pi0.5 numerics and scene generation due to XLA autotuning and worker scheduling.

Solution: Implemented forced deterministic XLA autotuning, unique JAX cache directories, and stabilized worker RNG seeds.

Human vs AI Approaches

Strategic Level

Strategic Design & Philosophy

Role	Approach
Human	Provided high-level direction (e.g., CV identity as a ‘researcher’ rather than a job-seeker; desire for a ‘WeChat-like’ sync UX).
AI	Focused on tactical implementation (LaTeX formatting, tool categorization, and technical detailing).

Difference Analysis: N/A

Policy & Scientific Decisions

Role	Approach
Human	N/A
AI	Consolidated disparate open questions into a structured decision table to minimize cognitive load.

Difference Analysis: N/A

Technical Autonomy

Role	Approach
Human	N/A
AI	Integrated tools (read_file, web_search) into the agent loop to solve document-based tasks autonomously.

Difference Analysis: The human defines the scientific/policy boundaries (the ‘what’), while the AI manages the execution (the ‘how’).

AI Limitations

Critical Limitations

• Failed to recognize non-visual data leakage in policy evaluations until human-prompted; struggled with maintaining focus in complex UI automation.
• Cannot make high-level policy decisions (e.g., defining ‘collision’) or perform physical human-in-the-loop tasks without user intervention.

General Limitations

• Initially made over-generalized claims regarding platform-specific storage/limitations (Telegram/WeChat) before verification.
• Technical guardrail issues (writing outside project boundaries) and experimental OCR limitations for complex WeChat UI elements.

Learnings

Key Learnings

• XLA autotuning and proprioceptive data leakage are critical hidden sources of non-determinism and inflated performance in robotic benchmarks.
• Successful VLA hardware export (QNN) requires careful management of activation-only recalibration and precision tensors.
• For research-focused applications (CVs/PhD), narrative should prioritize problem-definition ability and research intuition over raw metrics.
• SQLite WAL merging requires precise handling of the last committed frame to prevent main DB corruption during simultaneous writes.
• Automated VM deployment is significantly improved by real-time shell command injection and explicit cache management.

Conversation Summaries

RecoverBench Implementation

• Phase 2 Completion All subagents (A1-A9, B1-B5, C1-C8, H7p) have integrated improvements, verifying the entire codebase is ready for policy audit.

PhD Application Strategy

🔄 CV Narrative Restructuring Shifted CV strategy from a job-style resume to a research-centric narrative focusing on robotic reliability and problem definition.

RoboMemory

✅ Visual-only Policy Evaluation Executed critical re-runs to ensure visual-only memory constraints, correcting the success rate from 79.4% to 65.0%.

WeAgent/AstrBot

• Migration and Debugging Completed the migration to AstrBot, optimized social/reply logic, and consolidated deployment workflows.

MihD-dev

• Decision Consolidation Produced a master decision table to reconcile open scientific/procedural questions with the existing manuscript.

Pi0.5 VLA Optimization

🔄 W4A4 Quantization Recovery Investigated methods to maintain success rates for π0.5 models during QNN-targeted quantization.

Cross-device Sync Tooling

✅ Multi-device Transfer Evaluated synchronization methods, concluding Telegram is optimal for a ‘chat-like’ UX.

Game Automation

❌ Reverse 1999 Tasks Attempted UI automation for daily tasks but encountered input focus issues.

Token Usage