Weekly Report — 2026-W41 (2026-10-05 ~ 2026-10-11)

This week was characterized by a major transition from fragmented engineering tasks to a unified research and architectural front. Key achievements include the implementation of the RoboMemory JEV ‘plan-once’ architecture, the modularization of the Personal Agent system into a scalable plugin-based framework, and the successful refinement of the MIHD research narrative using the ‘Press-Release Principle’. Significant progress was made in robotics through large-scale VLM evaluations on the Tianhe3 cluster, quantization error attribution for the $\pi$0.5 model, and establishing a rigorous cross-platform validation protocol (X1=C) to mitigate floating-point divergence.

Weekly Overview

Metric	Value
Date Range	2026-10-05 ~ 2026-10-11
Active Days	5 / 7
Total Conversations	49
Projects	38
Tasks Completed	48
Tasks In Progress	17
Total Tokens	8,473,230,600
Total Cost	$2,121.96
Claude Code Token	8,429,999,488
Claude Code Cost	$2,028.89
Codex Token	43,231,112
Codex Cost	$93.07
Daily Average Cost	$424.39

Project Progress

RoboMemory JEV Research (6 days active) — 🔄 active

Accomplishments:

• Implemented ‘plan-once’ + non-pausing async writer architecture
• Executed large-scale 16-task evaluations using GPT-6 Astra (achieving 77.5% success)
• Developed ‘Arm C’ (Plan-once + JEV observer) testing protocols
• Completed JEV-27B model migration and probe development on Tianhe3

Blockers:

• ⚠️ Latency-induced protocol breakdown in non-paused modes

Personal Agent (LifeCopilot) Modularization (5 days active) — 🔄 active

Accomplishments:

• Refactored project into a central orchestration core with independent capability repositories
• Implemented core features including WeChat bridge, ASR, and summaries
• Resolved critical stability issues in instance locking and UI automation

Blockers:

• ⚠️ WeChat automation limitations and VM-to-host resource access constraints

Quantization & Model Analysis ($\pi$0.5) (4 days active) — 🔄 active

Accomplishments:

• Conducted component-wise ablation for W4A4/W4A8 accuracy collapse
• Identified a critical bug in the action expert’s lazy-initialization quantization path
• Performed operator-wise error attribution against FP16 baselines

Blockers:

• ⚠️ High difficulty in distinguishing rounding vs. clipping errors

Academic & PhD Strategy (3 days active) — ✅ completed

Accomplishments:

• Refined CV with quantitative research metrics
• Created a ‘Material Bank’ for multi-length project narratives
• Developed a high-impact outreach strategy linking research gaps to specific advisors

RecoverBench/RoboHarness (3 days active) — 🔄 active

Accomplishments:

• Validated version 5.1.2 package across Linux and Windows
• Completed 58GB data synchronization from Tianhe3 to local storage
• Implemented X1=C acceptance protocol to handle cross-platform drift

Key Tasks

• ✅ RoboMemory JEV ‘Plan-Once’ Implementation — Developed a decoupled architecture using monkey-patching to inject functionality into frozen modules without violating byte-level integrity.
• 🔄 Main Experiment Execution (Astra/JEV) — Executing 16 tasks $\times$ 10 episodes to validate the new constraint-based memory framework.
• ✅ Quantization Bug Resolution ($\pi$0.5) — Identified and documented a critical flaw where the action expert defaulted to float precision due to lazy-initialization.
• ✅ Modular Consolidation (PersonalAgent) — Transformed the agent into an orchestration core with independently runnable capability repositories.
• ✅ Paper Narrative Refinement — Applied the ‘Press-Release Principle’ to reframe research claims from ‘parity’ to ‘memory precision via constraints’.
• ✅ PhD Application Assets — Created strategic outreach materials and a research-aligned ‘Material Bank’.
• ✅ W4A8 LIBERO-PRO Evaluation — Ran 8,000 episodes to verify results against float baseline using adversarial verification.

Problems & Solutions

1. Strict W4A4 quantization leads to near-zero success rates on LIBERO tasks. [Quantization & Model Analysis]

Solution: Identified attention weight outliers and hypothesized necessity for a hybrid floating-point ‘island’ approach.

2. Mac (ARM) recordings drift on Linux (x86) due to floating-point sensitivity in contact-rich scenes. [RecoverBench/RoboHarness]

Solution: Implemented the ‘X1=C’ state-synchronization standard where validity is judged by physical consistency rather than exact replay.

3. Historical Google credentials found in Git history. [Personal Agent]

Solution: Updated .gitignore and advised immediate revocation and re-issuance of all identified credentials.

4. $\pi$0.5 action expert bypassed quantization due to lazy-initialization. [Quantization & Model Analysis]

Solution: Changed activation check to trigger quantization based on the existence of the ONNX bundle.

5. VM unable to access local vision models or shared GPU registries. [Personal Agent]

Solution: Modified model_access to support multi-host tracking and utilized directory junctions for sharing JSON registries.

Learnings

Architecture (architecture)

• Decoupling requires strict policies on resource (GPU/Model) discovery across process boundaries, not just code changes; tracking ‘host’ name is essential as PIDs are not unique across network shares.
• A ‘Plan-once + JEV’ architecture’s strength is defined by its information availability constraints, establishing a unique research scope.

Domain Knowledge (domain)

• Success rate alone is an insufficient metric for comparing harness methods; ‘Success Rate / Theoretical Ceiling’ is a more robust measure of memory precision.
• Effective research statements must transition from ‘what I did’ to ‘what I realized (the gap)’ to ‘what I will solve’.

Debugging (debugging)

• Lazy initialization of quantization simulators can silently bypass quantization paths if the specific property is never triggered during evaluation.

AI Usage Notes

Effective Patterns:

• ✓ Multi-agent adversarial workflows for statistical/claim verification
• ✓ Using the ‘Press-Release Principle’ as a meta-rule for narrative auditing
• ✓ Systematic decomposition of complex technical issues into ablation components

Limitations:

• ✗ Inability to bypass project-level guardrails for external data/repo access
• ✗ Difficulty in verifying social/UI behaviors (like scrolling) without explicit prompting
• ✗ Safety classifiers blocking legitimate production deployment commands

Next Week Outlook

Priorities will shift toward completing the current large-scale batch of comparison experiments (E8(b), E6, B0) on the Tianhe3 cluster. Key focus areas include fine-tuning the JEV-27B probe results, finalizing the W4A8 quantization attribution report, and advancing the Personal Agent ‘V1 Rewrite’ by integrating the WeChat/Summaries/ASR features into the new modular architecture. Additionally, PhD outreach will begin following the finalized material bank.

Token Usage Statistics

Peak Day: 2026-10-06 — $1178.12 / 4535.9M tokens

Daily Average: $424.39