Weekly Report — 2026-W11 (2026-03-09 ~ 2026-03-15)

本周重点在于从基于物理的架构向 quota-driven 的 Error Recovery Benchmark v5 进行重大架构转型，同时进行了广泛的基础设施稳定性建设。关键成果包括迁移 MIHD spatial omics pipeline，解决 HPC clusters 上关键的 VLA training convergence 和 ABI mismatch 问题，以及开发统一的 Research Scout/Profiler CLI tool。通过 cascading LLM recovery chains 和 container-aware telemetry 增强了系统可靠性，取得了显著进展，有效打通了多领域的 robotic 和学术研究工作流。

Weekly Overview

Metric	Value
Date Range	2026-03-09 ~ 2026-03-15
Active Days	6 / 7
Total Conversations	17
Projects	17
Tasks Completed	28
Tasks In Progress	3
Total Tokens	391,627,507
Total Cost	$239.07
Daily Average Cost	$34.15

Project Progress

Error Recovery Benchmark (v4 to v5 Transition) (5 days active) — 🔄 active

Accomplishments:

执行了约 50 个 legacy v4 modules 的战略性归档
设计了 v5 架构，其特点是包含 11 个 semantic error skills 和 quota-driven scheduling
实现了 context-replay engines 和 offline trajectory scanning

Blockers:

⚠️ 从 physics-force injection 向 semantic skill paradigm 的转型需要对新的 data collection loops 进行全面验证

VLA / RoboTwin Training Pipeline (5 days active) — 🔄 active

Accomplishments:

通过 gradient scaling 和 task_pos_weight 调整解决了 VLA training loss oscillation 问题
为 LeRobot-to-PI05 converter 修复了缺失 progress metrics 的问题
在 Flax NNX 中设计了四个实验性的 progressive conditioning architectures
修复了 PyTorch/torchvision ABI mismatches 和 Curobo JIT compilation failures

Blockers:

⚠️ 由于 API connectivity issues，Lerobot 的 data format updates 尚未完成

Research Scout & Profiler Unification (2 days active) — ✅ completed

Accomplishments:

将 research pipelines 整合进一个带有 citation graph integration 的单一 CLI 中
实现了多后端 LLM routing (Haiku/Sonnet/Opus)，用于稳健的学术分析
将 bibliometric popularity bias 与核心 relevance scoring 解耦

MIHD Spatial Omics (2 days active) — 🔄 active

Accomplishments:

将 repository 重构为标准化的 DLPFC/HD/RM-IDEAL 层级结构
实现了 path migration 和 cross-section topology alignment 的自动化

Blockers:

⚠️ 由于 coordinate system mismatches，发现了 cross-sample patch retrieval failures

Infrastructure & Tooling (GPU Monitor / MCP) (4 days active) — ✅ completed

Accomplishments:

使用 /proc fd inspection 开发了 container-aware Kubernetes GPU telemetry utility
为 Gadget framework 构建了具有严格 JSON-RPC isolation 的独立 MCP server
解决了 macOS TCC/sandbox permission 对 media playback 的影响问题

Key Tasks

✅ Error Recovery Benchmark v5 Architecture & Quota Scheduler — 设计了 11 个 self-contained semantic error skills，并构建了 quota-driven distribution schedulers 以取代 legacy force injectors，通过了 200+ unit tests 验证。
✅ VLA/RoboTwin Training Pipeline Remediation — 解决了复杂的 ABI crashes、pi0.py 中的 tensor shape broadcasting mismatches 以及 Curobo JIT mapping issues，从而稳定了 training throughput。
✅ Research Scout + Profiler CLI Unification — 部署了一个用于 researcher profiling 的模块化 pipeline，具备 dual-API ingestion、BFS discovery logic 和 cascading LLM fallback chains。
✅ pi05 Progressive Conditioning Experiment Architecture — 设计了四个实验性 pipelines，通过 config-driven flags 将 feature extraction (last-token vs special-token) 与 time/distance prediction 相结合。
✅ MIHD Spatial Omics Restructuring — 整合了碎片化的 output directories，并将所有 downstream code paths/documentation 更新为标准化的 hierarchical layout。
✅ Gadget Framework MCP Server Integration — 构建了一个独立的 MCP server，通过严格的 stdout isolation 对 CLI tools 进行封装，以实现安全的 AI agent 交互。

Problems & Solutions

1. VLA training loss exhibited severe oscillation due to fixed-size tensor padding diluting gradients. [RoboTwin/PI05]

Solution: 保持较低的 task_loss_weight (0.1) 并缩放 task_pos_weight (~5.0)，同时在 non-padded dimensions 上实现 masking。

2. Standard GPU telemetry tools failed in Kubernetes due to PID namespace isolation. [Infrastructure]

Solution: 设计了一个监控 utility，通过直接使用 /proc//fd symlink inspection 将 containers 映射到 physical devices。

3. Probabilistic LLM outputs produced malformed JSON, crashing analytical pipelines. [Research Scout]

Solution: 实现了一个带有 offline atomic logging 的 cascading LLM fallback chain (Haiku → Sonnet → Opus)。

4. Curobo JIT compilation failed due to missing CUDA headers in isolated conda/pip environments. [RoboTwin]

Solution: 在 pip-installed nvidia packages 中定位 headers，并显式配置了 CPATH 和 CUDA_HOME。

5. macOS media playback failed due to TCC/Sandbox permission expiration. [Desktop Wallpaper App]

Solution: 启用了 app sandboxing，并实现了一种带有 NSOpenPanel re-authorization triggers 的混合方法。

Learnings

Architecture (architecture)

Analytical pipeline resilience 需要显式的 multi-model fallback chains 和防御性 serialization defaults，以处理 probabilistic outputs 和 legacy data。
Cross-modal retrieval (例如 spatial transcriptomics) 需要 joint alignment 或统一的 baseline generation，以防止由于独立的 preprocessing 导致不可比的 latent spaces。

Debugging (debugging)

Virtualized/HPC environments 需要直接进行 /proc filesystem inspection 以获得准确的 hardware telemetry，并且需要显式的 TOML-level dependency overrides 来保证 C-extension 的稳定性。

Domain Knowledge (domain)

在 robotics VLA training 中，auxiliary loss weighting 需要相对于 primary heads 进行显式的 gradient scaling，以防止 action learning degradation。

AI Usage Notes

Effective Patterns:

✓ 对于 high-uncertainty tasks 使用 cascading model fallbacks
✓ 使用 AI 进行自动化的 AST-aware code refactoring 和 import-trace audits

Limitations:

✗ 在 containerized environments 中，最初倾向于假设标准的 system paths (CUDA) 或标准的 local-server contexts
✗ 在没有显式人类指导的情况下，难以处理 rendering tasks 中不断变化的 debugging parameters

Next Week Outlook

优先完成 Lerobot data format update（解决 API connectivity issues），以支持 progressive conditioning training。通过完成新 semantic skill trajectories 的全面 rollout，完成 v5 Error Recovery Benchmark 的验证。通过实现 joint alignment 或 dimension expansion strategy 来解决 MIHD cross-section retrieval failures。

Token Usage Statistics

**Peak Day:** 2026-03-10 — $92.78 / 137.6M tokens

Daily Average: $34.15

Weekly Report — 2026-W11 (2026-03-09 ~ 2026-03-15)#

Weekly Overview#

Project Progress#

Error Recovery Benchmark (v4 to v5 Transition) (5 days active) — 🔄 active#

VLA / RoboTwin Training Pipeline (5 days active) — 🔄 active#

Research Scout & Profiler Unification (2 days active) — ✅ completed#

MIHD Spatial Omics (2 days active) — 🔄 active#

Infrastructure & Tooling (GPU Monitor / MCP) (4 days active) — ✅ completed#

Key Tasks#

Problems & Solutions#

1. VLA training loss exhibited severe oscillation due to fixed-size tensor padding diluting gradients. [RoboTwin/PI05]#

2. Standard GPU telemetry tools failed in Kubernetes due to PID namespace isolation. [Infrastructure]#

3. Probabilistic LLM outputs produced malformed JSON, crashing analytical pipelines. [Research Scout]#

4. Curobo JIT compilation failed due to missing CUDA headers in isolated conda/pip environments. [RoboTwin]#

5. macOS media playback failed due to TCC/Sandbox permission expiration. [Desktop Wallpaper App]#

Learnings#

Architecture (architecture)#

Debugging (debugging)#

Domain Knowledge (domain)#

AI Usage Notes#

Next Week Outlook#

Token Usage Statistics#

Weekly Report — 2026-W11 (2026-03-09 ~ 2026-03-15)

Weekly Overview

Project Progress

Error Recovery Benchmark (v4 to v5 Transition) (5 days active) — 🔄 active

VLA / RoboTwin Training Pipeline (5 days active) — 🔄 active

Research Scout & Profiler Unification (2 days active) — ✅ completed

MIHD Spatial Omics (2 days active) — 🔄 active

Infrastructure & Tooling (GPU Monitor / MCP) (4 days active) — ✅ completed

Key Tasks

Problems & Solutions

1. VLA training loss exhibited severe oscillation due to fixed-size tensor padding diluting gradients. [RoboTwin/PI05]

2. Standard GPU telemetry tools failed in Kubernetes due to PID namespace isolation. [Infrastructure]

3. Probabilistic LLM outputs produced malformed JSON, crashing analytical pipelines. [Research Scout]

4. Curobo JIT compilation failed due to missing CUDA headers in isolated conda/pip environments. [RoboTwin]

5. macOS media playback failed due to TCC/Sandbox permission expiration. [Desktop Wallpaper App]

Learnings

Architecture (architecture)

Debugging (debugging)

Domain Knowledge (domain)

AI Usage Notes

Next Week Outlook

Token Usage Statistics