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

This week focused on a major architectural pivot from physics-based to quota-driven Error Recovery Benchmark v5, alongside extensive infrastructure stabilization. Key achievements included migrating the MIHD spatial omics pipeline, resolving critical VLA training convergence and ABI mismatch issues on HPC clusters, and developing a unified Research Scout/Profiler CLI tool. Significant progress was made in strengthening system reliability through cascading LLM recovery chains and container-aware telemetry, effectively unblocking multi-domain robotic and academic research workflows.

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:

• Executed strategic archival of ~50 legacy v4 modules
• Designed v5 architecture featuring 11 semantic error skills and quota-driven scheduling
• Implemented context-replay engines and offline trajectory scanning

Blockers:

• ⚠️ Transition from physics-force injection to semantic skill paradigm requires full validation of new data collection loops

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

Accomplishments:

• Resolved VLA training loss oscillation via gradient scaling and task_pos_weight adjustment
• Patched LeRobot-to-PI05 converter for missing progress metrics
• Engineered four experimental progressive conditioning architectures in Flax NNX
• Fixed PyTorch/torchvision ABI mismatches and Curobo JIT compilation failures

Blockers:

• ⚠️ Incomplete data format updates for Lerobot due to API connectivity issues

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

Accomplishments:

• Consolidated research pipelines into a single CLI with citation graph integration
• Implemented multi-backend LLM routing (Haiku/Sonnet/Opus) for robust academic analysis
• Decoupled bibliometric popularity bias from core relevance scoring

MIHD Spatial Omics (2 days active) — 🔄 active

Accomplishments:

• Restructured repository into standardized DLPFC/HD/RM-IDEAL hierarchy
• Automated path migration and cross-section topology alignment

Blockers:

• ⚠️ Cross-sample patch retrieval failures identified due to coordinate system mismatches

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

Accomplishments:

• Developed container-aware Kubernetes GPU telemetry utility using /proc fd inspection
• Built standalone MCP server with strict JSON-RPC isolation for Gadget framework
• Resolved macOS TCC/sandbox permission issues for media playback

Key Tasks

• ✅ Error Recovery Benchmark v5 Architecture & Quota Scheduler — Designed 11 self-contained semantic error skills and built quota-driven distribution schedulers to replace legacy force injectors, validated with 200+ unit tests.
• ✅ VLA/RoboTwin Training Pipeline Remediation — Resolved complex ABI crashes, tensor shape broadcasting mismatches in pi0.py, and Curobo JIT mapping issues to stabilize training throughput.
• ✅ Research Scout + Profiler CLI Unification — Deployed a modular pipeline for researcher profiling with dual-API ingestion, BFS discovery logic, and cascading LLM fallback chains.
• ✅ pi05 Progressive Conditioning Experiment Architecture — Designed four experimental pipelines combining feature extraction (last-token vs special-token) with time/distance prediction via config-driven flags.
• ✅ MIHD Spatial Omics Restructuring — Consolidated fragmented output directories and updated all downstream code paths/documentation to a standardized hierarchical layout.
• ✅ Gadget Framework MCP Server Integration — Built a standalone MCP server wrapping CLI tools with strict stdout isolation for secure AI agent interaction.

Problems & Solutions

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

Solution: Maintained low task_loss_weight (0.1) and scaled task_pos_weight (~5.0) while implementing masking over non-padded dimensions.

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

Solution: Engineered a monitoring utility that uses direct /proc//fd symlink inspection to map containers to physical devices.

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

Solution: Implemented a cascading LLM fallback chain (Haiku → Sonnet → Opus) with offline atomic logging.

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

Solution: Located headers within pip-installed nvidia packages and explicitly configured CPATH and CUDA_HOME.

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

Solution: Enabled app sandboxing and implemented a hybrid approach with NSOpenPanel re-authorization triggers.

Learnings

Architecture (architecture)

• Analytical pipeline resilience requires explicit multi-model fallback chains and defensive serialization defaults to handle probabilistic outputs and legacy data.
• Cross-modal retrieval (e.g., spatial transcriptomics) requires joint alignment or unified baseline generation to prevent incomparable latent spaces caused by independent preprocessing.

Debugging (debugging)

• Virtualized/HPC environments necessitate direct /proc filesystem inspection for accurate hardware telemetry and explicit TOML-level dependency overrides for C-extension stability.

Domain Knowledge (domain)

• In robotics VLA training, auxiliary loss weighting requires explicit gradient scaling relative to primary heads to prevent action learning degradation.

AI Usage Notes

Effective Patterns:

• ✓ Cascading model fallbacks for high-uncertainty tasks
• ✓ Using AI to perform automated AST-aware code refactoring and import-trace audits

Limitations:

• ✗ Initial tendency to assume standard system paths (CUDA) or standard local-server contexts in containerized environments
• ✗ Difficulty navigating shifting debugging parameters in rendering tasks without explicit human guidance

Next Week Outlook

Prioritize the completion of the Lerobot data format update (addressing API connectivity issues) to support progressive conditioning training. Finalize the v5 Error Recovery Benchmark validation by completing the full rollout of the new semantic skill trajectories. Address the MIHD cross-section retrieval failures by implementing a joint alignment or dimension expansion strategy.

Token Usage Statistics

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

Daily Average: $34.15