Daily Report — 2026-02-22
Daily Overview
- What was done: Advanced spatial transcriptome vision refinement architectures, engineered Phase II robotic error recovery evaluation pipelines, and deployed MimicGen-to-LeRobot conversion frameworks for foundation model fine-tuning.
- How it was done: Implemented self-supervised clustering baselines, refactured deadlock-prone SLURM/SSH scheduling into dynamic GPU allocation workflows, synchronized multi-task dataset converters with JAX scaling rules, and executed comprehensive project asset restructuring.
- Impact: Established reproducible multimodal performance baselines, unlocked scalable simulation infrastructure for Phase II paper drafting, and eliminated cross-device coordination bottlenecks through unified documentation and environment standardization.
DCC
- What was done: Executed MIHD image-only clustering experiments and vision encoder literature synthesis.
- How it was done: Integrated STEGO/BYOL+GAT/SCAN modules into run_benchmark.py, patched dense cosine similarity NaN divergences, and validated ARI metrics on DLPFC slices to confirm fusion baselines.
- Impact: Solidified the numerical stability of self-supervised pipelines and established quantitative performance ceilings for future multimodal alignment strategies.
MacBook
- What was done: Managed strategic planning, technical debt remediation, and autonomous documentation patching.
- How it was done: Condensed CLAUDE.md/README.md artifacts, consolidated historical plans into a unified structure, and utilized automated scanning to resolve CLI benchmark marker gaps for the Gadget toolset.
- Impact: Eliminated information redundancy, aligned cross-project strategic directives, and restored accurate architectural schemas for future development cycles.
tianhe
- What was done: Orchestrated Error Recovery Benchmark Phase II dependency mapping, M14 baseline execution, and MimicGen data pipeline construction.
- How it was done: Resolved SLURM/SSH deadlocks via srun standardization, patched VLA port binding conflicts, engineered HDF5-to-LeRobot conversion scripts, and configured multi-GPU JAX training parameters for pi0.5_base.
- Impact: Bypassed cluster permission locks, established scalable evaluation scaffolding, and synchronized dataset compatibility to enable seamless open-source framework integration.
Today’s work successfully bridged multimodal spatial transcriptome fusion pipelines with robotic error recovery infrastructure, establishing scalable evaluation baselines, restructuring critical project documentation, and engineering foundation model fine-tuning workflows across heterogeneous cluster environments.
Tasks
Architecture & Strategy
- ✅ MIHD Self-Supervised Clustering & Vision Refinement Pipeline Implementation — Integrated STEGO, BYOL+GAT, and SCAN modules into benchmark workflows, patched float32 overflow issues via LogSumExp normalization, and validated ARI baselines to establish pre-refined fusion strategies.
- 🔄 Error Recovery Benchmark Phase II Dependency Mapping & M14 Baseline Setup — Mapped milestone topologies, established dynamic VRAM-aware Slurm scheduling, implemented resume-capable evaluation collectiors, and verified collector output schemas.
- ✅ End-to-End STAIG GCN+STAIG Fusion System Development — Architected dynamic gene preprocessing routing, fixed hardcoded encoder constraints, and aligned adaptive edge dropout mechanisms to ensure cross-modal gradient stability.
- ✅ Multi-Task LeRobot Data Conversion & OpenPI Training Configuration — Engineered unified HDF5-to-Parquet conversion scripts for 9 MimicGen tasks, updated OpenPI training configs, and resolved HuggingFace datasets version compatibility breaks.
- ✅ Cluster Scheduling Overhaul & VLA Server Integration — Replaced deadlocked SSH workflows with srun/sbatch directives, resolved EGL/CUDA physical mapping inconsistencies, and implemented dynamic port allocation routines for Pi0.5 inference servers.
Implementation & Fixes
- ✅ Project Documentation Restructuring & Technical Debt Cleanup — Compressed CLAUDE.md, archived redundant scripts/logs, unified research plans in docs/plans.md, and documented Vision Encoder/Lit Review comparative baselines.
Problems & Solutions
Critical Issues
1. E2E training rigidity and adaptive edge dropout misalignment degrading cross-modal performance
Solution: Replaced hardcoded gene_encoder constraints with explicit preprocessing routing (auto/standard/clip/none) and realigned edge weight directions to enforce cross-domain penalty gradients.
Key Insight: Flexible input embedding acceptance paired with precise dropout masking is critical for enforcing alignment rather than intra-domain similarity capture.
2. LeRobot-HuggingFace datasets library incompatibility causing parquet metadata TypeErrors
Solution: Pinned the huggingface/datasets dependency to versions below 4.0 to restore compatibility with LeRobot v0.1.0 tensor stack expectations.
Key Insight: Breaking API changes in core data libraries silently corrupt schema validation; strict environment pinning is mandatory during framework migration.
3. Numerical overflow in dense self-supervised similarity matrices causing training divergence
Solution: Applied L2 normalization coupled with LogSumExp stabilization and forced float64 computation to prevent exponential saturation during softmax operations.
Key Insight: High-temperature cosine similarity matrices in float32 rapidly saturate; mathematical stabilization is required before gradient descent initialization.
4. Cluster resource deadlocks, EGL/CUDA mapping mismatches, and hard dependency blockers
Solution: Pivoted from SSH direct execution to standard srun/sbatch queues, aligned MuJoCo physical device IDs with CUDA virtual environments, and implemented dynamic fallback routing for network downloads.
Key Insight: Hardware-abstracted layers must be manually synchronized; enforcing standard scheduler interfaces eliminates permission deadlocks more reliably than policy workarounds.
General Issues
5. Foundation model VLA OOM constraints, port binding conflicts, and baseline latency miscalculation
Solution: Downscaled batch dimensions, allocated larger GPU tiers, implemented pre-launch port scanning, and recalibrated evaluation pacing based on empirical sanity-run inference speeds.
Key Insight: Base model success variance across heterogeneous scenes dictates actual throughput; dynamic resource allocation and real-world latency profiling must replace static estimates.
Human vs AI Approaches
Strategic Level
Strategic Architecture Design vs Engineering Scaffolding
| Role | Approach |
|---|---|
| Human | Defined the two-phase vision refinement paradigm, established Phase II academic milestones, and enforced dynamic resource allocation constraints to bypass performance ceilings. |
| AI | Translated high-level directives into executable dependency topologies, CLI parameter expansions, resume-capable collectors, and scalable multi-GPU JAX sharding blueprints. |
Difference Analysis: Human input provided strategic boundaries and research objectives; AI focused on structural validation, automated scaffolding, and operationalizing constraints into reproducible workflows.
Cross-Modal Feature Routing & Preprocessing Boundaries
| Role | Approach |
|---|---|
| Human | Explicitly rejected hardcoded encoder limitations, mandating that any pre-extracted gene embedding must dynamically route through the E2E pipeline. |
| AI | Diagnosed dimension mapping failures and rapidly implemented auto-detection routing alongside standardized preprocessing adapters to match user constraints. |
Difference Analysis: Human突破传统训练范式局限，直接点明架构设计缺陷；AI经修正后快速实现自动维度检测与预处理路由。
Direct Execution Control vs Iterative Approval Workflows
| Role | Approach |
|---|---|
| Human | Bypassed default AI planning gates for rapid task dispatch, maintaining absolute control over execution pacing and milestone prioritization. |
| AI | Adapted immediately from exploratory scaffolding to high-signal command interfaces, maintaining structural validation in the background without procedural friction. |
Difference Analysis: 人类追求高效直接的指令下达权，AI初期受限于工作流模板；适配后AI能无缝承接直接执行请求并产出高质量交付物。
AI Limitations
Critical Limitations
- Superficial methodological validation and lack of deep diagnostic depth: Over-reliance on abstracts/titles causes methodology misclassification, while subtle numerical divergences require explicit prompts rather than triggering automatic root-cause analysis.
General Limitations
- Infrastructure boundary compliance limitations: Cannot autonomously bypass hardware ACLs, exceed physical RAM limits, or override strict cluster scheduling policies; restricted to orchestrating compliant workarounds and fallback routing mechanisms.
- Context window fragmentation and persistent state blindness: Struggles with monolithic file restructuring and maintains no autonomous awareness of background SLURM/bash job states, necessitating manual polling or iterative context injection.
Learnings
Key Learnings
- Performance ceilings of base models: Pure vision encoders inherently plateau on fine-grained spatial stratification without self-supervised constraints or multimodal fusion; task-feature mismatch is an architectural characteristic rather than an implementational bug.
- Core algorithmic mechanisms: Adaptive edge dropout successfully forces cross-modal alignment by penalizing over-reliance on high-confidence intra-domain edges, while OpenPI’s automatic JAX sharding handles multi-GPU scaling transparently given valid batch divisibility.
Practical Learnings
- Engineering and infrastructure best practices: Framework migrations demand strict dependency pinning (e.g., datasets <4.0 for LeRobot), heavy CLI tools require defensive parameter validation to prevent context drift, and strategic documentation requires periodic structural reconstruction over incremental patching.
Conversation Summaries
MIHD Foundation Models Research & Spatial Fusion
✅ Image-Only Clustering Baselines, Vision Refinement Architecture, and Documentation Restructuring 20:42:56.462 | claude_code The session systematically benchmarked 151673 DLPFC slices against UNI/UNI2 baselines, achieving ARI ~0.303 with SCAN clustering. Self-supervised modules (STEGO/BYOL) were integrated to address float32 overflow divergence. Strategic architectural pivots established a two-phase pre-refinement fusion pipeline, while comprehensive asset cleanup compressed reference artifacts and unified research plans into a single strategic index.
Error Recovery Benchmark & Phoenix Fine-tuning Pipeline
✅ Phase II Dependency Topology, M14 Baseline Execution, LeRobot Data Conversion, and Multi-GPU Scaling 19:40:17.347 | claude_code Mapped hierarchical milestone dependencies for Phase II research objectives, establishing M14 as the critical evaluation unlock. Resolved SLURM/SSH deadlocks by standardizing on srun/sbatch and synchronized Pi0.5 VLA inference servers with dynamic port allocation. Architected a MimicGen-to-LeRobot conversion framework for 9 RoboMimic tasks, downgraded incompatible datasets libraries to prevent schema corruption, and designed scalable multi-GPU JAX training configurations for pi0.5_base baseline comparisons.
Gadget CLI Toolset Maintenance
✅ Automated Documentation Gap Resolution and Command Line Standardization 19:59:39.822 | claude_code Leveraged automated codebase scanning to identify and patch missing Hugo pipeline descriptions and CLI benchmark flags within CLAUDE.md and README.md. Unified cross-references and directory mappings to eliminate long-standing architectural documentation fragmentation.