Weekly Summaries

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.

This week was defined by major architectural transitions and high-performance stabilization across robotics (VLA), bioinformatics (spatial genomics), and macOS desktop ecosystems. Key achievements included a 60% increase in GPU utilization through a new batched inference server, the optimization of LLaVA training via ZeRO-2 reducing ETA from 13.5 to 2 days, and the successful decoupling of spatial transcriptomics workloads into a high-efficiency two-stage pipeline. Significant structural improvements were also made via global symlink removal and repository refactoring, while critical macOS sandbox and rendering barriers were resolved through native window compositing and entitlement patching. Despite challenges with GPU OOM errors, Docker rendering failures, and network restrictions, the week concluded with stabilized training foundations, improved evaluation metric integrity, and a more robust, agent-friendly infrastructure.

This week was characterized by significant technical breakthroughs in multi-modal spatial omics pipelines and the architecture of a robust robotic evaluation framework. Key achievements include resolving catastrophic weight corruption in scGPT models, optimizing fusion workflows via vectorization, and establishing a highly resilient HPC training/evaluation pipeline for BC-RNN and Pi0.5 VLA models. We successfully navigated complex cluster security and networking constraints, moving from fragile, error-prone scripts to a deterministic, cross-device orchestration system that supports large-scale, long-horizon training and evaluation.

This week successfully bridged multimodal spatial transcriptome research with robotic error recovery benchmarks, transitioning from theoretical planning to validated execution pipelines. Significant progress was made in the MIHD pipeline by resolving critical coordinate swap bugs and implementing self-supervised clustering (STEGO, SCAN) to restore data integrity. Simultaneously, the Error Recovery Benchmark surpassed M5 goals through massive scene expansion and the implementation of a VLA dual-server infrastructure. While the team encountered cluster-level permission hurdles and model-specific training challenges (BC-RNN), the establishment of robust data conversion frameworks (HDF5-to-LeRobot) and automated reporting infrastructure has stabilized the foundation for large-scale multi-policy evaluation and multimodal fusion training.

This week was characterized by significant architectural overhauls and scalability improvements across multiple high-impact domains, including the implementation of two-phase caching pipelines for MIHD spatial transcriptomics (reducing redundant computation by 62%), the massive scaling of the Error Recovery robotics benchmark from 3 to 118 samples, and the hardening of the Gadget/Summarize and CalendarPro toolkits through robust multi-device sync and OAuth optimization. Despite facing hardware resource constraints like VRAM saturation and environmental dependency blockers in robotics, key progress was made in establishing crash-resilient, cost-effective workflows and structured multi-phase enhancement roadmaps for future development.

This week focused on stabilizing the MIHD multimodal pipeline through rigorous architectural alignment with academic baselines, resolving critical benchmark documentation gaps, and engineering a secure automated submission infrastructure. Significant progress was made in establishing a robust evaluation paradigm by porting the QueST RM-Ideal metric into MIHD, enabling high-fidelity spatial niche benchmarking, and resolving foundational issues in metric masking and environment reproducibility across HPC clusters.

This week focused on critical troubleshooting of the MIHD spatial transcriptomics pipeline, specifically addressing a significant benchmark accuracy regression. Efforts successfully restored ARI metrics to expected levels by rectifying environment mismatches, realigning training hyperparameters with the original STAIG architecture, and patching GCN activation logic discrepancies.

The week focused on the initial setup and session initialization for the AutoSeg-SAM2 project. While the codebase environment was established, there were no substantive technical developments or task completions recorded during this period.