Parallel progress across DCC, tianhe, and TzJsDesktop: MIHD cross-modal enhancement module implementation and RM-IDEAL cross-sample evaluation, robot error recovery benchmark quota-based data generation (exposing online architecture limitations and establishing the offline injection approach), VLA robot framework engineering (UniVLA/Pi0/RoboTwin data pipelines), and upgrading gadget tools into AI Agent-callable MCP services with CalendarPro integration.
Completed a large-scale error_recovery_benchmark refactor on the tianhe server (full cleanup of 65 symlinks + code path migration), developed multi-task error scenario MP4 visualization and a pre-error replay storage layer, advanced the VLA-RoboTwin critical region annotation system and Vulkan rendering fix, and locally added author search and several other features to gadget research_scout.
Completed the full lifecycle build of the gadget Research Scout paper management system in a single day with first validation producing 3 research directions; advanced CalendarPro intent classification system root cause analysis and cross-project architecture spec design (introducing OpenClaw context window management model); and batch-filled three days of backlogged daily reports.
Three devices progressing in parallel on the same day: DCC traced the normalization pipeline for ContraVAE and STHD spatial transcriptomics data; tianhe fixed three consecutive bugs in the RoboTwin evaluation pipeline (Vulkan rendering, frozen dataclass, duplicate path) and completed the Phoenix/FLARE training script suite covering all 9 tasks; TzJsDesktop wrote a 1674-line Chinese tutorial for CalendarPro and identified three root causes behind the Discord bot’s cross-message context loss.
Parallel progress across three machines (DCC/tianhe/TzJsDesktop) on four projects: DCC completed the MIHD multimodal spatial omics cross-sample benchmark and established a zero-shot narrative framework; tianhe deployed the zhaoganlong framework’s 9-task training pipeline, launched two training runs, fixed an Openpi-moe normalization issue, and advanced the Phoenix/FLARE codebase separation; TzJsDesktop upgraded CalendarPro into a personal AI chief-of-staff with autonomous perception and multi-agent orchestration capabilities (321 tests passing)
DCC conducted an in-depth analysis of the QueST spatial transcriptomics paper, focusing on its cross-sample query and evaluation methodology; tianhe completed multiple project documentation updates (error recovery benchmark panorama summary.md + zhaoganlong training guide), diagnosed the root cause of BC-RNN zero success rate, and planned the an49 full-task training infrastructure.
Completed cross-section RM-IDEAL benchmark implementation and methodology documentation on DCC; completed full evaluation of Pi0.5 LoRA (58.9% vs BC-RNN 0%) on the Tianhe supercomputing cluster, verified training weight sources (confirmed all use pi05_base), and resumed training for 9 tasks.
Completed the full scGPT+UNI2 fusion experiment suite and weekly report visualization on DCC, identified the key architectural fact that STAIG does not use the gene encoder; managed migration and restart of 9 Pi0.5 LoRA training jobs across two Tianhe nodes (an49/an53), fixed multiple critical issues in the eval pipeline (WebSocket timeout, JIT concurrency crash), conducted in-depth root cause analysis of the ~10% GPU utilization in VLA inference, and completed the design of a batched concurrent eval optimization plan.
Parallel progress across four projects on DCC and tianhe servers: DCC completed VisiumHD three-annotation visualization and STAIG fusion multimodal run (Silhouette 0.343), and executed a 6-phase large-scale cleanup refactor of the MIHD repo (deleted ~250K lines of code); tianhe completed Pi0.5 9-task base model evaluation (4.2% SR), diagnosed and fixed missing Coffee BC-RNN object observation key, developed the BC-RNN Stack_D0 error scenario generation pipeline (fixed 4-layer image bug, generated 11 scenarios), and successfully launched Pi0.5 LoRA 9-task parallel fine-tuning at 100% GPU utilization on 6×A800 after fixing 4 startup bugs.
Completed full-pipeline Visium HD extension for the MIHD project on DCC (8 files modified) and fixed a critical scGPT weight-loading bug (ARI +44.4%); on tianhe, built the BC-RNN Phoenix baseline training pipeline from scratch and successfully launched 9 tasks in parallel training, while also completing the first Pi0.5 Phoenix evaluation deployment (7/9 tasks done).