Daily Report — 2026-03-04

Today’s Overview

What was done: Parallel research and engineering work across two machines: DCC analyzed the QueST paper to inform cross-sample query design for the MIHD project; tianhe advanced documentation alignment, performance diagnosis, and training infrastructure for the error recovery benchmark.
How it was done: DCC retrieved the paper’s HTML version via multiple rounds of WebFetch/WebSearch and extracted technical details; tianhe used parallel Agents to explore the codebase, compared documentation against implementation, updated documents with Edit/Write tools, and planned training orchestration scripts.
Why it matters: A structured understanding of QueST’s evaluation methodology can be directly applied to MIHD evaluation design; error recovery benchmark documentation fidelity improved significantly; root-causing BC-RNN’s zero success rate provides a basis for future tuning; the an49 training pipeline plan is complete, laying the groundwork for multi-task training deployment.

DCC

What was done: Read and analyzed the QueST paper (arXiv:2410.10652v3): a complete methodology and evaluation design for a cross-sample spatial transcriptomics niche query framework.
How it was done: After a failed PDF parsing attempt, switched to the HTML version to obtain architectural details; supplemented v3 update content via WebSearch and Moonlight/GitHub (since v3 has no HTML version); produced a structured Chinese summary of the graph autoencoder, contrastive learning, adversarial batch correction modules, and the WWL Kernel evaluation scheme.
Why it matters: Clarified how QueST uses the WWL Graph Kernel to construct pseudo ground truth for cross-sample niche query evaluation, providing a reference for the MIHD project’s evaluation metric design.

tianhe

What was done: Completed multiple parallel workstreams for the error recovery benchmark: updated the project panorama summary.md (aligned implementation details + corrected M14 evaluation data), created the zhaoganlong training guide document, diagnosed the BC-RNN zero success rate, planned the an49 full-task training schedule, and modified data preparation scripts to enable all 9 tasks.
How it was done: Used multiple parallel Agents to explore the codebase (error_framework: 53 files, scripts: 22 scripts) and compare documentation; read summary.json directly for actual evaluation data; checked GPU utilization, dataset status, and checkpoint existence; used the Edit tool to make precise modifications to large Markdown documents.
Why it matters: Documentation fidelity improved dramatically (M14: 726 ep → 6474 ep; implementation details went from blank to comprehensive); root-caused BC-RNN Normal SR=0% as an observation key bug (not a model capability issue); the an49 training plan is complete, and the data preparation script modifications lay the foundation for enabling all 9 tasks.

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.

Today’s Tasks

Architecture & Strategy

✅ Update project panorama summary.md — align documentation with actual code — Section 3.2 now includes detailed implementation descriptions for detectors/injectors/validators/classifiers/core modules; corrected supported VLA models (Pi0-FAST → Phoenix/Flare); updated code statistics (scripts 18→22, test cases 109→122); updated M14 evaluation data (726 ep→6474 ep with actual SR/RP); added by_phase and by_severity statistics to the database.
✅ Diagnose root cause of BC-RNN zero success rate on normal tasks — User observed that BC-RNN training rollout results were completely inconsistent with evaluation results; after exploring the code, AI identified an object observation key bug in the baseline_accuracy evaluation that caused SR=0%; Pi0.5 Per-Task LoRA performed well on normal tasks (Stack_D0 ~95%), in stark contrast to BC-RNN’s 0%.
✅ Analyze QueST paper’s cross-sample query method and evaluation design — Read arXiv:2410.10652 (QueST), extracting the graph autoencoder architecture for cross-sample niche queries, contrastive learning design, adversarial batch removal, and the evaluation method using WWL Graph Kernel to construct pseudo ground truth. User followed up on the evaluation section and requested the v3 version.
✅ Create zhaoganlong training guide document — Created docs/zhaoganlong_training_guide.md, covering the MPM→MCM closed-loop framework overview, full commands and hyperparameters for all 6 training stages, filesystem verification for all checkpoints (Diffusion Policy / OpenPI Base / Pi0.5 Phoenix), the three inference pipeline modes, and a complete path reference.
✅ Plan an49 full-task training schedule (zhaoganlong 6 stages) — Drafted training plan: GPU allocation (GPU 0 for Diffusion Policy, GPU 1-4 for LLaVA MPM/MCM, GPU 2-5 for Pi0.5), 4-step data preparation pipeline, tmux session management, all 9 MimicGen tasks, checkpoints stored under the tangzijia directory.
✅ Update core pipeline diagram to show three parallel input paths — User pointed out that the original pipeline diagram had only Demo Dataset as the entry point, omitting the VLA/Policy Rollout injection and natural failure capture paths; AI updated Section 3.1 to show all three parallel paths feeding into the ErrorSceneDatabase.

Implementation & Fixes

🔄 Modify data preparation scripts to enable all 9 tasks — Requires modifying 4 scripts (create_5hz_dataset_new_motion.py, create_speed_dataset.py, generate_llava_json_dataset.py, generate_render_llava_dataset.py) to uncomment all 9 tasks and remove pdb breakpoints; also requires copying 6 missing HDF5 files to origin_datasets/. Plan was complete but execution was interrupted by user.
🔄 Generate Pi0.5 visualization videos — User requested visualization videos of Pi0.5 policy rollout; AI confirmed the outputs/pi05_eval_results/videos/ directory was empty and that the VLA server on an49 would need to be started before running visualize_policy_rollout.py; execution was interrupted before completion.

Issues & Solutions

Critical Issues

1. BC-RNN SR=0% on all normal tasks, completely inconsistent with training rollout

Solution: Code exploration traced the issue to an object observation key bug in the baseline_accuracy evaluation, causing the policy to receive empty observations; this is a bug in the evaluation script, not a model capability issue.

Key insight: When training and evaluation results diverge, check whether the observation space is consistent (key names, dimensions) before assuming the model failed to learn.

2. Documentation described a pipeline with multiple discrepancies from the actual code (incorrect VLA model types, outdated code statistics, M14 data reflecting only early results)

Solution: Systematically launched 3 parallel Agents to explore different dimensions of the codebase, collected actual figures (file counts, function counts, evaluation results), and made precise Edit updates to the documentation entry by entry.

Key insight: Large project documentation tends to drift from the code; regular “docs vs code” alignment is key to maintaining project readability, and the code should be treated as the source of truth.

General Issues

3. Data preparation scripts contained leftover pdb.set_trace() breakpoints and only had coffee_d1 enabled, making it impossible to batch-run all 9 tasks

Solution: Read all 4 scripts line by line to identify pdb call locations and commented-out task lists, then used the Edit tool to batch-uncomment tasks and remove breakpoints.

Key insight: Leftover debug breakpoints in shared codebases cause silent hangs in automated pipelines; these should be a first-priority check during implementation planning.

4. arXiv PDF could not be parsed directly (FlateDecode binary stream), and v3 also had no HTML version (404)

Solution: Used WebSearch to find the v1 HTML version for the core architecture, then supplemented v3 differences via Moonlight literature review site and GitHub repository.

Key insight: Not all arXiv paper versions have HTML rendering; secondary literature review platforms can be used to obtain summaries of the latest version’s changes.

5. The project panorama summary.md exceeded 25,000 tokens, making it impossible to retrieve the full content in a single Read tool call

Solution: Used offset+limit for segmented reading, combined with Grep to pinpoint lines needing modification, avoiding full-document loading.

Key insight: For large documents, segmented reading + targeted Grep is more efficient than loading everything; critical edits should start by Grep-confirming line numbers before using Edit.

Human Thinking vs. AI Thinking

Strategic Level

Interpreting BC-RNN Evaluation Results

Role	Approach
Human	User observed that their own rollouts produced varied results and questioned the credibility of BC-RNN’s uniformly 0% scores in the M14 evaluation; later explicitly clarified they were referring to Normal rollout SR, not Error Recovery SR.
AI	AI initially explained that M14 specifically evaluates error recovery scenarios, making SR≈0% expected, but did not proactively distinguish Normal SR from Error SR; required user follow-up to clarify.

Difference analysis: The user maintained healthy skepticism about the results (“I’ve seen different outcomes”), which drove the investigation into the normal rollout baseline and ultimately uncovered the BC-RNN observation key bug. The AI tended to rationalize existing results.

Discovering that the pipeline documentation was missing the VLA Rollout injection path

Role	Approach
Human	User proactively pointed out that the core pipeline diagram in the documentation only had Demo Dataset as an input, which didn’t match the actual design — it should also support injecting errors during VLA model rollouts and capturing natural failures, with all three sources feeding into the ErrorSceneDatabase.
AI	AI described the three existing paths (Demo injection / Policy Rollout injection / Natural capture) but the documentation diagram only depicted one of them; AI did not proactively identify the discrepancy between the diagram and the user’s intent.

Difference analysis: The user identified a documentation gap from a system design perspective, while the AI accurately reflected the existing documentation state — a difference between high-level architectural awareness and textual accuracy.

Implementation Level

Switching from QueST paper v1 to v3

Role	Approach
Human	After AI cited v1 analysis, user directly noted “don’t use v1, there’s a v3 with updates.”
AI	AI did not proactively check for newer versions and defaulted to using the first HTML link returned by Google search (v1).

Difference analysis: The user had stronger awareness of version management for literature; when retrieving papers, AI should default to checking for the latest version.

Scope and goals of the zhaoganlong training plan

Role	Approach
Human	User explicitly chose all 6 stages, multi-task data, and checkpoints stored in the tangzijia directory — these were concrete engineering decisions.
AI	AI confirmed via AskUserQuestion before starting design, and adjusted parallelization strategy based on the actual state of the codebase (GPU utilization, data preparation status).

Difference analysis: The user provided goal constraints; the AI was responsible for translating constraints into a detailed, executable plan — a good collaborative division of work.

AI Limitations

Critical Limitations

When interpreting M14 evaluation results, the AI tended to rationalize “all results being 0%” (error recovery scenarios naturally have low SR) rather than proactively asking about the user’s reference frame (Normal SR vs Error SR); required user correction to clarify.
Unable to proactively identify systematic discrepancies between documentation and code; required the user to point them out one by one before alignment began. The ideal behavior would be to systematically scan all figures and descriptions against the code when modifying documentation.

General Limitations

Multiple parallel Agent tasks timed out in the same session (timeout 1122s), possibly due to remote filesystem I/O latency; Agent planning should break large exploration tasks into smaller read granularities.
When retrieving papers, defaulted to the first search result link (v1) without proactively checking for newer versions (v3); required user prompting to switch, and when v3 HTML was unavailable, had to supplement via third-party channels.
PDF files could not be parsed directly (returned binary stream), causing the first WebFetch call to fail and requiring user confirmation; for arXiv PDF URLs, the default approach should be to try the /abs/ page or HTML version first.

Today’s Takeaways

Core Takeaways

QueST’s core cross-sample evaluation idea: when no ground truth exists at the niche level, use cell type annotations + WWL Graph Kernel to construct pseudo ground truth, then use Pearson correlation to quantify the consistency between model similarity ranking and kernel ranking — a general method for establishing a comparable benchmark in unsupervised embedding space.
The performance gap between Pi0.5 Per-Task LoRA and the Global Model is enormous (Stack_D0: ~95% vs ~24%), demonstrating that foundation models require task-level fine-tuning to reach their potential on specialized tasks; the Global model’s 58.9% average SR masks severe per-task imbalance.
A clear distinction between Normal SR and Error Recovery SR is a core metric design principle for the error recovery benchmark: M14’s near-0% Error SR is not a model failure — it is the central thesis the benchmark aims to demonstrate (existing policies lack error recovery capability), and the contrast with Normal SR is what makes the argument.
The zhaoganlong framework’s three-module design: Motion Prediction Module (MPM) predicts future motion direction and encodes it into a 37-dimensional codebook → Motion-Conditioned Diffusion Policy receives the codebook to generate actions → Motion Correction Module (MCM) detects execution deviations and generates correction instructions, forming a closed-loop self-reflection mechanism.

Practical Takeaways

Large project documentation alignment strategy: treat the code as the source of truth, use parallel Agents to collect actual state from multiple dimensions (error_framework, scripts, outputs), then systematically Edit the documentation section by section — 3–5× more efficient than manual line-by-line comparison.

Session Summaries

MIHD

✅ Analyze QueST paper: cross-sample spatial transcriptomics niche query method and evaluation design 22:21:12.826 | claude_code User requested analysis of how arXiv:2410.10652v3 (QueST) implements cross-sample queries. AI first encountered PDF parsing failure, then obtained complete technical details via the HTML v1 version (GIN encoder + adversarial batch correction + cosine similarity retrieval). User further requested details on the evaluation method; AI extracted the design of WWL Graph Kernel as pseudo ground truth and two evaluation metrics (Best Niche Match Accuracy + Pearson correlation). User requested the v3 version, but v3 has no HTML; the Moonlight review site was used to supplement the methodological overview.

Error Recovery Benchmark

✅ Update project panorama summary.md: align documentation descriptions with actual code implementation 03:04:05.254 | claude_code Parallel Agents deeply explored the error_framework (53 files), scripts, and outputs directories, identifying 5 core discrepancies between documentation and code. Completed 5 precise updates: Section 3.2 now includes detailed implementation descriptions for detectors/injectors/validators/classifiers; VLA supported models corrected to Phoenix/Flare; code statistics updated; M14 evaluation data updated from 726 ep to 6474 ep (with actual SR/RP); database statistics now include phase and severity distributions.

✅ Diagnose BC-RNN zero success rate: identify observation key bug and summarize Pi0.5 normal task performance 00:31:43.102 | claude_code User reported a large discrepancy between BC-RNN training rollout and evaluation results. AI explored the code and traced the issue to an object observation key bug in the baseline_accuracy evaluation — the fundamental cause of BC-RNN Normal SR=0%, not insufficient model capability. Also found that Pi0.5 Per-Task LoRA performed well on normal tasks (Stack_D0 ~95%, overall ~58.9%), in stark contrast with Global model’s 4.22% and M14 Error Recovery’s 0%, clearly distinguishing the Normal SR and Error Recovery SR metrics.

✅ Create zhaoganlong Motion-based Self-Reflection Framework training guide 22:23:48.001 | claude_code User wanted to understand the zhaoganlong framework’s training process and checkpoint status. After 3 parallel Agents explored the codebase, AI found the framework contains 3 trainable modules (MPM/MCM/Diffusion Policy) and 6 training stages. Created docs/zhaoganlong_training_guide.md, including complete training commands, hyperparameters, checkpoint filesystem verification (Diffusion Policy 4×253MB available, LLaVA MPM/MCM missing, Pi0.5 Phoenix 180GB available), and three inference pipeline modes.

🔄 Correct core pipeline diagram: add VLA Rollout injection and natural failure capture paths 00:53:32.952 | claude_code User pointed out that the project documentation’s pipeline diagram only showed the Demo Dataset entry, omitting the VLA/Policy Rollout injection and natural failure capture paths. AI updated Section 3.1 to show all three parallel inputs feeding into the ErrorSceneDatabase. User further questioned the ErrorSceneDatabase design; AI analyzed the database.py and core.py implementations in depth, explained the current design’s structure and API, and completed comprehensive code vs. documentation alignment work (ExitPlanMode was rejected).

🔄 Understand zhaoganlong retry/reset framework model training and plan documentation 21:24:32.032 | claude_code User asked about the zhaoganlong framework’s training process and checkpoint status. AI launched 3 Agents to explore but all timed out; after restarting, successfully completed the exploration and designed a plan for creating zhaoganlong_training_guide.md (user rejected ExitPlanMode), laying the foundation for the document creation in the 22:23 session.

🔄 Implement an49 full-task training plan: modify data preparation scripts to enable 9 tasks 22:56:14.823 | claude_code User provided a detailed training plan; AI began execution: read 4 data preparation scripts in parallel, confirmed that only coffee_d1 was active and pdb breakpoints were present; verified source HDF5 files (all 9 tasks available in tangzijia/mimicgen_datasets/core/); checked that 6 files were missing from origin_datasets/. Began modifying scripts to enable all tasks and remove breakpoints, but execution was interrupted by the user.

❌ Generate Pi0.5 visualization videos 00:26:04.288 | claude_code User requested Pi0.5 rollout visualization. AI found the outputs/pi05_eval_results/videos/ directory exists but video files were empty (not successfully generated); the VLA server on an49 would need to be started before running visualize_policy_rollout.py –policy vla_pi05; video generation could not be completed due to session interruption.

✅ Ask how to download project panorama summary.md to local machine 22:34:38.641 | claude_code User asked how to download 项目全景总结.md to their local machine; AI provided four methods with specific commands: SCP, SFTP, VS Code Remote Explorer, and FileZilla.

Token Usage

Overview

Metric	Value
Total Tokens	21,258,501
Input Tokens	35,540
Output Tokens	77,340
Cache Created	1,428,509
Cache Read	19,717,112
Cache Hit Rate	93.2%
Total Cost (USD)	$13.4929

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-opus-4-6	10,163	24,198	732,760	12,812,265	$11.6416	86.3%
claude-haiku-4-5-20251001	25,377	53,142	695,749	6,904,847	$1.8513	13.7%

Per-Device Usage

Device	Total Tokens	Input	Output	Cost
DCC	289,447	242	2,232	$0.3474
tianhe	20,969,054	35,298	75,108	$13.1455

Daily Report — 2026-03-04#

Today’s Overview#

DCC#

tianhe#

Today’s Tasks#

Architecture & Strategy#

Implementation & Fixes#

Issues & Solutions#

Critical Issues#

1. BC-RNN SR=0% on all normal tasks, completely inconsistent with training rollout#

2. Documentation described a pipeline with multiple discrepancies from the actual code (incorrect VLA model types, outdated code statistics, M14 data reflecting only early results)#

General Issues#

3. Data preparation scripts contained leftover pdb.set_trace() breakpoints and only had coffee_d1 enabled, making it impossible to batch-run all 9 tasks#

4. arXiv PDF could not be parsed directly (FlateDecode binary stream), and v3 also had no HTML version (404)#

5. The project panorama summary.md exceeded 25,000 tokens, making it impossible to retrieve the full content in a single Read tool call#

Human Thinking vs. AI Thinking#

Strategic Level#

Interpreting BC-RNN Evaluation Results#

Discovering that the pipeline documentation was missing the VLA Rollout injection path#

Implementation Level#

Switching from QueST paper v1 to v3#

Scope and goals of the zhaoganlong training plan#

AI Limitations#

Critical Limitations#

General Limitations#

Today’s Takeaways#

Core Takeaways#

Practical Takeaways#

Session Summaries#

MIHD#

Error Recovery Benchmark#

Token Usage#

Overview#

Model Breakdown#

Per-Device Usage#