Daily Log — 2026-03-16

Today’s Overview

  • What was done: Parallel progress all day across three machines on three tracks: spatial transcriptomics research, robot learning data engineering, and an academic researcher profiling system — completing a full loop from experimental validation to pipeline implementation to tool refactoring.
  • How it was done: DCC ran controlled variable experiments comparing 5 embedding methods and batch-generated visualization documents; tianhe implemented 8 new modules using TDD and batch-generated error scenes via Slurm; TzJsDesktop ran the researcher profiling pipeline through a three-stage LLM prompt chain (analysis → repair → award recognition), and improved tool reliability through /simplify and two rounds of code refactoring.
  • Why it matters: Confirmed scGPT gene Foundation Model’s decisive advantage in zero-shot cross-section retrieval; compressed VLA training data requirements from 1,740 to 329 demos (81% savings); completed profiles for 20+ researchers across multiple domains and fixed systematic S2 disambiguation failures, establishing a reliable data foundation for future bulk analysis of key academics.

DCC

  • What was done: Completed cross-section embedding diagnostic experiments (5 methods × 14 combinations), batch-generated visualization PDFs (5 full sets + 35 per-layer sub-files), and rewrote the diagnostic report as separate English and Chinese documents.
  • How it was done: Extended benchmark_rm_ideal.py to support scGPT/UNI2 embedding sources, wrote visualize_cross_section_experiments.py for batch Letter-format PDF generation, and used PyMuPDF to convert large PDFs to PNGs embedded in Markdown.
  • Why it matters: Produced directly citable English and Chinese diagnostic reports, confirming scGPT (100% hit rate) far outperforms UNI2 (71%) and PCA/STAIG (0–14%), providing a complete visual evidence chain for paper writing.

TzJsDesktop

  • What was done: Batch-processed 20+ researcher academic profiles (trajectory analysis, JSON repair, conference award recognition), ran /simplify three-dimensional parallel code review, completed two rounds of Research Profiler robustness refactoring (retry logic + disambiguation scoring + three-level parsing chain), and validated against Yiran Chen (Duke University).
  • How it was done: Pipeline driven by a three-stage LLM prompt chain; /simplify triggered three parallel sub-agents (reuse/quality/efficiency) to review a 443KB diff; two rounds of code changes updated semantic_scholar.py/analysis.py/cli.py file by file; when S2 rate-limited, used WebSearch to find the correct authorId.
  • Why it matters: Completed multi-domain researcher profiles, identified 5+ severe name collision contamination cases; after fixes, Pieter Abbeel (h=164) is now correctly identified; eliminated 6 code issues including 2 efficiency optimizations (DiskCache hot-path redundancy + unnecessary LLM calls).

tianhe

  • What was done: Designed the VLA error recovery data collection plan (RBG grouping + 329-demo budget), implemented the full 8-file pipeline (all 139 tests passing), fixed the CompositeBodyObject coordinate transform bug, launched batch error scene generation across all tasks (Slurm job 49363), and developed VLA evaluation auxiliary tools (manip_progress overlay + CALVIN format conversion).
  • How it was done: Surveyed MimicGen/IntervenGen literature to design 5 RBG groups; implemented recovery_types/segmenter/collection augmentation conversion scripts module by module using TDD; used git blame to trace the CompositeBodyObject bug to commit 398af01b; submitted GPU batch jobs via Slurm sbatch + tmux tzj.
  • Why it matters: Compressed 1,740 naive collection requirements down to 329 (81% savings); all 139 unit tests pass after the fix, with the coffee machine model’s lid_main position corrected from the wrong 0.211m back to 0.1045m; error scene generation is running on an46 A800 GPU.

Parallel progress across three machines — DCC, tianhe, and TzJsDesktop: DCC completed cross-section Foundation Model validation for spatial transcriptomics (scGPT 100% hit rate) and bilingual documentation; tianhe finished the 329-demo VLA error recovery collection plan, full pipeline implementation, fixed the CompositeBodyObject transform bug, and launched batch error scene generation; TzJsDesktop processed 20+ researcher academic profiles in bulk and ran two rounds of Research Profiler refactoring, ultimately fixing systematic S2 disambiguation failures and achieving correct h-index recognition for prominent professors.

Today’s Tasks

Architecture & Strategy

  • ✅ Cross-section embedding diagnostic: 5-method comparison and visualization documentation — On the DCC server, compared PCA/STAIG/Raw HVG/UNI2/scGPT across 151673↔151508 cross-section RM-IDEAL evaluation (14 combinations), batch-generated Letter-format PDFs (5 full sets + 35 per-layer sub-PDFs), rewrote the diagnostic report with conclusions up front and embedded images, split into separate English and Chinese documents.
  • ✅ Error Recovery demo data collection and augmentation pipeline end-to-end implementation — On the tianhe server, implemented 8 new files per the user’s design: recovery_types.py (data structures), recovery_segmenter.py (trajectory segmentation), 2_collect_recovery_demos.py, 3_mimicgen_recovery_augment.py, 4a/4b conversion scripts (Phoenix MCM + Diffusion Policy formats), recovery_collection.yaml, and test files; added 34 unit tests, all 139 tests (including original 105) passing; updated Makefile, CLAUDE.md, and project overview.
  • 🔄 CompositeBodyObject coordinate transform bug fix and batch error scene generation across all tasks — Fixed missing locations_relative_to_corner coordinate transform logic in generated_objects.py’s init (aligned with commit 398af01b); all 139 unit tests pass after the fix, 13 coffee task demo videos successfully re-rendered; launched batch generation across all tasks via Slurm job 49363 on an46 A800 GPU (6 tasks, target 50 scenes per subtype); also added tqdm progress tracking and –skip_scan/–skip_schedule step-skipping options to the v5 pipeline.
  • 🔄 Research Profiler refactoring: three-dimensional parallel review + two-round disambiguation architecture overhaul — Ran /simplify to trigger three parallel code reviews (reuse/quality/efficiency), fixed 6 issues (missing import, duplicate SHA256 implementation, in-function import, DiskCache hot-path unnecessary mkdir, redundant LLM call, duplicate path resolution); implemented disambiguation v1 (exponential backoff retry + scoring function + name normalization) and v2 (three-level parsing chain, weight recalibration, s2_author_id field, –paper/–author-id CLI parameters, paper reverse-lookup function); v3 paper title search support planned but implementation deferred.
  • ✅ VLA error recovery data collection plan design — Surveyed MimicGen/IntervenGen/FailSafe/RESample literature, grouped 29 error subtypes into 5 RBG groups by recovery motor primitive (Re-grasp/Retrieve/Retract/Redirect/Realign), designed 6 tasks × 3-tier priority structure, set a 329-demo human demo budget (81% savings), and selected SpaceMouse teleoperation + stack task as the starting validation point.
  • ✅ Research Profiler bulk scholar analysis (20+ researchers) — Generated complete profiles with trajectory_summary/breakthroughs/research_themes for 20+ researchers across embodied AI (Yuke Zhu, Pieter Abbeel, Yunzhu Li, Shuran Song, Chelsea Finn, Sergey Levine, etc.), power electronics (Haochen Shi), analytical chemistry (Fan Chen), marine geology (P. Yan), and other fields; performed 20+ rounds of JSON repair (Chinese quote escaping) and 10+ rounds of conference award recognition; identified 5+ severe name collision cases (Xiaoxiao Liu with three independent trajectories, Yan Yang with 140 heavily mixed papers, etc.).

Implementation & Fixes

  • ✅ VLA auxiliary tool development: manip_progress video overlay (cv2) + CALVIN format conversion — Modified four files (pi0.py/policy.py/pi_model.py/deploy_policy.py) to implement real-time manip_progress prediction overlay in evaluation videos (cv2.putText white text with black outline, supporting both 1-dim and 2-dim formats); combined calvin_to_lerobot.py and rlds_dataset_builder to write rlds_to_lerobot.py implementing RLDS→LeRobot format conversion.
  • 🔄 CalendarPro full test suite fix — All 230 targeted tests pass; the full pytest suite hangs due to HuggingFace semantic routing model downloads, issue unresolved; needs pytest markers to isolate heavy tests or mock the model download.

Problems & Solutions

Key Issues

1. Per-section independent PCA/STAIG produces incomparable embedding spaces, causing SL@50=0 across 10 of 14 cross-section retrieval combinations, with normalization unable to fix it

Solution: Switched to a pretrained Foundation Model (scGPT): all sections share the same model weights, producing embeddings naturally in the same coordinate space. SL@50 improved from 0.013 to 0.416, and hit rate from 14% to 100%.

Key Insight: The root cause was not insufficient gene feature information (Raw HVG’s 86% hit rate proves information exists), but rather that per-section PCA principal component axes differ, making cosine similarity meaningless. Normalization cannot fix coordinate system inconsistency (mathematically impossible) — a Foundation Model is the only correct zero-shot solution.

2. CompositeBodyObject falls apart during env.reset() (lid floating, base offset 0.1–0.2m), but works fine during HDF5 playback (set_sim_state_flat overrides body positions, masking the issue)

Solution: Added self.locations_relative_to_corner instance attribute storage (with assertions) in generated_objects.py’s init, and restored the corner-to-center coordinate transform logic in _append_object(), aligned with MimicGen commit 398af01b.

Key Insight: set_sim_state_flat() restores saved state from HDF5 by overriding all body pos/quat — only env.reset() initializing from XML exposes the CompositeBodyObject coordinate calculation bug. The root cause was precisely located via git diff.

3. VLA error recovery data collection is expensive: 6 tasks × 29 subtypes × 10 demos = 1,740 human demonstrations

Solution: Grouped 29 error subtypes into 5 RBG groups by recovery motor primitive; demos within the same group can be shared across subtypes via augmentation; MimicGen generates 1,000+ demos from 10 source demos; total requirement compressed to 329 (81% savings).

Key Insight: Error type classification (by trigger cause) and recovery behavior classification (by motor primitive) are two different dimensions; the structural similarity of the latter is the key to enabling cross-error-type data reuse.

4. Academic databases (Semantic Scholar) incorrectly merge papers from multiple researchers with the same name, causing a single profile to span completely unrelated fields, severely distorting statistics like h-index (140 papers but h-index only 4; prominent professor Pieter Abbeel matched to a namesake with h=4)

Solution: Two-layer approach: (1) Analysis layer: LLM proactively identifies contamination and adds warnings in output, cross-validating via h-index/citation count/field breadth signals; (2) Tool layer: refactored disambiguation scoring weights (quantitative metrics dominate over string similarity) + new three-level parsing chain (exact ID → paper reverse-lookup → name search) + –author-id CLI parameter for manual specification.

Key Insight: Three signals for name collision detection: h-index to paper count ratio abnormally low, research fields that are methodologically impossible to coexist in one person, citation statistics contradicting the actual publication timeline. Core disambiguation principle: academic output differences between namesakes are often orders of magnitude apart — let quantitative metrics dominate disambiguation.

5. UNI2 vision Foundation Model performs surprisingly poorly (71% hit rate, negative Spearman r in some directions)

Solution: Accepted UNI2’s limitations in cross-sample scenarios and adopted scGPT as the primary approach. Root cause: H&E histology images have cross-sample batch effects (staining/preparation variation), making visual features unreliable across samples.

Key Insight: The Gene FM vs Vision FM performance gap (100% vs 71%) reveals that gene expression has stronger cross-sample consistency than morphological images for cross-sample tasks — broadly informative for multimodal FM selection.

6. When S2 API returns 429 rate limits, _s2_request()’s recursive retry has no termination condition, causing the program to hang indefinitely; Yiran Chen’s first run failed due to rate limiting during paper reverse-lookup and was incorrectly matched to a medical namesake (h=10 instead of h=65)

Solution: Replaced recursion with a for loop + exponential backoff (5→10→20→40→60 seconds) + S2RateLimitError thrown after 5 attempts; when rate-limited, used WebSearch to find the correct authorId (5442167) and bypassed disambiguation with –author-id.

Key Insight: Recursive retry is a resource leak risk; external API rate limiting requires a fallback strategy (manual ID specification), which validates the necessity of the new –author-id parameter.

7. Research profiler batch run experienced three rounds of total failure: sub-agent no Bash permission → conda activate failed → common module missing → ANTHROPIC_API_KEY missing

Solution: Abandoned the Agent tool and ran directly from the main session with Bash run_in_background; switched to the conda environment’s direct Python absolute path (miniconda3/envs/AI/python.exe); set PYTHONPATH; switched to –api claude_cli backend (as explicitly specified by the user).

Key Insight: Claude Code Agent sub-agents don’t inherit Bash permissions by default; Windows conda requires direct Python path in non-interactive shells; this project’s environment conventionally uses claude_cli rather than the anthropic backend — confirm the user’s API preference before starting.

8. Error scene generation pipeline was repeatedly interrupted: an53 SSH connection failure, VLA rollout data source change, repeated pipeline failures

Solution: Switched to Slurm sbatch submitted to the ai partition (specifying –partition=ai), monitored in tmux tzj on ln206; changed the collect step to use only MimicGen augmented dataset (1,000 demos/task), updated num_demos from 20 to 1,000.

Key Insight: Long-running GPU tasks should be submitted via Slurm rather than SSH nohup; the MimicGen augmented dataset is already rich enough — VLA participation in the collect stage is unnecessary.

9. When LLM generates long JSON with Chinese academic descriptions, it systematically produces unescaped double quotes (in paper titles, concept name references, etc.) causing JSON parse failures, with some outputs also truncated at the end

Solution: Added a dedicated JSON repair sub-step in the pipeline, submitting corrupted output to the LLM requesting only the repaired, clean JSON back; approximately 20 repair tasks were executed today with a high success rate.

Key Insight: Decoupling generation from formatting is a more reliable engineering strategy; Chinese quote conventions naturally conflict with JSON escaping rules — prompts should preemptively require " escaping, or introduce the jsonrepair library in post-processing, reducing extra API calls by 30–50%.

10. DiskCache calls mkdir() on every get() read, causing hot-path redundancy; discover_homepage_urls() still calls the LLM even when s2_homepage is already provided, wasting API calls

Solution: Added an ensure_dir parameter to DiskCache: ensure_dir=False during get(), only mkdir() during put(); when s2_homepage already has a value, add and return it directly, skipping the LLM call.

Key Insight: Read paths and write paths have different guarantee requirements — conflating them causes unnecessary system call overhead on hot paths; LLM calls should be a last resort, and short-circuit logic (early return) is the most effective pattern for reducing call frequency.

11. Conference award recognition step has extremely low recall: ~80% of batches return empty lists, nearly ineffective for specialized fields like power electronics, materials science, and marine geology; papers after 2023 cannot be confirmed

Solution: Maintained a conservative strategy (prefer omission over false positives), suggesting users consult official conference websites when results are empty; only 1 award was confirmed all day (GraphR HPCA 2018 Best Paper).

Key Insight: This step has very low ROI under current LLM capabilities and should be replaced with an external data source approach (maintaining a JSON file of top conference historical Best Paper lists, queried via exact title matching) rather than relying on LLM memory.

Human Thinking vs. AI Thinking

Strategic Level

Experimental Methodology Constraint Identification: Zero-shot Constraints + Normalization Hypothesis Correction

Role Thinking
Human User immediately pointed out that joint PCA, Procrustes alignment, and similar proposals violate zero-shot constraints; when the user speculated “maybe normalization wasn’t done right,” AI correctly explained that normalization cannot resolve coordinate system inconsistency and clarified the root cause through analogy.
AI AI’s initial proposals (joint PCA, Procrustes alignment, joint training) all required simultaneous access to both sections’ data — it failed to recognize the zero-shot constraint; however, AI gave the correct mathematical-level diagnosis on the normalization hypothesis.

Analysis: Users are more sensitive to experimental design constraints and can identify methodologically invalid proposals; AI has an advantage in diagnostic reasoning (correctly analyzing why normalization can’t fix coordinate system issues), but has blind spots in proactively checking constraint satisfaction. This correction directly redirected the research from alignment methods toward Foundation Models.

Error Recovery Core Architecture Designed Independently by Human

Role Thinking
Human Human independently designed the complete 5-group RBG grouping system (clustering 29 subtypes by motor primitive), the 6-task tiered strategy, the precise 329-demo allocation table per (task, subtype, division), the iterative validation strategy, and selected SpaceMouse as the teleoperation device. The full plan was ~2,000 words, reflecting deep understanding of robot learning data engineering.
AI AI implemented code based on the human plan: explored existing framework interfaces, designed data structures consistent with the framework, implemented 5 files module by module, wrote 34 unit tests, and updated configuration and documentation.

Analysis: Core design decisions (RBG grouping, demo allocation, 81% data efficiency savings) were entirely made by the human; AI handled interface adaptation and code implementation. The plan provided by the human directly determined the entire system’s data efficiency — this core insight AI could not have generated independently.

API Backend Preference and Domain Prior Knowledge (h-index Anomaly Identification)

Role Thinking
Human User expected claude_cli backend from the start (the project’s conventional configuration); after AI showed prominent professors with h-index=4/6, user immediately identified the data anomaly based on domain prior knowledge.
AI AI defaulted to the anthropic backend, only discovering the missing API key from error messages after three rounds of complete failure; when displaying the h-index list, AI did not proactively question the values.

Analysis: Users have domain common sense (top professors can’t possibly have such low h-indices) and project conventions (habitual use of claude_cli) — AI lacks automatic validation capability for both; “ask before doing” applies in both dimensions.

Proactive Name Collision Detection (AI Quality Check Beyond Task Boundaries)

Role Thinking
Human Human designed the structured analysis framework but did not explicitly ask AI to proactively detect name collisions in prompts; in some cases, human directly passed contaminated profiles into the pipeline without pre-screening.
AI AI proactively identified name collisions in multiple cases through multi-dimensional signals (h-index to paper count ratio, methodologically impossible field breadth, citation statistics contradictions), added explicit warnings and classifications to outputs, and even identified 3 independent trajectories in the Xiaoxiao Liu case.

Analysis: AI demonstrated proactive quality-checking capability beyond task boundaries — behavior not explicitly requested by the prompt but highly valuable; without this AI initiative, name collision contamination would have directly caused incorrect trajectory reports.

Research Profiler Analysis Depth Exceeds Information Extraction

Role Thinking
Human Human designed a structured JSON template (trajectory_summary, breakthroughs with why_not_before fields, etc.), intending to extract structured information.
AI AI demonstrated academic commentary-level understanding while filling the template: identifying Yuke Zhu’s core “infrastructure-oriented thinking” characteristic, Pieter Abbeel’s narrative arc from RL theory to embodied AI, and the deep technical prerequisites for DPO’s “why it couldn’t be done before” (requiring simultaneous deep understanding of RL objective functions and language model training dynamics).

Analysis: AI output quality exceeded information extraction, reaching academic commentary-level insight; this value was not in the prompt design but stems from AI’s depth of understanding of academic knowledge. The why_not_before field is the highest-value field in the entire analysis.

Implementation Level

Scope Definition and Use of Plan Mode

Role Thinking
Human User rejected ExitPlanMode tool calls multiple times, explicitly requesting direct execution rather than entering plan mode; in the CALVIN task, explicitly scoped the work to “just integrate the code, no need to check environment dependencies.”
AI AI tended to enter plan mode to organize the approach before executing (considering it safer); in the CALVIN task, also launched a Plan agent and background environment check commands, both interrupted by the user twice.

Analysis: For tasks with clear planning documents or well-defined scope, entering plan mode is redundant; AI’s over-planning tendency requires active user intervention to stay focused.

AI Limitations

Important Limitations

  • Environment configuration not pre-validated caused three rounds of total batch task failure: failed to account for Agent sub-agents lacking Bash permissions, conda non-interactive activation failures, missing PYTHONPATH, and API key type issues. Should validate with a single task before batch expansion, and should confirm the user’s API backend preference before starting.
  • Initial diagnostic conclusion was wrong and failed to proactively check experimental constraints: attributed cross-section failure to “gene features inherently weak” rather than incomparable coordinate systems (methodological error); when proposing joint PCA and similar approaches, failed to proactively check whether zero-shot constraints were satisfied — required user correction to redirect toward Foundation Models.
  • Lack of automatic domain common sense validation: when displaying obviously anomalous values like Pieter Abbeel h-index=4 or Sergey Levine h-index=6, did not proactively question them — required user to point out the S2 disambiguation systematic failure based on domain prior knowledge.
  • Conference award knowledge base coverage severely uneven: reasonably reliable for mainstream AI conferences like NeurIPS/CVPR/ICCV, but nearly ineffective for specialized fields (power electronics, materials science, marine geology, etc.); papers after 2023 return empty lists due to knowledge cutoff, causing ~80% of batches to produce no output with extremely low ROI.
  • When name collision contamination is severe (beyond threshold), AI still forces generation of a “primary researcher” analysis, potentially producing misleading content; the system should support outright refusal to analyze when contamination is too severe, requiring the user to provide a disambiguation hint (e.g., –author-id).

General Limitations

  • Ignoring user’s explicit scope-limiting instructions: when user said “no need to check environment dependencies,” AI still launched a Plan agent and background check commands; repeatedly attempted to enter plan mode for tasks with clear existing plans, rejected by the user each time.
  • Poor JSON format stability when LLM generates large text containing Chinese: systematic unescaped double quotes and tail truncation issues occur approximately once every 5–6 analysis tasks, requiring additional repair steps that increase pipeline complexity; also prone to syntax issues like incorrect import placement when generating large test files.

Today’s Takeaways

Core Takeaways

  • Per-section independent processing (PCA/training) produces incomparable embedding spaces — this is an architectural-level fundamental limitation of cross-sample retrieval in spatial transcriptomics, and cannot be fixed via normalization or post-processing. The only correct zero-shot solution is a pretrained Foundation Model that makes all samples share the same model weights and feature space.
  • In spatial transcriptomics cross-section tasks, Gene FM (scGPT) significantly outperforms Vision FM (UNI2, 100% vs 71% hit rate), because H&E images have cross-sample batch effects (staining differences, section thickness), while gene expression has stronger cross-sample consistency. This has broad implications for multimodal FM selection.
  • The Recovery Behavior Group (RBG) grouping strategy reduces human demonstration requirements from 1,740 to 329 (81% savings): grouping 29 error subtypes into 5 groups by motor primitive allows cross-subtype demo augmentation within groups, D0 demos can generate D1/D2 variants via perturbation, and Tier 1 task demos can transfer to Tier 2/3. This is a paradigm broadly applicable to robot recovery data engineering.
  • set_sim_state_flat() restores saved state from HDF5 by overriding all body pos/quat, masking XML model assembly errors; only env.reset() initializing from XML exposes CompositeBodyObject coordinate calculation bugs. Long-running GPU tasks should be submitted via Slurm rather than SSH nohup; saving scan results (–skip_scan) is an important engineering practice (avoiding repeated 1–4 hour scans).
  • The core contradiction in Semantic Scholar author disambiguation: academic output differences between namesakes are often orders of magnitude apart (h=4 vs h=164), so correct disambiguation requires quantitative metrics (significantly boosted paper count/h-index weights) to dominate over string similarity; an exact name match is actually a signal for the name collision trap. The three-level parsing chain (exact ID → paper reverse-lookup → name search) is a robust architectural pattern.
  • Three signals for academic database name collision detection: ① h-index to paper count ratio abnormally low (e.g., 140 papers but h-index=4); ② research fields that are methodologically impossible to coexist in one person; ③ citation statistics (last 5 years) contradicting the actual content year range of the paper list. These three signals can be embedded as automatic detection heuristics at the data collection layer, rather than relying on post-hoc identification at the analysis layer.
  • Correct runtime configuration for research_scout.py profile command on Windows (all three conditions required): PYTHONPATH= C:/Users/tongt/miniconda3/envs/AI/python.exe research/research_scout.py profile “Name” –api claude_cli. Claude Code Agent sub-agents don’t inherit the main session’s Bash permissions by default — long-running tasks involving Bash execution must be run directly from the main session with run_in_background.
  • The LLM JSON repair as an independent step (decoupling generation from formatting) has been validated as effective in practice: submitting corrupted output to the LLM specifically for repair achieves a much higher two-step success rate than requiring perfect single-step output. Root prevention approach: preemptively require " escaping in prompts, or introduce jsonrepair library in post-processing, reducing extra API calls by ~30–50%.
  • Conference award recognition has extremely low ROI under current LLM capabilities and should be replaced with an external data source approach (maintaining a JSON file of top conference historical Best Paper lists queried via exact title matching) rather than relying on LLM memory; only reasonably reliable for pre-2022 mainstream AI/ML/CV/Robotics conferences.
  • LLM calls should follow the “last resort” principle: when all low-cost information sources (cache, structured API return values) meet the need, skip the LLM via short-circuit logic. Cache system read and write paths have different guarantee requirements: read operations assume the resource already exists (no mkdir triggered), only write operations ensure the directory exists — conflating the two causes unnecessary system call overhead on hot paths.
  • The three-parallel code review framework (reuse/quality/efficiency as three independent concurrent agents reviewing the same diff) was effective in practice: the three dimensions found completely non-overlapping problem sets (missing import, duplicate SHA256 implementation, DiskCache hot-path redundancy), with parallel execution saving time; large-scale refactoring must be followed by systematic “downstream consumer follow-up checks,” including import completeness and duplicate functionality implementation — these issues don’t immediately surface as runtime errors.
  • LLM’s depth of understanding for academic trajectory analysis exceeded expectations: it spontaneously identified high-order features like “infrastructure-oriented researcher” and “technical prerequisites for paradigm shifts”; the why_not_before field (attributing the historical prerequisites for each breakthrough across data/compute/insight dimensions) is the highest-value field in researcher profiles and is suitable as a core feature of research_scout.

Practical Takeaways

  • tqdm displays correctly in tmux/nohup environments with PYTHONUNBUFFERED=1 + python -u flags; overlaying VLA model internal predictions (manip_progress) onto evaluation video frames is a low-cost, high-efficiency debugging approach (cv2.putText white text with black outline is clearly readable across backgrounds).

Session Summaries

MIHD Spatial Transcriptomics

✅ Full cross-section embedding diagnostic pipeline: 5-method comparison → scGPT confirmed optimal → visualization PDF generation → bilingual documentation 00:01:55.299 | claude_code Starting from Raw Shared HVG diagnostic results, user pointed out that joint methods violate zero-shot constraints and noted the current system is already a Foundation Model architecture, requesting tests of scGPT and UNI2. Extended the benchmark script to support two new embedding sources; after parallel runs, confirmed scGPT 14/14 hit rate (avg SL@50=0.416), while UNI2 achieved only 10/14 due to cross-sample H&E batch effects. Implemented visualize_cross_section_experiments.py to generate 5 sets of Letter-format PDFs (cover + 14 pages) and 35 per-layer sub-PDFs. After multiple format iterations (mixed English/Chinese → all Chinese → split into two documents), finally used PyMuPDF to convert per-layer PDFs to PNGs and embed them, creating separate English and Chinese diagnostic reports, confirming per-section independent training as the root cause.

Error Recovery Benchmark

🔄 VLA error recovery data collection end-to-end: plan design → pipeline implementation → CompositeBodyObject fix → Slurm batch generation 01:03:13.720 | claude_code User designed the complete RBG grouping plan (5 groups, 329-demo budget); AI implemented 8 new files on the robosuite/MimicGen framework (recovery_types/segmenter/collection augmentation conversion scripts), all 139 tests passing. Simultaneously fixed the CompositeBodyObject fallback bug (aligned with commit 398af01b), 13 coffee error skill videos successfully re-rendered after the fix. Added tqdm progress tracking and step-skipping options to the v5 pipeline; after an53 went offline → migrated to Slurm approach (first attempt failed without specifying partition, succeeded after adding –partition=ai), launched batch error scene generation across all tasks via tmux tzj + job 49363 on an46 A800 GPU, with pipeline running at the pick_place injection stage (14%|72/500).

VLA Auxiliary Tools

✅ manip_progress video overlay (cv2) + CALVIN RLDS→LeRobot format conversion script 03:02:15.000 | claude_code Implemented real-time manip_progress prediction overlay for VLA evaluation: traced the inference chain and made minimal modifications to pi0.py/policy.py/pi_model.py/deploy_policy.py four files, overlaying white text with black outline on each frame via cv2. After user explicitly requested “just integrate the code (no environment dependency checks),” following two interruptions of the Plan agent and background checks, directly read the two source files and wrote rlds_to_lerobot.py implementing RLDS→LeRobot format conversion.

Research Scout / Research Profiler Bulk Scholar Profile Analysis

✅ Bulk execution of 20+ multi-domain researcher academic trajectory analysis, JSON repair, and conference award recognition pipeline 02:44:44.000 | claude_code Intensive pipeline runs all day on TzJsDesktop, covering embodied AI (Yuke Zhu/infrastructure thinking, Pieter Abbeel/RL→embodied AI, Chelsea Finn/π0 VLA, Yunzhu Li/physical reasoning, Sergey Levine group: Eysenbach/Myers, etc.), CV (Ruoshi Liu/Zero-1-to-3, D’idac Surís/ViperGPT), power electronics (Haochen Shi/DAB converter), analytical chemistry (Fan Chen), marine geology (P. Yan), and 20+ more researchers. Produced ~20 complete profile JSONs with trajectory_summary/breakthroughs (why_not_before)/research_themes; performed ~20 JSON format repairs (Chinese quote escaping); conducted 10+ rounds of conference award recognition (confirmed MineDojo NeurIPS 2022 Outstanding Paper, RoboMimic CoRL 2021 Best Paper, DPO NeurIPS 2023 Outstanding Paper, Zero-1-to-3 ICCV 2023 Oral, Open X-Embodiment CoRL 2023 Best Paper, etc.); identified 5+ severe name collision cases including Xiaoxiao Liu (three independent trajectories), Yan Yang (140 heavily mixed papers), Yanyan Chen (thermoacoustics/quantum field theory mix), etc.

Gadget Research Profiler Code Quality Improvement

🔄 /simplify three-dimensional parallel review + two-round S2 disambiguation refactoring + Yiran Chen field validation 02:20:54.392 | claude_code Ran /simplify to conduct three parallel code reviews (reuse/quality/efficiency) on a 443KB diff from the common/ package refactoring, finding and fixing 6 issues (missing StudentCandidate import, duplicate SHA256 implementation, in-function import of math, DiskCache unnecessary mkdir, redundant homepage_urls LLM call, duplicate path resolution). Then implemented two refactoring rounds targeting systematic S2 disambiguation failures (prominent professors all with incorrect h-indices): v1 (retry logic + scoring disambiguation + _names_match fix); v2 (scoring weight recalibration to make quantitative metrics dominate strings, three-level parsing chain, get_author_by_id/search_paper_by_title/resolve_author_by_paper new functions, s2_author_id field, –paper/–author-id CLI parameters). Yiran Chen (Duke University, h=65) field validation: first run matched a medical namesake (h=10) due to S2 rate limiting, second run succeeded after finding the correct authorId via WebSearch, profile deployed to Hugo. v3 (paper title search support) planning complete, implementation deferred.

Research Profiler Batch Run Environment Debugging

🔄 12-researcher profiler batch run: three rounds of environment failures → 7 completed, insights report generated 02:37:49.375 | claude_code User requested re-running profiler on 10 previously researched robotics professors plus Duke University’s Yiran Chen and Hai Li, 12 total in parallel. Encountered four obstacles: Agent sub-agent no Bash permission → conda activate failed → common module missing → ANTHROPIC_API_KEY missing, rerunning all 12 tasks each time; after user explicitly specified –api claude_cli, adopted PYTHONPATH + direct Python absolute path + claude_cli approach, ultimately completing 7 (Xiaolong Wang 23.7, Ruoshi Liu 45.1, Pieter Abbeel 29.8, etc.), 5 killed. Same day user ran /insights command, analyzing 13 sessions to generate an HTML report revealing the user’s planning-oriented + bulk-operation + tolerant-of-partial-failure work style.

CalendarPro

🔄 Test suite stratification: 230 targeted tests pass, full suite hangs due to HuggingFace model download 00:24:51.000 | claude_code All 230 targeted tests (excluding semantic routing) pass; the full pytest suite including HuggingFace semantic routing model downloads was killed multiple times, issue unresolved. Recommended isolating heavy tests with pytest markers (@pytest.mark.slow) or mocking model downloads with monkeypatch.

Token Usage

Overview

Metric Value
Total Tokens 49,501,971
Input Tokens 39,621
Output Tokens 122,384
Cache Creation 4,038,982
Cache Read 45,300,984
Cache Hit Rate 91.8%
Total Cost (USD) $38.4677

Model Breakdown

Model Input Output Cache Creation Cache Read Cost Share
claude-opus-4-6 18,157 65,173 2,439,204 33,365,164 $33.7147 87.6%
claude-haiku-4-5-20251001 21,363 54,114 1,167,476 11,234,327 $2.8747 7.5%
claude-sonnet-4-6 101 3,097 432,302 701,493 $1.8783 4.9%

Usage by Machine

Machine Total Tokens Input Output Cost
DCC 1,074,928 1,267 4,499 $1.4459
tianhe 44,299,011 38,093 110,679 $32.3961
TzJsDesktop 4,128,032 261 7,206 $4.6258