Daily Log — 2026-03-21

Today’s Overview

• What was done: Two major projects progressed in parallel: error_recovery_benchmark completed a full cycle from single-point parameter fixes to architectural trajectory segmentation refactoring (including pipeline contract alignment and large-scale training scene generation); gadget fixed critical bugs in cross-device data sync and daily report generation.
• How it was done: On tianhe, through systematic code review (3 parallel agents + log aggregation statistics), phased fixes (parameter adjustment → architectural refactoring), and large-scale validation via Slurm parallel jobs; on TzJsDesktop, by precisely locating and fixing the npx hang bug, and using sed to batch-upgrade all ECC agent model configurations.
• Why it matters: Benchmark training data coverage improved from 88/174 subtypes to 130+/160 subtypes, with threading task subtypes increasing 8x; the gadget daily report pipeline is restored, and ECC inference capability is now at maximum (opus + effortLevel: max).

DCC

• What was done: Force-synchronized the local main branch with GitHub origin/main after a historical divergence.
• How it was done: Confirmed no unique local code via git fetch + git diff --stat, then executed git reset --hard origin/main.
• Why it matters: Codebase is now in sync with GitHub, making new features like rclone sync.py available on DCC.

TzJsDesktop

• What was done: Fixed git merge conflicts in the gadget project, configured rclone data sync, fixed the npx subprocess hang bug, improved documentation, completed a global ECC agent model upgrade, and reviewed new vs. old opportunity scan comparisons — discovering an unexpected regression in three_piece_assembly.
• How it was done: git reset --hard to align with remote, Path.as_posix() to fix the Windows path bug, npx --yes to skip interactive prompts, sed for batch agent config replacement; ran the opportunity scanner to get fresh scan data and compared task-by-task changes.
• Why it matters: Gadget data sync restored, daily report generation pipeline no longer hangs, ECC upgraded to opus + max thinking across the board; pinpointed the three_piece_assembly -19 regression and initiated follow-up investigation.

tianhe

• What was done: Completed the full-stack repair of error_recovery_benchmark: code review identified root causes (phase detection failure, objects[0] ambiguity) → parameter fixes (7 error skill types) → architectural refactoring (trajectory segmentation + contract alignment Steps 1–7) → large-scale training scene generation (1,222 scenes) → opportunity map rescan (threading 3→25) → root cause analysis of three_piece_assembly regression.
• How it was done: Parallel multi-agent code review, bash log statistics, Python data validation, file-by-file modifications (7 error skill files + 5 framework files), sbatch Slurm parallel jobs (96 workers).
• Why it matters: Training scene success rate improved from ~55% to 81% (130/160 subtypes achieving 10/10); threading showed the most significant improvement; the root cause of the three_piece_assembly regression has been identified and a fix is ready.

Completed full-stack repairs on error_recovery_benchmark in the tianhe HPC cluster — from parameter tuning to architectural trajectory segmentation refactoring (1,222 training scenes, threading subtypes increased from 3 to 25) — while fixing critical gadget infrastructure bugs on TzJsDesktop and upgrading the entire ECC toolchain to opus + max thinking.

Today’s Tasks

Architecture & Strategy

• ✅ Systematic Contract Fix Design (Trajectory Segmentation Architecture) — Identified contract inconsistencies across the entire detector→injector→validator→generator pipeline; established trajectory segmentation by object interaction (InteractionSegmenter) as the core fix direction, with each segment having a clearly defined current target object to entirely bypass phase detection defects. Plan file created and updated, providing full context for Steps 1–7 implementation.
• ✅ Comprehensive Code Review of 13 Error Skills and Root Cause Investigation of Training Data Generation — Used 3 parallel agents to perform a complete code review of can_inject/inject/validate across all error skills, uncovering 5 categories of implementation bugs; aggregated 42 parallel log files to count failure causes (gripper not closed: 1,698 times; insufficient displacement: 1,390 times; etc.); used bash to analyze the phase distribution in the opportunity map, discovering that all 372 threading opportunities were labeled as pre_reach with pick_place entirely missing reach/grasp phases — confirming phase detection failure as the core root cause.
• ✅ Systematic Error Skill Parameter Fixes (Fix A–G + Fix 5–6) — Applied 24 parameter changes across 7 error skill types (offset lower bounds, step counts, velocity directions, validation thresholds); fixed get_target_object() to prioritize searching within graspable objects (Fix 5); restricted InteractionSegmenter to the graspable candidate set (Fix 6); all 139 unit tests pass.
• ✅ Trajectory Segmentation + Contract Alignment Implementation (Steps 1–7 + 6 Missed Fixes) — Added InteractionSegment dataclass and InteractionSegmenter; updated CleanTrajectory to support segment serialization; updated OpportunityScanner to retrieve target_object/phase/other_objects from segments; fixed objects[0] semantics across 13 skills (e01–e13); fixed e02 gripper direction and e11 freeze_steps hardcoding; added target_pos to trajectory_context; fixed e12/e10 config key drift; fixed context_replay.py to use error_spec.target; fixed EnvWrapper._sim_body_name2id substring fallback; all 139 unit tests pass.
• 🔄 three_piece_assembly Scan Regression Root Cause Analysis (23→4 subtypes) — By analyzing NPZ files (missing interaction_segments_json), scan logs (879/887 frames labeled pre_reach), and the collect script (never calls segment_interactions()), identified 4 root causes: collect script not integrated with the segmenter, replay_and_label_phases() not passing target_object (causing immovable base fixture to be used for phase detection), scanner fallback overwriting bad labels, and missing defensive logic. Fix plan drafted; code implementation pending.
• ✅ gadget: Fixed npx Subprocess Permanent Hang Bug — npx calls in fetch_codex_usage_full() and fetch_ccusage_full() lacked --yes, causing them to wait indefinitely for install confirmation in capture_output=True mode; adding --yes to all 3 npx calls resolved the issue and restored the daily report generation pipeline.
• ✅ gadget: ECC Full Upgrade to opus + max thinking — Thoroughly reviewed ECC components (5 core agents, 4 skills, hooks system); used sed to batch-upgrade 27 agents from sonnet/haiku to opus (doc-updater kept at sonnet); changed settings.json effortLevel to max (a new feature the user proactively informed me of).
• ✅ Large-Scale Parallel Training Scene Generation (Multiple Slurm Jobs) — Three rounds of scene generation: Round 1 (after Fix A–G): 96 workers, 1,209 scenes (158/160 subtype success); Round 2 (after Fix 5–6): 32 workers, 1,159 scenes (107/160 achieving 10/10); Round 3 (after Steps 1–7): 96 workers on node an53, 1,222 scenes in 32 min 49 sec (130/160 subtypes satisfied).
• ✅ Full 6-Task collect + scan Validation and Opportunity Map Rescan — Re-ran collect+scan for all 6 tasks multiple times; final scan results: threading 3→25 (+22), pick_place 21→23 (+2), three_piece_assembly 23→4 (−19), total 130→135 (+5); validated the significant effect of the target_pos fix on threading while discovering the unexpected three_piece_assembly regression.
• ✅ gadget: Added Codex Usage Aggregation Support to monthly_summary.py — Added functions including aggregate_token_usage and combine_usage_summaries; independently tracks codex_token_usage in monthly summaries and generates combined_token_usage_summary; README documentation updated accordingly.

Implementation & Fixes

• ✅ gadget: Git Sync and rclone Data Configuration — Fixed git merge conflicts on DCC and TzJsDesktop (git reset --hard origin/main to align with remote); created ~/.config/gadget/sync.json; fixed the Windows backslash bug in sync.py (Path.as_posix()); successfully pulled all personal data from Google Drive.
• ✅ CLAUDE.md / AGENTS.md Review and Update (ErrorRecoveryBenchmark) — Ran /init to review and update CLAUDE.md, adding missing Makefile targets (v5-training, v5-mass-gen, etc.), training scripts, and 6 undocumented pipeline scripts; generated a 358-word AGENTS.md contributor guide via Codex covering project structure, build/test commands, code style, and PR standards.

Problems and Solutions

Critical Issues

1. Systematic phase detection failure across multi-object tasks (threading/pick_place, etc.): all threading frames labeled pre_reach, pick_place missing reach/grasp phases, causing 12/13 skills to find no opportunities

Solution: Root cause: phase detection relies on the distance between a single object and the EEF, which fundamentally fails with unusual geometry (needle shape) or multi-object scenarios. Core fix: added InteractionSegmenter to segment trajectories by object interaction (EEF proximity + gripper state + co-motion detection), giving each segment a clearly defined target_object and phase, entirely bypassing phase detection.

Key Insight: The single-object assumption in phase detection is an architectural design flaw that cannot be fixed by tuning parameters; trajectory segmentation is a more fundamental solution that circumvents the flawed abstraction rather than patching it.

2. Pipeline-wide target_object ambiguity in v5: OpportunityScanner/skill.inject()/validate()/ContextReplayEngine all default to objects[0], systematically selecting the wrong operation target in multi-object scenarios; missing target_pos in trajectory_context renders drop-type skill filtering conditions ineffective

Solution: Steps 1–7 propagate target_object and target_pos through the entire pipeline; fixed objects[0] semantics in 13 skills; wrote target_pos to trajectory_context after construction; context_replay.py uses error_spec.target.

Key Insight: In multi-object tasks, objects[0] is the first object by dictionary insertion order — semantically incorrect but raises no errors — causing all object-state-based judgments (displacement/gripper/pose) to be systematically wrong.

3. Systematic mismatch between error skill injection parameters and validation thresholds, causing 41+ subtypes to generate 0 scenes (5 root causes: insufficient displacement, empty gripper, object won’t release, insufficient collision force, etc.)

Solution: Categorized 5 root causes and made targeted fixes across 7 files with 24 parameter changes (lowered validation thresholds, increased step counts, adjusted velocity directions); drop-type skills now apply a slight initial velocity (Z=−0.15 m/s) to assist object release.

Key Insight: Injection parameter lower bounds must be calibrated against validation thresholds: when lowering the injection lower bound, the validation threshold must be lowered simultaneously; drop-type errors cannot rely solely on gravity and need an active initial velocity to overcome friction.

4. The collect script never calls segment_interactions(), so NPZ files lack segmentation data; in three_piece_assembly, InteractionSegmenter and get_target_object() incorrectly select the base fixture as the target, causing scan results to regress from 23 to 4 subtypes

Solution: Fix 5–6 restricts target search to graspable objects (those with grasp_geoms configured); collect script needs to call segment_interactions(); replay_and_label_phases() needs to pass target_object; scanner needs to fix label override timing. (Fixture part fixed; collect integration pending.)

Key Insight: The segmenter was implemented but never integrated into the data collection pipeline; contract alignment must track storage-layer persistence — passing unit tests does not mean E2E correctness.

5. python summarize/daily_summary.py permanently hangs at the @ccusage/codex step with no output and no errors

Solution: subprocess.run(capture_output=True) redirects stdin to DEVNULL; npx’s first-run install confirmation prompt waits indefinitely; adding --yes to all 3 npx calls skips the interactive confirmation.

Key Insight: When calling any CLI tool that may have interactive prompts in capture_output=True mode, --yes/-y or equivalent flags must be explicitly passed; otherwise the process hangs indefinitely rather than timing out.

6. Multiple HPC obstacles: 32+ workers simultaneously forking and importing heavy libraries on a shared login node caused IO contention crashes; the ai partition forces gpu:a800:1 even for purely CPU tasks; sbatch –wrap uses /bin/sh which doesn’t support source/conda; MUJOCO_GL=osmesa fails to initialize on some nodes

Solution: Submitted a Slurm exclusive-node job (96 cores); accepted 1 GPU but ran with MUJOCO_GL=disabled (physics simulation needs no GL); wrapped sbatch commands in bash -c, replaced source with . for conda init.

Key Insight: MuJoCo physics simulation (enable_camera=False) is a purely CPU task; sbatch –wrap defaults to /bin/sh, not the user’s login shell; worker count should exactly match the requested –cpus-per-task.

General Issues

7. In e02 drop_in_transit, the settle phase has action[-1]=-1.0 commented ‘Open gripper’ but actually closes the gripper, causing an already-dropped object to be re-grasped and the drop to fail; in e11, validate() threshold uses min_progress * 30 hardcoded while inject() reads freeze_steps from config, making the evaluation criteria inconsistent

Solution: Changed e02 to action[-1]=1.0 (in robosuite OSC, 1.0 = open); added freeze_steps = self.config.get('freeze_steps', 30) in e11 to unify the source.

Key Insight: robosuite gripper action sign is counter-intuitive and opposite to comments (−1=close, 1=open); in logs this appears as drop metrics of 0, easily misinterpreted as a physical execution failure rather than a control command error.

8. YAML config keys don’t match the keys read in Python code (lateral_offset vs. lateral_offset_range; rotation_offset vs. rotation_range; reverse_steps vs. regression_range), causing affected skills to always use hardcoded defaults

Solution: Added dual-key fallback (check new name first, then old name); reverse_steps handling changed from a fixed value to range sampling.

Key Insight: Config key evolution was not synchronized with the code; the getdefault mechanism masked the error — the config was silently ignored with no crash, making this a highly insidious bug.

9. git pull triggered merge conflicts in 13 files; AI defaulted to the “preserve both sides” flow, causing many wasted steps; sync.py on Windows generated backslash-based rclone remote paths, causing sync failures

Solution: User indicated remote should take precedence; executed git reset --hard origin/main to align directly; changed str(Path(remote_rel).parent) to Path(remote_rel).parent.as_posix().

Key Insight: Conflict resolution strategy depends on business intent; AI should ask before acting rather than defaulting to a complex merge path; Windows pathlib.Path must explicitly call .as_posix() when passing paths to external commands.

Human Thinking vs. AI Thinking

Strategic Level

Problem Classification: Systemic Contract Inconsistency vs. Isolated Point Bugs

Role	Approach
Human	Explicitly identified that generation failures were not a matter of tuning thresholds in individual skills, but rather contract inconsistencies across the entire detector→injector→validator→generator pipeline, breaking it down into three levels: detectors being missed, validators never passing, and generators stopping early after consecutive failures.
AI	Found multiple isolated code bugs and phase detection failure symptoms; tended toward fixing them one by one, lacking a systemic view of the full pipeline.

Analysis: The human defined the nature of the problem from an architectural perspective, directly changing the direction of the entire fix strategy; AI is better at quickly pinpointing specific bugs locally but lacks a holistic view of cross-module data flow.

Core Fix Direction: Trajectory Segmentation (Bypass phase detection) vs. Patching phase detection

Role	Approach
Human	Proposed a fundamental architectural solution — segment trajectories by object interaction, with each segment having a clearly defined current target object and gripper detection based on co-position of object and EEF — entirely bypassing the phase detection flaw.
AI	Only identified the symptoms of phase detection failure and proposed fixing the single-object assumption in get_task_completion_stages(), remaining at the patch level.

Analysis: The human proposed a more elegant architectural solution — bypassing rather than patching; AI’s thinking was stuck patching at the wrong level of abstraction, which would have led to many ad hoc workarounds for special cases.

Identifying Systemic Gaps in Local Fixes (6 Uncovered Issues)

Role	Approach
Human	After AI completed the 7-step plan, a systematic review identified: target_pos not being passed, fallback phase logic not fixed, config drift only half-fixed, e05/e06/e09 still using objects[0], error_spec.target not used, three_piece_assembly body-name warnings.
AI	Completed the 7-step plan and validated with unit tests (139 passing), but did not systematically verify all skills and call sites; lacked initiative in reviewing the overall data flow of modified modules.

Analysis: The human reviewed the entire system from a data contract perspective rather than file by file; AI tends to consider tasks complete after executing the plan, lacking the initiative for a global post-hoc review.

Validating Basic Assumptions About Task Object Counts

Role	Approach
Human	Quickly corrected AI’s basic assumption errors about pick_place (1 → 4 objects) and threading (no grasping → must grasp needle).
AI	Made inferences about task structure from code without first reading the task configuration YAML, causing parts of the analysis to be built on incorrect premises.

Analysis: The human identified basic assumption errors from prior knowledge; AI should first read config files to verify assumptions before beginning analysis — “config files are the map, code is the terrain; read the map first.”

GPU Resource Requirements and Worker Count Configuration

Role	Approach
Human	Pointed out that scene generation is purely CPU-based with no GPU needed; repeatedly corrected AI over-requesting GPUs (8→1); clarified that worker count should exactly match the requested CPU core count (rather than AI’s conservative estimate of 8–16).
AI	Defaulted to assuming MuJoCo simulation requires GPU rendering without first checking the enable_camera parameter; tended to maximize resource allocation without aligning to actual node core counts.

Analysis: The human assessed resource needs from the nature of the task; AI relied on surface-level assumptions and did not read the code to verify before acting — these errors led to multiple wasted Slurm job submissions.

ECC Model Selection Strategy and Awareness of New Features

Role	Approach
Human	Required “the latest opus 4.6 with max thinking for all components” and proactively informed AI that a new effortLevel: max option existed.
AI	Initially recommended a balanced cost approach of “opus for core + sonnet for others”; incorrectly told the user that high was the highest effortLevel.

Analysis: The user prioritized maximizing capability over cost; the user knew about new features that AI was unaware of — a classic case of information asymmetry; AI should proactively declare that its knowledge of rapidly evolving tools may be outdated.

npx Hang Root Cause Diagnosis (AI Systematic Investigation vs. Human Symptom Description)

Role	Approach
Human	Provided accurate symptoms (hung at @ccusage/codex step with no output) but was unclear on the specific root cause.
AI	Systematically eliminated hypotheses: checked data volume → tested command directly → pinpointed capture_output + missing –yes as root cause; the entire diagnostic chain was clear and efficient.

Analysis: AI’s systematic diagnostic process was more efficient than intuitive guessing; the human provided precise symptoms while AI executed the inference chain — one of the few scenarios where AI clearly outperforms intuition.

Alertness to Subtype Count Anomalies and Precise Statistics

Role	Approach
Human	Self-counted 48 failing subtypes (listing file paths, line numbers, and log examples for each of 5 root cause categories); noticed that 130 subtypes was insufficient (theoretical 174) and proactively asked follow-up questions to drive a rescan.
AI	Classification framework was roughly correct but too coarse; did not accurately count the 48 number; when reporting 130 subtypes, did not proactively flag the gap against the expected 174.

Analysis: The human had prior expectations about results and noticed when they didn’t match actuals; AI only reported observed results without proactive sanity-checking.

AI Limitations

Key Limitations

• When facing complex systemic problems, AI tends to locate isolated code bugs and lacks the ability to analyze contract inconsistencies across the full data flow (detector→injector→validator→generator); it requires human guidance at the architectural level to elevate its analytical perspective.
• After completing local fix plans, AI lacks initiative for global data flow validation: multiple gaps — including three skills (e05/e06/e09), missing target_pos propagation, and the collect script not integrating segment_interactions() — all required human systematic review to discover; passing unit tests created a false sense of completion.
• AI makes inferences about task structure without first reading task configuration files (task YAML), causing analyses to be built on incorrect premises (pick_place object count, whether threading involves grasping); configuration files should always be read first to validate basic assumptions before beginning analysis.
• AI assumed GPU was needed without first checking the enable_camera parameter; despite repeated corrections, Slurm scripts continued to request too many GPUs; AI inaccurately assesses runtime resource requirements and tends to forget user preferences set in earlier turns of a multi-round conversation.
• In git merge conflict scenarios, AI defaults to the complex “preserve both sides, resolve file by file” workflow without first asking for user intent (discard local vs. keep local), resulting in much wasted effort before being corrected.
• The three_piece_assembly regression (23→4 subtypes) was presented as a “normal result” in AI’s report without proactive flagging or analysis; when analyzing failures, AI also failed to proactively enumerate all files of the same type (e05/e06/e09 were missed) until the human explicitly listed them.
• AI is unaware of the latest Claude Code features (effortLevel: max) and incorrectly told the user that high was the highest level; AI has a knowledge lag on rapidly evolving tool ecosystems and should proactively declare that newer features may be unknown to it.

Today’s Takeaways

Core Takeaways

• Pipeline contract alignment must track the complete data flow: it’s not enough to fix the processing logic in code — you must also ensure data is persisted at the storage layer (e.g., segment_interactions() results written to NPZ); otherwise data is lost on reload and fixes have no effect on downstream processes. Passing unit tests ≠ E2E correctness.
• In multi-object robot manipulation tasks, target_object must be treated as a first-class citizen propagated through all three stages of detector/injector/validator; objects[0] is the first object by dictionary insertion order, semantically wrong in multi-object scenarios and will not raise an error, causing all object-state-based judgments to be systematically incorrect.
• Before analyzing a complex system problem, all relevant configuration files must be read to validate basic assumptions (e.g., how many objects a task has, what type of task it is) — “config files are the map, code is the terrain; read the map first.”
• After systematic fixes, a before/after comparison across all tasks must be performed; you cannot assume there are no side effects just because unit tests pass. The simultaneous threading +22 and three_piece_assembly −19 results prove exactly this.
• Error skill injection parameter lower bounds must be adjusted in tandem with validation thresholds: when lowering the injection lower bound, validation thresholds (min_displacement, min_rotation_deviation, etc.) must be lowered simultaneously; otherwise injections still fail validation. Drop-type errors cannot rely solely on gravity and need a slight initial downward velocity (Z=−0.15 m/s) to overcome friction and assist release.
• subprocess.run(capture_output=True) redirects stdin to DEVNULL; when calling any CLI tool that may have interactive prompts (such as npx), --yes/-y or equivalent flags must be explicitly passed; otherwise the process hangs indefinitely rather than timing out.
• Log aggregation statistics (Counter) are far more effective than reading individual log lines for identifying systemic bugs: 1,698 instances of “gripper not closed” pointed directly to target_object ambiguity, not actual gripper execution problems; data-driven root cause analysis is superior to subjective guessing.
• MuJoCo physics simulation (enable_camera=False) is a purely CPU task; MUJOCO_GL=disabled completely bypasses OpenGL. HPC shared nodes are not suitable for large-scale parallel subprocesses — use exclusive Slurm nodes with worker count exactly matching –cpus-per-task; sbatch –wrap defaults to /bin/sh, so commands must be explicitly wrapped in bash -c.
• Graspable objects and fixtures must be distinguished in multi-object MuJoCo tasks; the grasp_geoms field in task_config serves as a filter criterion to avoid misidentifying target when the EEF trajectory passes above a fixture. other_objects should still include fixtures for collision detection — only filter them out when searching for the target.
• robosuite OSC controller gripper action sign is counter-intuitive and opposite to comments (−1.0=close, 1.0=open); in logs this appears as drop-related metrics of 0, easily misinterpreted as physical execution failure rather than a control command error. robosuite MuJoCo body naming has mapping inconsistencies with task configs, requiring fuzzy-match fallback.
• ECC v1.9.0 added effortLevel: max; combined with full opus models, this brings AI inference capability to its maximum configuration — a one-time infrastructure investment. Windows pathlib.Path must use .as_posix() when passing paths to external commands (rclone, git, etc.).

Session Summaries

ErrorRecoveryBenchmark

✅ Code Review & Root Cause Investigation: CLAUDE.md update, comprehensive review of 13 skills, phase detection failure discovery, systemic contract issues established 02:19:30.892 | claude_code + codex Reviewed and updated CLAUDE.md via /init (adding Makefile targets, training scripts, and undocumented pipeline scripts); performed a comprehensive code review of 13 error skills using 3 parallel agents, uncovering 5 bug categories (e02 gripper direction reversed, e7 empty dict, e11 hardcoded thresholds, YAML key mismatches, etc.); used bash to analyze phase distribution in the opportunity map, finding all 195 threading frames labeled as pre_reach — confirming phase detection failure as the core root cause; human identified the systemic nature of the problem (contract inconsistency across the full pipeline) and established “trajectory segmentation” as the fundamental fix direction; Codex concurrently generated a 358-word AGENTS.md contributor guide.

🔄 Architectural Fix Implementation: Steps 1–7 Trajectory Segmentation + Contract Alignment + 6 Missed Fixes + Large-Scale Scene Generation + Scan Validation and Regression Analysis 03:46:21.126 | claude_code Systematically implemented the 7-step plan: added InteractionSegment/InteractionSegmenter, updated CleanTrajectory for segment serialization, updated OpportunityScanner and ContextReplayEngine to propagate target_object, fixed objects[0] semantics in 7 skills and 2 explicit bugs; human review identified 6 missed fixes (target_pos, fallback phase, config drift, e05/e06/e09 semantics, error_spec.target, body-name fallback); sbatch job 50015 generated 1,222 scenes on node an53 in 32 min 49 sec; rescan results: threading 3→25 (+22), three_piece_assembly 23→4 (−19); NPZ analysis and collect script review identified the regression root cause (collect never called segment_interactions()); fix plan drafted, awaiting implementation.

✅ Parameter Fixes and Initial Scene Generation: Fix A–G (7 error skill types) + Fix 5–6 (graspable filtering) + Parallel Scene Generation + 48-Subtype Root Cause Analysis 17:31:04.519 | claude_code Implemented Fix A–G: 24 parameter changes across 7 files; 96-worker Slurm job generated 1,209 scenes (158/160 subtype success); implemented Fix 5–6: restricted graspable candidate set to fix three_piece_assembly target misidentification; re-ran collect+scan validation for all 6 tasks; 32-worker job generated 1,159 scenes (107/160 achieving 10/10); user provided precise 48-subtype failure classification (with file paths and line numbers); AI read 7 skill source files and confirmed 5 systemic root causes, documenting the fix plan.

gadget

✅ Infrastructure Fixes and Toolchain Upgrade: git sync, rclone config, npx bug fix, ECC full opus upgrade, Codex monthly aggregation support 02:35:05.863 | claude_code + codex Fixed git merge conflicts on DCC and TzJsDesktop (git reset –hard origin/main to align with remote, avoiding the complex merge path AI would have defaulted to); created rclone sync configuration, fixed the Windows backslash bug (Path.as_posix()), and successfully pulled all personal data; precisely identified the root cause of daily_summary.py hanging at the @ccusage/codex step (npx missing –yes, waiting indefinitely in capture_output mode), added –yes to 3 locations to fix; after thoroughly reviewing ECC components, batch-upgraded 27 agents to opus (doc-updater kept at sonnet) and set effortLevel to max (new feature the user proactively informed me of) using sed; added Codex usage aggregation functions to monthly_summary.py via Codex, updated README documentation; supplemented undocumented CLI parameters in research/CLAUDE.md.

Token Usage

Claude Code

Overview

Metric	Value
Total Tokens	39,706,094
Input Tokens	47,162
Output Tokens	108,045
Cache Creation	2,303,350
Cache Read	37,247,537
Cache Hit Rate	94.2%
Total Cost (USD)	$26.1748

Model Breakdown

Model	Input	Output	Cache Creation	Cache Read	Cost	Share
claude-opus-4-6	21,301	43,156	1,138,906	29,102,427	$22.9125	87.5%
claude-haiku-4-5-20251001	23,791	58,982	963,158	7,767,682	$2.2994	8.8%
claude-sonnet-4-6	2,070	5,907	201,286	377,428	$0.9629	3.7%

Usage by Device

Device	Total Tokens	Input	Output	Cost
tianhe	32,492,898	43,924	77,886	$22.1018
TzJsDesktop	7,213,196	3,238	30,159	$4.0730

Codex

Overview

Metric	Value
Total Tokens	9,917,937
Input Tokens	9,827,688
Output Tokens	90,249
Reasoning Tokens	43,171
Cache Read	8,714,880
Total Cost (USD)	$6.3145

Model Breakdown

Model	Input	Output	Reasoning	Cache Read	Cost	Share
gpt-5.4	9,827,688	90,249	43,171	8,714,880	$6.3145	100.0%