Daily Report — 2026-09-24

Daily Overview

• What was done: The work involved implementing complex architectural changes for resource management (TokenMonitor), refining robotics control rules (I-166), conducting deep diagnostic analysis of training failures (RoboMemory), and performing system maintenance and optimization across MIHD and Minecraft server environments.
• How it was done: Activities were conducted through a mix of local command testing, remote evaluation runs, automated data processing/video analysis, code probes, and cost-benefit calculations using web-based tools.
• Impact: These efforts improved system stability through better resource scheduling (reducing CPU spikes), enhanced robotics policy accuracy via prerequisite latching, provided critical diagnostic insights into why low loss fails to ensure success, and restored test suite integrity.

The day spanned diverse activities including implementing prerequisite latching rules for robotics, developing a serial refresh architecture for resource optimization, diagnosing training loss discrepancies in robotic tasks, and resolving test regressions in the MIHD repository.

Tasks

Architecture & Strategy

• ❌ I-166 (Prerequisite rule implementation & closed-loop re-runs) — Complete closed-loop runs for framesamp (all 14 tasks) and seg_sum (affected tasks) with the new prerequisite rule (PRE_K=3) to prevent early memory marks.
• 🔄 Serial Refresh Implementation (S1-S10) — A multi-phase workflow to remove refresh waste, implement a FIFO compute gate, freeze values between samples, and deploy a new single-owner scheduler.
• ✅ Implement I-164 (Per-task loss evaluation) — Develop a workflow to recompute flow-matching loss per task and per phase to diagnose specific failures.
• ✅ Plan Budget Estimation (I-074) — Implement logic to estimate the dollar budget of a plan based on spent amount divided by utilization percentage, including per-model accuracy and historical backfilling for Codex.
• 🔄 Implement I-163 (Context evaluation) — Implement new context window variants (1/3/5 frames) and structured segmentation summaries for Gemini evaluation.
• 🔄 Draft SPEC-2 for trainable parameter counting — Create a specification to automate the counting of trainable and total parameters across various fusion modules and references.

Implementation & Fixes

• 🔄 Full-scale I-163 execution — Wait for human decision on scale (a: 5 tasks/10 eps vs b: 4 tasks/5 eps).
• ✅ Fix golden fusion-gen dual-write mismatch — Update tests/test_golden_fusion_gen.py to reflect the intentional gt/gt_labels dual-write introduced in commit eb3f29d.
• ✅ Analyze failure videos — Review StopCube and InsertPeg failure videos to identify behavioral patterns.
• ✅ Minecraft server resource optimization — Analyze CPU/RAM usage of the current m8a.xlarge instance and evaluate cheaper migration options (r6a.large, t3a.large, etc.).
• ✅ Command usage testing — Repeatedly invoked the /usage command to check for stability or specific output patterns.

Problems & Solutions

Critical Issues

1. Early memory marks in ButtonUnmask tasks causing policy to skip subgoals.

Solution: Added a ‘prerequisite_done’ latch in writer_probe/gemini_v2_env that requires 3 consecutive ‘done’ signals after the first step is sensed visually/physically before enabling marks.

2. StopCube has low training loss but low success rate.

Solution: Recomputed phase-specific loss and analyzed error distribution, discovering that loss is dominated by ‘waiting’ phases and misses the critical, short-duration ‘pressing’ timing error.

3. Refreshing all data at once caused CPU spikes and UI stuttering.

Solution: Designed a scheduler that spreads independent jobs (archiving, pricing, budgeting) evenly across the refresh interval and uses a single atomic publish.

4. API quota exceeded on Duke LiteLLM proxy.

Solution: Awaiting quota reset or new key injection by user.

5. The initial budget estimation was too imprecise due to the fixed ±20% error margin.

Solution: Implemented a ‘stretch-based’ calibration method that uses discrete segments where one model is dominant to calculate the true cost per percentage point.

6. InsertPeg failure mode is unclear.

Solution: Analyzed loss and video to determine it is a contact-precision problem rather than a memory or learning issue.

7. Codex limit estimation lacked historical data for new installations.

Solution: Built a specialized log parser to backfill plan budget data directly from existing Codex session JSONL files.

General Issues

8. Golden test ’test_golden_fusion_gen.py’ failing due to an assertion mismatch regarding output arrays (expecting ‘gt’ only, while pipeline now writes ‘gt’ and ‘gt_labels’).

Solution: Confirmed via git log (eb3f29d) that the dual-write was an intentional architectural decision to unify schemas; updated the test to assert both keys and verify their equality.

9. Minecraft server experienced lag (TPS drop) during player chunk generation on the new r6a.large instance.

Solution: Identified the cause as the limitation of having only one physical core (2 hyperthreads) during high-load chunk generation; recommended reducing view distance or using pre-generation tools.

10. AI attempted to use shell redirection/piping which was blocked by the system guard.

Solution: Switched to using the Edit tool for writing files and removed redirection in bash commands to comply with security constraints.

11. Intermittent tunnel failure during long-running remote tasks.

Solution: Integrated automatic tunnel reconnection logic and monitored session health via remote sleep/ssh.

Human vs AI Approaches

Strategic Level

Loss as a metric for success

Role	Approach
Human	Questioned why low loss does not translate to high success rates.
AI	Calculated loss concentration (top 5% share) and phase-specific statistics to prove that average loss masks critical timing errors.

Difference Analysis: The human provided the critical intuition/question (why the discrepancy exists), while the AI provided the mathematical derivation and diagnostic framework.

Refresh Scheduling

Role	Approach
Human	N/A
AI	Proposed various scheduling fixes and individual job optimizations.

Difference Analysis: The human dictated the core architectural requirement: tasks must be spread out over the interval to prevent spikes, and the UI should only refresh when the calculated data is ready, showing cached values in the interim.

Budget Estimation Methodology

Role	Approach
Human	N/A
AI	Initially proposed a simple spend/utilization ratio with a static ±20% error margin.

Difference Analysis: The human suggested refining the estimation to be more dynamic and specific to the window units (5h, week, month), forcing a move toward a more robust historical sampling approach (stretches).

Implementation Level

Codebase debugging (Stale assertions)

Role	Approach
Human	Provided the initial context of the failure and pointed to the specific commit (eb3f29d) that caused the drift.
AI	Systematically probed the codebase to find ‘hidden’ failures that were masked by the initial crash, discovering a second stale assertion at line 432.

Difference Analysis: The AI’s ability to run individual test functions in a loop allowed it to find errors the user wasn’t yet aware of.

Decision on PickHighlight truth

Role	Approach
Human	N/A
AI	Implement prerequisite rule consistent with 80k ckpt (don’t draw before button is pressed).

Difference Analysis: User needs to confirm if this conflicts with the 9/23 new truth definition.

AI Limitations

Critical Limitations

• AI failed to account for the fact that different models impact the total limit at different rates (e.g., cache hits vs. full input), leading to early imprecise estimates.

General Limitations

• AI initially struggled with shell command syntax constraints (redirection/piping) enforced by the environment guard.
• The AI was unable to perform a git commit directly due to repository security guards (guarding the ‘git commit’ verb), requiring the human to execute the final command.

Learnings

Key Learnings

• Average loss can be misleading in long-horizon tasks where success depends on a single, brief, critical action window (e.g., StopCube timing).
• Calculating budget from ‘stretches’ of single-model usage provides a self-correcting accuracy mechanism that adapts to provider changes.
• Rank correlation between training loss and success rate can be low (-0.51), especially when ’easy’ phases dilute the loss.

Practical Learnings

• For Minecraft servers, CPU core count (physical cores vs hyperthreads) and single-core speed are more critical for stability during chunk generation than raw vCPU numbers.
• When a script fails at a certain line, other later assertions in the same script might also be stale; manual probing of individual test functions is necessary to ensure full coverage.

Conversation Summaries

Claude Code Environment

✅ Repetitive /usage command testing The user executed the /usage command dozens of times in rapid succession to observe system behavior and command response consistency.

RoboMemory

✅ Diagnostic analysis of StopCube and InsertPeg Analyzed failure videos and recomputed phase-specific losses. Discovered that StopCube fails because the visual ‘badge’ signal updates too late relative to the required action timing. InsertPeg is identified as a precision issue.

TokenMonitor

🔍 Serial Refresh Architecture Design 21:52:42.000 | claude_code The user requested a move to a ‘serial refresh’ model to prevent CPU spikes. A complex multi-phase workflow was launched to: 1. Implement timeouts, 2. Archive data once per hour, 3. Implement a FIFO compute gate, and 4. Create a new master scheduler (refresh.rs). The workflow is currently executing phases S9 and S10.

✅ Plan Budget Estimation Implementation 04:35:20.677 | claude_code The user requested a feature to show estimated USD budgets for AI plans. The AI implemented a system that calculates budget via usage/utilization ratios, added a historical archive for Claude/Kimi, and backfilled Codex data from local logs. Tests were passed and the UI was updated with compact, unit-aware budget labels.

MIHD

🔍 Parameter Counting SPEC Drafted a detailed technical specification (SPEC-2) to count trainable parameters for NCL, ablation studies, and external references like QueST and STAIG original.

✅ Fix golden fusion-gen dual-write Investigated failure in test_golden_fusion_gen.py. Determined that commit eb3f29d introduced an intentional dual-write for ‘gt’ and ‘gt_labels’. Updated the test suite to expect both keys, ensuring all 14 golden tests pass.

Minecraft Server

✅ Cost and Performance Analysis Analyzed AWS instance usage and pricing. Determined that moving to r6a.large (16GB) or r8a.large (16GB) offers a better balance of cost and stability compared to the current m8a.xlarge.

Token Usage