Daily Report — 2026-03-28

Today’s Overview

What was done: Systematic fixes and feature integrations across four projects on two machines: a dual-layer refactor of the Context Replay mechanism in Error Recovery Benchmark — both conceptual (VLA narrative cleanup) and code-level (policy_adapter timing + render_window rename); LIBERO/BOSS evaluation environment repair and zero-shot design intent analysis; intelligent dual-track instrumentation architecture integration for the cchypothesis skill (validated against 11 adversarial questions from a critic agent); four progressive bug fixes in the TokenMonitor Tauri app (Dashboard 1–2 Hz jitter, 42 Rust compile errors, 4 broken frontend imports, inverted window resize anchor).
How it was done: On tianhe: ccplan planning → Explore subagent exploration → batch grep/Edit modifications → pytest validation loop to complete the context_replay.py refactor and 22-location documentation cleanup across 7 files; then diff-compared asset directories and traced the whitelist mechanism in form_boss_44_dataset.py to confirm benchmark design intent. On TzJsDesktop: adversarial ccplan planning for the skill documentation refactor; TokenMonitor fixes applied in root-cause order, validated end-to-end with cargo/vitest/svelte-check.
Why it matters: Eliminated a fundamental conceptual error in error_recovery_benchmark (misunderstanding of the VLA context window), bringing code logic and documentation into alignment; the BOSS evaluation pipeline can now load all tasks correctly and properly interprets the expected 0% behavior for zero-shot tasks; cchypothesis gains runtime instrumentation verification capability; TokenMonitor recovered from multiple UX defects to a stable, releasable state with 222 Rust + 191 frontend tests passing and svelte-check reporting 0 errors.

tianhe

What was done: Completed two projects: (1) A comprehensive Context Replay refactor for error_recovery_benchmark — corrected the false VLA context window narrative, fixed policy_adapter timing, removed dead observations code, renamed render_window, and synchronized ~22 documentation locations across 7 files; (2) Fixed 5 missing object assets in the BOSS benchmark and confirmed through analysis that the 0% success rate on 7 LIVING_ROOM zero-shot tasks is intentional benchmark design for generalization testing.
How it was done: Used the ccplan skill for structured planning, combined with Explore subagent for codebase exploration; batch-located changes with grep and applied precise edits with the Edit tool; verified each round with pytest (139 unit tests). On the LIBERO side, confirmed design intent by diff-comparing asset directories and examining the whitelist mechanism in form_boss_44_dataset.py.
Why it matters: Improved correctness of context_replay.py (removed dead code, fixed timing, unified naming), with consistent updates across 7 files; the BOSS evaluation script can now load all 44 task environments; confirmed that 0% success on 7 zero-shot tasks is expected behavior, not a model problem.

TzJsDesktop

What was done: Completed two projects: (1) Refactored the cchypothesis skill into an intelligent dual-track architecture (Phase 3: parallel static + serial instrumentation + Git Safety Checkpoint + Phase 4: human confirmation), validated against 11 adversarial questions from a critic agent, spanning 4 files with +395/−70 lines; (2) Four progressive bug fixes in TokenMonitor: Dashboard 1–2 Hz jitter (four-layer positive feedback loop interruption), 42 compile errors left over from the Rust commands module refactor, 4 broken frontend import paths, and inverted window resize anchor (Win32 API replacement + removed dynamic anchor detection + atomic IPC command).
How it was done: On cchypothesis: used ccplan to select Option C, batch-edited 4 technical documents, self-reviewed before handing off to the critic agent for validation. On TokenMonitor: fixed in root-cause order — RESIZE_SETTLE_DELAY + shallowPayloadEqual + throttle to break the jitter loop; rewrote commands.rs as a thin module root to resolve Rust module ambiguity; updated import paths to fix Vite resolution; removed dynamic anchor detection in favor of fixed bottom anchoring, with backend IPC atomic resize handling.
Why it matters: cchypothesis now has runtime instrumentation verification capability and its architecture has passed rigorous adversarial review; TokenMonitor completed a full recovery from multiple UX defects to all tests passing (222 Rust + 191 frontend, svelte-check 0 errors), with window positioning behavior matching system tray popup expectations.

A full day of parallel work across tianhe and TzJsDesktop on four projects: tianhe completed the Context Replay conceptual refactor and code fixes for error_recovery_benchmark, and resolved LIBERO/BOSS evaluation environment missing assets with zero-shot design analysis; TzJsDesktop completed the intelligent dual-track instrumentation architecture integration for the cchypothesis skill, plus four progressive bug fixes in TokenMonitor — Dashboard jitter, Rust compilation errors, broken frontend imports, and window anchor inversion.

Today’s Tasks

Architecture & Strategy

✅ Context Replay code logic fix (remove dead code + fix policy_adapter timing + rename render_window) — Removed the dead observations list (collected but never consumed), moved policy_adapter feeding from inside the replay loop (clean-frame phase) to Step F (after post-injection environment stabilization), globally renamed context_window to render_window (with backward-compatible fallback in the ErrorScene data structure), updated 3 pipeline scripts and test files; 139 unit tests pass.
✅ Integrate instrumentation debug mode into cchypothesis skill (intelligent dual-track architecture) — Used ccplan to select Option C; refactored Phase 3 to: Git Safety Checkpoint + investigation routing (static/needs-instrumentation) + parallel static analysis + serial instrumentation probing ([DEBUG Hx] tagged logs + per-round git restore cleanup) + synthesis; Phase 4 expanded with Human Confirmation; added Instrumentation Protocol section; resolved 11 adversarial issues from the critic agent; touched SKILL.md/cchypothesis.md/diagnostic-schema.md/skills/CLAUDE.md across 4 files with +395/−70 lines.
✅ TokenMonitor Dashboard 1–2 Hz vertical jitter fix — Four concurrent fixes to break the ResizeObserver↔setSize positive feedback loop: ① RESIZE_SETTLE_DELAY_MS 16→100ms; ② shallowPayloadEqual to skip no-op store updates; ③ resize throttle (max 3 triggers per 500ms); ④ is_active in parser.rs gets a 2-minute grace period to eliminate 30-minute boundary oscillation; 191 vitest tests pass.
🔄 Context Replay residual check and set_sim_state_flat alternative planning — Used ccplan to audit ContextReplayEngine residuals in the codebase; found it fully present (393 lines) and used by 3 pipeline scripts; planned an alternative using set_sim_state_flat to jump directly to the injection frame instead of replaying frame by frame; user interrupted at ExitPlanMode, no code changes executed.
✅ Fix BOSS benchmark missing environment assets and analyze zero-shot task design intent — Evaluation script raised KeyError: ‘potato’; diff comparison revealed 5 object assets (corn/egg/lemon/onion/potato) missing from the standard LIBERO repository; after copying assets and registering 4 new classes in hope_objects.py, examined form_boss_44_dataset.py to confirm: boss_44’s allowed_files whitelist contains only 46 KITCHEN_SCENE files — the 7 LIVING_ROOM tasks are intentionally excluded as zero-shot generalization tests.
✅ Fix 42 compile errors left over from TokenMonitor Rust commands module refactor — Rewrote the old 2222-line commands.rs as an ~80-line thin module root (declaring 6 submodules, retaining AppState and shared helpers), eliminating the Rust module path ambiguity caused by the old file and the new commands/ directory coexisting; also fixed 4 pre-existing clippy warnings; cargo check/test (222 pass)/clippy/fmt all pass.
✅ TokenMonitor window positioning and resize bottom-anchor fix — Fixed two compounding bugs: ① replaced the inaccurate tauri_plugin_positioner with Win32 API (FindWindowW/FindWindowExW to locate TrayNotifyWnd) for precise initial popup positioning above the system tray; ② removed the VerticalAnchor enum and detect_vertical_anchor dynamic detection, aligned_window_origin now always calculates Y as work.bottom - height; ③ frontend setSize() changed to call backend set_window_size_and_align IPC atomic command to update both size and position simultaneously; all tests pass.

Implementation & Fixes

✅ Fix 4 broken frontend import paths in TokenMonitor — Updated import paths for rateLimitMonitor/traySync/windowAppearance in App.svelte and usage.ts to their new locations, added resizeDebug stub to uiStability.ts, filled in missing usage_source/usage_warning fields in emptyPayload/makePayload; 191 vitest tests pass, svelte-check reports 0 errors across 229 files.
✅ Full codebase VLA narrative cleanup and documentation update (OVERVIEW.md + 22 replacements) — Corrected 5 items in OVERVIEW.md (removed VLA timing narrative from Context Window description, changed Trajectory Collector to MimicGen Generator 10→1000 demos, Recovery Behavior Groups description, detailed reference table for 13 Error Skills, statistics); batch-replaced ~22 incorrect statements across 7 files including context_replay.py/framework/init.py/CLAUDE.md/benchmark_v5.yaml/项目全景总结.md with deterministic replay narrative; grep verified 0 residuals; 139 unit tests pass.

Problems & Solutions

Critical Issues

1. policy_adapter in Context Replay was fed inside the replay loop (clean-trajectory phase), meaning the policy saw clean pre-injection states rather than stable post-error states — inconsistent with real deployment scenarios.

Solution: Moved the policy_adapter.predict() call to Step F (after collect_rollout_stats completes and the environment has stabilized), ensuring the policy receives post-error observations after injection and environment stabilization.

Key Insight: The timing of policy_adapter feeding must match real deployment — the policy can only see states after the error has occurred and the environment has stabilized. Feeding clean frames during the replay phase is meaningless.

2. The context_window parameter name carried dual semantics (VLA observation window size vs. rendering display start-frame offset), and the documentation contained a false “VLA requires a temporal context window” narrative that fundamentally contradicts how VLA open-loop inference actually works.

Solution: Renamed the parameter to render_window to explicitly reflect its sole purpose — controlling render range; batch-replaced ~22 incorrect descriptions across 7 files with the correct “MuJoCo deterministic simulation state replay” description; grep verified 0 residuals.

Key Insight: VLA is open-loop inference — each step independently receives a single-frame input, maintains no recurrent state, and requires no “temporal context window.” Naming is the cheapest form of documentation; incorrect narratives are more dangerous than code bugs because they cause systematic misunderstanding during handoffs, paper writing, and code review.

3. TokenMonitor Dashboard was continuously jittering vertically at 1–2 Hz, caused by three overlapping positive feedback loops: data refresh → re-render → window Resize → trigger data refresh again, plus is_active state oscillation at 30-minute boundaries.

Solution: Four concurrent fixes: ① RESIZE_SETTLE_DELAY_MS 16→100ms to widen the stability window; ② shallowPayloadEqual to skip no-op store updates; ③ resize throttle to limit cascading (500ms/3 triggers); ④ is_active check with a 2-minute grace period to eliminate time-boundary oscillation.

Key Insight: Jitter bugs caused by multiple overlapping positive feedback loops must have all loops broken simultaneously — any single fix can only weaken, not eliminate, the problem.

4. BOSS evaluation script raised KeyError: ‘potato’ at runtime and couldn’t load task environments; 7 LIVING_ROOM tasks all showed 0% success rate in boss_44 evaluation, suspected to be a model generalization or training data issue.

Solution: Copied 5 missing object assets (corn/egg/lemon/onion/potato) from the BOSS repository to the corresponding LIBERO directories and registered new classes; examined form_boss_44_dataset.py to confirm that the allowed_files whitelist contains only KITCHEN_SCENE files — the 7 LIVING_ROOM tasks are intentionally excluded zero-shot generalization tests, and 0% success rate is the expected design behavior.

Key Insight: BOSS is an extended benchmark built on LIBERO that introduces new objects absent from the standard repository; its core evaluation philosophy (Out-of-Suppositional-Set) is to assess zero-shot generalization on completely unseen scenarios — 0% should not be misread as model failure.

Solution: Designed an intelligent dual-track architecture: static hypotheses follow the parallel READ-ONLY subagent path; inconclusive static investigation results escalate to serial instrumentation probing ([DEBUG Hx] tagged logs + per-round git restore cleanup); Git Safety Checkpoint at the Phase 3 entry protects the user’s workspace.

Key Insight: Instrumentation debugging should be the escalation path when static analysis is inconclusive, not a replacement — this preserves the speed advantage of parallel execution while gaining runtime probing capability.

6. 42 compile errors appeared in the Rust project (unresolved imports like crate::change_stats/crate::integrations, etc.) caused by the old commands.rs (2222 lines) and the new commands/ subdirectory coexisting, creating module path ambiguity.

Solution: Rewrote the old commands.rs as an ~80-line thin module root that correctly declares 6 submodules under the commands/ subdirectory, removing all duplicate functions and stale imports that had been moved to submodules; also fixed 4 pre-existing clippy warnings.

Key Insight: In Rust’s module system, commands.rs and commands/mod.rs are mutually exclusive as module roots; when both exist, the old file prevents the new submodules from being recognized. Incremental refactors must replace/remove the old entry point as the final step.

Solution: ① Replaced tauri_plugin_positioner with Win32 API (Shell_TrayWnd → TrayNotifyWnd) to get precise tray coordinates; ② removed the detect_vertical_anchor dynamic detection, aligned_window_origin now always calculates Y as work.bottom - height; ③ changed frontend setSize() to call the backend set_window_size_and_align IPC atomic command to update both size and position simultaneously.

Key Insight: tauri_plugin_positioner’s Windows support is unreliable; system tray popups are always bottom-anchored and require no dynamic detection; Tauri’s setSize() is a size-only API — resize must go through backend IPC to atomically handle both position and size.

General Issues

8. The observations list collected the last 50 frames of obs during the replay loop, but the list was never consumed by subsequent code — dead code that wasted memory and obscured the rendering purpose.

Solution: Removed the observations list and the associated context_start initialization code directly; rendering proceeds independently through the render_fn callback and is unaffected.

Key Insight: The render_fn callback and the observations collection were two parallel mechanisms; the latter was dead code left over from incremental development, and its removal affects no functionality.

9. Vite could not resolve 4 frontend import paths (rateLimitMonitor.js/traySync.js/windowAppearance.js/resizeDebug.js) — caused by an incremental refactor deleting old files without updating references.

Solution: Updated import paths to new locations, added a resizeDebug stub function in uiStability.ts as a replacement export, and filled in missing fields in emptyPayload/makePayload.

Key Insight: Incremental refactors that delete old files must synchronously update all import references, otherwise Vite resolution errors will be left behind.

Human Thinking vs. AI Thinking

Strategic Level

VLA domain knowledge: direct command of context window concept and project architecture

Role	Approach
Human	User explicitly pointed out that VLA has no context window concept (open-loop inference, each step independent, no recurrent state), so there’s no need to replay 50 frames for VLA at injection time; also directly identified that the Trajectory Collector in Section 3.2 is actually the MimicGen Generator (10→1000 demos).
AI	AI did not proactively question the VLA-aware design assumptions in code comments and accepted the old narrative; understanding of project architecture lagged behind the user’s direct knowledge and required reconstruction by tracing code paths.

Difference: The user had prior domain knowledge of VLA inference mechanics and direct command of the project’s overall design intent; AI tended to trust design comments already present in code, producing less accurate architectural understanding that required explicit user correction before systematic cleanup could begin.

Physical intuition for correct policy_adapter feeding timing

Role	Approach
Human	User explicitly stated that policy_adapter should only start receiving frames “after error injection is complete and the environment has stabilized” — a precise sim-to-real alignment requirement derived immediately from physical simulation intuition.
AI	AI identified that policy_adapter was being called inside the replay loop but tended to enumerate options for user confirmation rather than directly determining the correct timing from simulation semantics.

Difference: User immediately identified the correct timing from physical intuition; AI needed option confirmation, reflecting insufficient depth of understanding of simulation semantics.

Independent root cause identification for Rust module refactor

Role	Approach
Human	User only provided the compile error output, without explaining the refactor background or the module coexistence issue.
AI	AI proactively used Explore agent to deeply analyze old and new module structures, independently identified that commands.rs as the module root was blocking commands/ from being recognized, and formulated a complete rewrite plan.

Difference: AI correctly and independently identified the Rust module system’s coexistence ambiguity trap — a language-mechanics problem that doesn’t depend on domain knowledge, demonstrating proactive analysis capability beyond the user’s prompt.

Hypothesis direction for BOSS zero-shot task root cause

Role	Approach
Human	User proactively proposed the core hypothesis: the 7 tasks’ 0% success rate may be due to training set coverage gaps rather than poor model generalization.
AI	AI listed training set files, compared against evaluation tasks, examined the dataset construction script to validate the hypothesis, and further discovered this was intentional zero-shot generalization testing by benchmark design.

Difference: Human proposed the correct problem direction (data coverage hypothesis); AI handled validation execution and added the mechanistic explanation (allowed_files whitelist design). Human intuition was correct; AI provided the evidence chain.

Decision-making for cchypothesis integration approach

Role	Approach
Human	User chose the most architecturally comprehensive Option C (intelligent dual-track), selected all four integration modes, and approved the full implementation plan including 11 risk fixes — a more aggressive decision than AI anticipated.
AI	AI designed three options at increasing complexity levels and recommended Option C; performed adversarial analysis upfront and pre-fixed most known risks before the critic agent feedback.

Difference: User’s decision was more aggressive than AI expected; AI’s pre-fix pattern showed initiative but caused the critic agent’s findings to become post-hoc confirmation rather than pre-emptive prevention.

User visual perception identifies independent bugs and assesses fix complexity in TokenMonitor

Role	Approach
Human	User immediately identified multiple independent issues through visual perception: screenshot directly showed popup position was wrong; after the first fix, immediately pointed out the independent “bottom edge moves, top edge stays” logic gap (signaled with ????? implying the fix should be simple); pre-collected community solution documentation for the Dashboard jitter.
AI	AI addressed only the currently reported bug each time; did not anticipate that resize was an independent second bug during the first fix; called Explore + Plan agents for extensive analysis of the window anchor issue when the actual fix was just deleting ~30 lines.

Difference: User’s user-perspective allowed direct perception of functional defects and fix complexity assessment; AI tended toward systematic analysis of each bug and may over-analyze simple problems; user’s pre-collection of solutions separated the high-cost search step out of the main workflow.

AI Limitations

Significant Limitations

Insufficient understanding of physical simulation semantics: for the policy_adapter timing error, AI tended to ask “which option?” rather than directly determining the correct timing from simulation semantics; for VLA open-loop inference mechanics, AI failed to proactively question false assumptions in code comments, requiring explicit user correction before initiating systematic cleanup.
Blind spots in code data flow analysis: the dead observations list (collected but never consumed) required user guidance to discover; during the first TokenMonitor window positioning fix, AI did not anticipate that resize was an independent second bug, only recognizing the Tauri setSize() position-less API behavior after user visual feedback.
Execution pacing and parallel processing judgment issues: after ccplan planning completed, AI tried to push ahead with changes before waiting for user confirmation, leading to user interruption; critic agent findings became post-hoc confirmation rather than pre-emptive prevention because it returned after ~390 lines of changes were already committed; AI over-invoked Explore + Plan agents for extensive analysis on a simple problem (a window anchor fix requiring just 30 lines deleted).

General Limitations

Lack of global view across sessions: repeatedly scanned for the same class of problems (VLA context narrative) across multiple sessions, starting from scratch each time, with relatively low efficiency.
Environment dependency and tooling limitations: unable to verify LIBERO OBJECTS_DICT registration in the main environment (requires robosuite); process substitution diff commands batch-failed in Windows Git Bash requiring serial retries; cannot read binary files (.mp4, etc.).

Today’s Learnings

Core Learnings

VLA (Vision-Language-Action) models are open-loop inference: each step independently receives a single-frame observation and outputs an action, maintaining no recurrent hidden state — so “replaying N frames to build a context window before injection” is a fundamental misunderstanding of how VLA works.
MuJoCo simulation state is deterministic but not snapshot-reproducible: actions must be executed frame by frame from the initial state to obtain correct intermediate physical states; directly using set_sim_state_flat to jump to a target frame is a potential alternative (physical consistency requires evaluation).
UI jitter bugs are often the result of multiple overlapping positive feedback loops; a single-layer fix can only weaken, not eliminate the problem. All loops must be broken simultaneously (measurement delay, equality checking, throttle, data boundary grace period).
The correct architectural pattern for instrumentation debugging: parallel static analysis as the primary path, instrumentation probing as the escalation path for inconclusive results, each hypothesis independently completing an instrument→run→analyze→cleanup cycle, with Git Safety Checkpoint at the entry protecting the user’s workspace; in multi-phase debugging, prefer git restore . over git stash to avoid stacking conflicts.
Naming is the cheapest expression of design intent (context_window → render_window); incorrect documentation narratives are more harmful than code bugs — they don’t affect current execution results but cause systematic misunderstanding during project handoffs, paper writing, and code review, and must be proactively identified and systematically cleaned.
BOSS benchmark design mechanism: boss_44 training set intentionally covers 37 KITCHEN tasks via an allowed_files whitelist while excluding 7 LIVING_ROOM tasks for zero-shot generalization evaluation; BOSS extends standard LIBERO’s object assets (corn/egg/lemon/onion/potato) and requires separate retrieval from the BOSS repository before use.
System tray popup positioning should always be bottom-anchored (work.bottom - height) with no dynamic detection needed (dynamic detection is extremely error-prone under initialization timing and race conditions); tauri_plugin_positioner’s Windows support is unreliable — use Win32 API (FindWindowW/FindWindowExW) for precise coordinates; Tauri’s setSize() is a size-only API; resize must atomically handle both size and position through backend IPC.
Safe order for incremental Rust module refactors: first create the new file structure and have the old entry file re-export everything, verify compilation passes, then replace/delete the old entry as the final step; commands.rs and commands/mod.rs coexisting causes module path ambiguity where the old file blocks new submodules from being recognized.
The shallowPayloadEqual pattern (reference equality check on cache hit + field-level shallow comparison on background refresh) is an effective way to prevent unnecessary Svelte store re-renders, especially suited for high-frequency data polling scenarios.

Session Summaries

Error Recovery Benchmark

🔄 Comprehensive Context Replay Refactor: VLA Concept Correction + Narrative Cleanup + Code Logic Fixes 04:10:46.496 | claude_code User discovered a fundamental false “VLA requires temporal context window” narrative in context_replay.py, and completed a comprehensive refactor across four sessions: ① planned a set_sim_state_flat replacement for frame-by-frame replay (user interrupted, not executed); ② corrected 5 items in OVERVIEW.md (Context Window description, MimicGen Generator 10→1000 demos, 13 Error Skills detailed table, etc.) and fixed 7 peripheral config file locations; ③ batch-replaced ~22 VLA narrative occurrences across 7 files with deterministic replay descriptions, grep verified 0 residuals; ④ fixed 3 code logic defects: removed dead observations code, moved policy_adapter feeding to post-injection stabilization (Step F), globally renamed context_window to render_window (with ErrorScene backward-compatible fallback). 139 unit tests passed throughout.

OpenPI-LIBERO

✅ Fixed BOSS Benchmark Missing Object Assets and Confirmed Zero-Shot Task Design Intent 11:06:05.882 | claude_code Running the BOSS evaluation script raised KeyError: ‘potato’; diff comparison revealed 5 object assets (corn/egg/lemon/onion/potato) missing from the standard LIBERO repository. After copying assets and registering 4 new classes, further analysis of the 7 LIVING_ROOM tasks all showing 0% success: examination of form_boss_44_dataset.py confirmed the allowed_files whitelist intentionally excludes LIVING_ROOM scenes — this is BOSS benchmark’s core design philosophy of evaluating zero-shot generalization on completely unseen scenarios; 0% is expected behavior.

gadget-skills

✅ Integrated Instrumentation Debug Mode into cchypothesis Skill (Intelligent Dual-Track Architecture) 04:19:54.398 | claude_code Web search first confirmed no comparable product exists; cchypothesis’s parallel subagent + batch hypothesis design is unique. Through complete ccplan planning, user selected all four modes and chose Option C (intelligent dual-track); AI refactored Phase 3 (Git Safety Checkpoint + investigation routing + parallel static + serial instrumentation [DEBUG Hx] tags + per-round git restore), extended Phase 4 (human confirmation), added Instrumentation Protocol section, and completed fixes after 11 adversarial issues from the critic agent — 4 files with +395/−70 line changes total.

TokenMonitor

✅ TokenMonitor Four Progressive Bug Fixes (Jitter, Rust Compile, Frontend Imports, Window Anchor) 04:02:33.844 | claude_code Completed four progressive fixes throughout the day: ① Dashboard 1–2 Hz jitter — user provided community solution documentation, AI identified three-layer positive feedback loops and applied RESIZE_SETTLE_DELAY increase + shallowPayloadEqual shallow comparison + resize throttle + is_active 2-minute grace period as four-layer fixes; ② Rust commands module refactor leaving 42 compile errors — rewrote the 2222-line old commands.rs as an 80-line thin module root, resolving the module path ambiguity from commands.rs and commands/ directory coexisting; ③ 4 broken frontend import paths — updated to new locations and added resizeDebug stub, svelte-check reports 0 errors across 229 files; ④ initial window positioning and resize bottom-anchor fix — replaced tauri_plugin_positioner with Win32 API, removed dynamic anchor detection in favor of work.bottom - height fixed bottom anchoring, changed frontend setSize to IPC atomic command. Final result: 222 Rust + 191 frontend tests all passing.

Token Usage

Overview

Metric	Value
Total Tokens	69,731,622
Input Tokens	127,251
Output Tokens	183,224
Cache Created	3,409,971
Cache Read	66,011,176
Cache Hit Rate	95.1%
Total Cost (USD)	$46.3856

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-opus-4-6	52,717	95,468	2,067,580	55,051,251	$43.0983	92.9%
claude-haiku-4-5-20251001	74,534	87,756	1,342,391	10,959,925	$3.2873	7.1%

Per-Device Usage

Device	Total Tokens	Input	Output	Cost
tianhe	14,020,085	40,870	46,529	$9.1710
TzJsDesktop	55,711,537	86,381	136,695	$37.2146

Bug Journal 2026-03-28

Daily Report — 2026-03-28

Today’s Overview

tianhe

TzJsDesktop

Today’s Tasks

Architecture & Strategy

Implementation & Fixes

Problems & Solutions

Critical Issues

1. policy_adapter in Context Replay was fed inside the replay loop (clean-trajectory phase), meaning the policy saw clean pre-injection states rather than stable post-error states — inconsistent with real deployment scenarios.

3. TokenMonitor Dashboard was continuously jittering vertically at 1–2 Hz, caused by three overlapping positive feedback loops: data refresh → re-render → window Resize → trigger data refresh again, plus is_active state oscillation at 30-minute boundaries.

4. BOSS evaluation script raised KeyError: ‘potato’ at runtime and couldn’t load task environments; 7 LIVING_ROOM tasks all showed 0% success rate in boss_44 evaluation, suspected to be a model generalization or training data issue.

5. cchypothesis’s existing Phase 3 pure READ-ONLY parallel architecture cannot validate runtime hypotheses (timing races, dataflow state, dynamic behavior), creating a debugging blind spot.

6. 42 compile errors appeared in the Rust project (unresolved imports like crate::change_stats/crate::integrations, etc.) caused by the old commands.rs (2222 lines) and the new commands/ subdirectory coexisting, creating module path ambiguity.

General Issues

8. The observations list collected the last 50 frames of obs during the replay loop, but the list was never consumed by subsequent code — dead code that wasted memory and obscured the rendering purpose.

9. Vite could not resolve 4 frontend import paths (rateLimitMonitor.js/traySync.js/windowAppearance.js/resizeDebug.js) — caused by an incremental refactor deleting old files without updating references.

Human Thinking vs. AI Thinking

Strategic Level

VLA domain knowledge: direct command of context window concept and project architecture

Physical intuition for correct policy_adapter feeding timing

Independent root cause identification for Rust module refactor

Hypothesis direction for BOSS zero-shot task root cause

Decision-making for cchypothesis integration approach

User visual perception identifies independent bugs and assesses fix complexity in TokenMonitor

AI Limitations

Significant Limitations

General Limitations

Today’s Learnings

Core Learnings

Session Summaries

Error Recovery Benchmark

OpenPI-LIBERO

gadget-skills

TokenMonitor

Token Usage

Overview

Model Breakdown

Per-Device Usage

Daily Report — 2026-03-28#

Today’s Overview#

tianhe#

TzJsDesktop#

Today’s Tasks#

Architecture & Strategy#

Implementation & Fixes#

Problems & Solutions#

Critical Issues#

1. policy_adapter in Context Replay was fed inside the replay loop (clean-trajectory phase), meaning the policy saw clean pre-injection states rather than stable post-error states — inconsistent with real deployment scenarios.#

3. TokenMonitor Dashboard was continuously jittering vertically at 1–2 Hz, caused by three overlapping positive feedback loops: data refresh → re-render → window Resize → trigger data refresh again, plus is_active state oscillation at 30-minute boundaries.#

4. BOSS evaluation script raised KeyError: ‘potato’ at runtime and couldn’t load task environments; 7 LIVING_ROOM tasks all showed 0% success rate in boss_44 evaluation, suspected to be a model generalization or training data issue.#

5. cchypothesis’s existing Phase 3 pure READ-ONLY parallel architecture cannot validate runtime hypotheses (timing races, dataflow state, dynamic behavior), creating a debugging blind spot.#

6. 42 compile errors appeared in the Rust project (unresolved imports like crate::change_stats/crate::integrations, etc.) caused by the old commands.rs (2222 lines) and the new commands/ subdirectory coexisting, creating module path ambiguity.#

General Issues#

8. The observations list collected the last 50 frames of obs during the replay loop, but the list was never consumed by subsequent code — dead code that wasted memory and obscured the rendering purpose.#

9. Vite could not resolve 4 frontend import paths (rateLimitMonitor.js/traySync.js/windowAppearance.js/resizeDebug.js) — caused by an incremental refactor deleting old files without updating references.#

Human Thinking vs. AI Thinking#

Strategic Level#

VLA domain knowledge: direct command of context window concept and project architecture#

Physical intuition for correct policy_adapter feeding timing#

Independent root cause identification for Rust module refactor#

Hypothesis direction for BOSS zero-shot task root cause#

Decision-making for cchypothesis integration approach#

User visual perception identifies independent bugs and assesses fix complexity in TokenMonitor#

AI Limitations#

Significant Limitations#

General Limitations#

Today’s Learnings#

Core Learnings#

Session Summaries#

Error Recovery Benchmark#

OpenPI-LIBERO#

gadget-skills#

TokenMonitor#

Token Usage#

Overview#

Model Breakdown#

Per-Device Usage#

Daily Report — 2026-03-28

Today’s Overview

tianhe

TzJsDesktop

Today’s Tasks

Architecture & Strategy

Implementation & Fixes

Problems & Solutions

Critical Issues

1. policy_adapter in Context Replay was fed inside the replay loop (clean-trajectory phase), meaning the policy saw clean pre-injection states rather than stable post-error states — inconsistent with real deployment scenarios.

3. TokenMonitor Dashboard was continuously jittering vertically at 1–2 Hz, caused by three overlapping positive feedback loops: data refresh → re-render → window Resize → trigger data refresh again, plus is_active state oscillation at 30-minute boundaries.

4. BOSS evaluation script raised KeyError: ‘potato’ at runtime and couldn’t load task environments; 7 LIVING_ROOM tasks all showed 0% success rate in boss_44 evaluation, suspected to be a model generalization or training data issue.

5. cchypothesis’s existing Phase 3 pure READ-ONLY parallel architecture cannot validate runtime hypotheses (timing races, dataflow state, dynamic behavior), creating a debugging blind spot.

6. 42 compile errors appeared in the Rust project (unresolved imports like crate::change_stats/crate::integrations, etc.) caused by the old commands.rs (2222 lines) and the new commands/ subdirectory coexisting, creating module path ambiguity.

General Issues

8. The observations list collected the last 50 frames of obs during the replay loop, but the list was never consumed by subsequent code — dead code that wasted memory and obscured the rendering purpose.

9. Vite could not resolve 4 frontend import paths (rateLimitMonitor.js/traySync.js/windowAppearance.js/resizeDebug.js) — caused by an incremental refactor deleting old files without updating references.

Human Thinking vs. AI Thinking

Strategic Level

VLA domain knowledge: direct command of context window concept and project architecture

Physical intuition for correct policy_adapter feeding timing

Independent root cause identification for Rust module refactor

Hypothesis direction for BOSS zero-shot task root cause

Decision-making for cchypothesis integration approach

User visual perception identifies independent bugs and assesses fix complexity in TokenMonitor

AI Limitations

Significant Limitations

General Limitations

Today’s Learnings

Core Learnings

Session Summaries

Error Recovery Benchmark

OpenPI-LIBERO

gadget-skills

TokenMonitor

Token Usage

Overview

Model Breakdown

Per-Device Usage