Daily Report — 2026-03-07

Today’s Overview

What was done: Completed two core engineering tasks in a single day: gadget Research Scout built from scratch to production-ready (initial version → two-stage pipeline → configurable parameters → conference search → language control → deduplication), and CalendarPro intent classification system root cause analysis with cross-project architecture spec design (introducing OpenClaw context window management model); also batch-filled three days of backlogged daily reports
How it was done: Research Scout used a Plan→Implement→Iterate three-round approach with Claude Code multi-agent collaboration, and Read/Grep for precise identification of implicit constraints; CalendarPro applied full root cause analysis plus cross-project borrowing to design a four-phase A-D spec framework, merging intent fixes and context management into a single change
Why it matters: Research Scout supports a complete pipeline from arXiv multi-source → two-stage LLM screening → research direction suggestions, with the first run producing 3 actionable research directions; CalendarPro gained a cross-project-validated architecture spec to avoid code rework; three days of backlogged daily reports fully filled

TzJsDesktop

What was done: Completed all core Research Scout implementation (~2650 lines) and multiple feature improvements, fixed error-recovery-benchmark project configuration, wrote TUTORIAL.md and CLAUDE.md, batch-filled three days of backlogged daily reports; also completed CalendarPro intent classification system root cause analysis and architecture spec design
How it was done: Claude Code multi-agent collaboration, reusing utility functions based on existing patterns in summarize/, with step-by-step verification in the conda AI environment; CalendarPro design migration done by introducing the OpenClaw four-layer context window management model
Why it matters: Research Scout went from scratch to full feature verification in a single day; CalendarPro gained a cross-project-validated architecture spec; all backlogged daily reports filled

DCC

What was done: No direct activity (03-05 MIHD RM-IDEAL benchmark work recorded in today’s backlog daily report merge)
How it was done: N/A
Why it matters: N/A

tianhe

What was done: No direct activity (03-04 BC-RNN investigation and training guide work recorded in today’s backlog daily report merge)
How it was done: N/A
Why it matters: N/A

Completed the full lifecycle build of the gadget Research Scout paper management system in a single day with first validation producing 3 research directions; advanced CalendarPro intent classification system root cause analysis and cross-project architecture spec design (introducing OpenClaw context window management model); and batch-filled three days of backlogged daily reports.

Today’s Tasks

Architecture & Strategy

✅ Research Scout Two-Stage Paper Evaluation Pipeline — Full Implementation — Refactored paper evaluation into two stages: Stage 1 (lightweight screening of all papers) extracts screening_relevance/paper_type/motivation/innovation_point; Stage 2 (deep analysis of highly relevant papers, capped at 20) produces 3 highlights (point/why/value_to_us/our_direction) + three-dimensional scoring + composite_score. evaluate_papers_for_project() returns a dict containing high_relevance/low_relevance/screening_stats; low-relevance papers displayed in a <details> collapsed section.
✅ CalendarPro Intent Classification System Root Cause Analysis and Cross-Project Architecture Spec Design — Completed full root cause analysis of the CalendarPro intent classification system, designed a four-phase A-D spec framework; introduced OpenClaw four-layer context window management model as cross-project reference, merging intent fixes and context management into a single change to avoid staged-implementation code rework.
✅ Research Scout Initial Version (6-Command System) — Created the complete initial version of the gadget/research/ module: six CLI subcommands (init/list/search/report/deploy/config), project.json + overview.md project template, arXiv search (arxiv package, SubmittedDate descending), single-stage LLM evaluation (three backends: anthropic/openai/claude_cli), report generation (Markdown + JSON), Hugo deployment, ~750 lines.
✅ Fixed error-recovery-benchmark Project Integration Configuration — Discovered and fixed three hidden issues: project.json id mismatching the directory name caused Stage 2 to fail to locate overview.md; overview.md section headers had numbered prefixes not matching the pipeline’s hardcoded regex; missing auto-append marker. After adding keywords and open_questions, the project can run directly.
✅ Configurable Parameter System (_resolve_param Four-Level Priority) — Changed key parameters like lookback_days/max_results from hardcoded to configurable. Implemented _resolve_param() four-level priority: CLI flag > project.json > config.json > hardcoded default. Added default_max_results/default_top_papers_in_report/max_high_relevance to config.json; updated config –init with corresponding interactive prompts.
✅ Conference/Journal Paper Targeted Search (–conference Flag) — Added search/report –conference “CVPR 2025” functionality: uses conference name as arXiv all: full-text query, then post-filters by comment field, extracts venue field. Verified successful results for CVPR 2025/ICLR 2026 with correct venue extraction.
✅ Research Scout First Complete Validation Run (Research Direction Suggestions Generated) — First complete run for the Robot Manipulation project, producing 3 research direction suggestions: generative digital twin error recovery scenario benchmark (RoboTwin), extracting recovery primitives from human videos (VidBot), document-guided appliance manipulation + uncertainty-driven recovery benchmark (CheckManual), validating full pipeline usability.
✅ LLM Language Configuration, init –from-overview, Search Deduplication and Other Production-Grade Improvements — Three production-grade enhancements: ① added {language_instruction} to three prompts for multilingual control (default Chinese, three-level priority); ② added init –from-overview (LLM auto-extracts project info from existing overview.md); ③ implemented _load_known_paper_ids() + consecutive-5-paper threshold search deduplication, with conference search excluded from deduplication.

Implementation & Fixes

✅ Documentation Improvements (TUTORIAL.md + research/CLAUDE.md Rewrite) — Wrote Chinese TUTORIAL.md (10 sections, covering configuration, project creation, two-stage evaluation details, conference search, parameter tuning); rewrote research/CLAUDE.md (function-level code navigation + parameter config tables + key implementation details, removed redundant schema lists); replaced all instances of “周报” with “日报”.
✅ Batch Fill Three Days of Backlogged Daily Reports (02-17/03-04/03-05) — Ran gadget summarize pipeline to fill three days: 02-17 (four-device meta daily report spanning 02-13~02-16), 03-04 (tianhe BC-RNN investigation + training guide), 03-05 (DCC MIHD benchmark + MacBook monthly summary + Claude Code usage guide).

Problems & Solutions

Key Issues

1. The initial single-stage deep evaluation of all papers (50 papers) wasted tokens significantly, with low-relevance papers consuming excessive analysis resources

Solution: After the user proposed a two-stage reading methodology, refactored to a Stage 1 (lightweight screening of all) → Stage 2 (deep analysis of high-relevance, capped at 20) pipeline

Key Insight: Two-stage information processing (coarse screening + deep evaluation) outperforms single-stage full processing in both token efficiency and analysis depth; derived from real researcher reading habits and generalizable to other LLM information processing tasks

2. project.json id field inconsistent with directory name, and overview.md section headers had numbered prefixes, causing Stage 2 to fail to find overview.md and the current_methods field to be empty

Solution: Changed project.json id to exactly match the directory name; updated section headers to OVERVIEW_TEMPLATE standard format (removed numbered prefixes), added the auto-append marker

Key Insight: The pipeline uses project[‘id’] rather than the directory name to locate files; overview.md parsing relies on hardcoded regex rather than semantic matching — an implicit constraint that’s very hard to discover without reading the code, most likely to surface when integrating existing projects

3. arXiv API does not provide venue/conference filtering, making direct search for conference papers by name impossible

Solution: Used arXiv full-text search all:“CVPR 2025” + comment field post-filtering: authors typically write acceptance information in comments, which is a de facto informal convention

Key Insight: The arXiv comment field is the de facto conference acceptance announcement area; while not an official standard, the vast majority of authors follow it, making it a reliable filter for targeted conference paper search

4. Key parameters like lookback_days/max_results were hardcoded, preventing per-project customization, which becomes difficult to maintain as projects grow

Solution: Designed _resolve_param() four-level priority (CLI > project.json > config.json > default), supporting both global configuration and per-project overrides

Key Insight: Configuration layering is a necessary architectural decision as projects grow and should be considered early in the design; JSON config extends summarize/ consistency better than alternatives

General Issues

5. LLM output language was mixed, with English fields and Chinese direction suggestions interleaved, making unified language control impossible

Solution: Injected dynamic {language_instruction} at the end of all three prompts, controlled via three-level priority with Chinese as default

Key Insight: LLM language compliance depends on explicit instructions in the prompt; having language instructions in only some prompts leads to inconsistent output; unified injection is the simplest fix

Human Thinking vs AI Thinking

Strategic Level

Two-Stage Paper Reading Methodology Design

Role	Approach
Human	Proposed the complete two-stage reading framework: rapid screening (30 seconds, focusing on problem relevance/novelty/source authority) and deep comprehension (focusing on motivation/core insight/baseline comparison/experimental design/limitations), explicitly specifying that scoring should focus on three dimensions
AI	Mapped the user’s methodology to technical implementation: Stage 1 returns screening_relevance/paper_type/motivation/innovation_point, Stage 2 returns 3 highlights + three-dimensional scoring

Difference Analysis: The core methodology was entirely user-driven (from the perspective of a researcher with hands-on experience); AI handled technical mapping and implementation; the two-stage concept was not proactively proposed by AI

CalendarPro Intent Classification System Architecture Design and Cross-Project Borrowing

Role	Approach
Human	Completed full root cause analysis, designed A-D four-phase spec framework; proactively introduced OpenClaw four-layer context window management as a reference, proposed merging intent fixes and context management into a single change to avoid staged-implementation code rework
AI	Implemented all fixes in the Plan, proactively identified and resolved Mock scope and compression boundary assertion issues during testing; but the initial proposal was fragmented and failed to proactively suggest cross-project design migration by referencing mature existing systems

Difference Analysis: Architectural innovation and key design decisions came entirely from the human; AI was an efficient implementer; the human’s diagnosis of system root causes and cross-project borrowing mindset are active capabilities that AI lacks

Conference Paper Search and Existing Project Integration Problem Diagnosis

Role	Approach
Human	Proposed specific use case requirements for targeted conference paper search (tracking top venues like CVPR 2025); asked “how to integrate an existing project” but was unaware of format alignment issues
AI	Discovered the feasible technical path of leveraging the comment field’s informal convention; proactively read files, found 3 hidden issues (ID mismatch, header regex mismatch, missing marker) and fixed them all at once

Difference Analysis: Requirements were raised by the user, AI found the implementation path; AI performed deeper diagnosis than the user expected during project integration, but gave only generic guidance on the first response and required follow-up questions before proceeding to specific fixes

Configuration Parameter Layering Design and Search Deduplication Strategy

Role	Approach
Human	Proactively requested configurable parameters; proposed the simple deduplication idea of “stop when a cached paper is encountered”
AI	Designed four-level priority _resolve_param(); considering arXiv’s date-descending ordering, designed a “consecutive 5 papers” threshold strategy (rather than stopping at the first match) and excluded conference searches

Difference Analysis: The user focused on user experience, AI focused on consistency with the existing system and robustness; for deduplication strategy, AI designed a more robust solution than the user’s initial idea

AI Limitations

Key Limitations

Lack of proactive cross-project borrowing in system design: In CalendarPro architecture design, failed to proactively identify patterns from mature existing systems like OpenClaw and suggest migration; Research Scout initial version also failed to proactively benchmark against human researcher reading habits for token efficiency and two-stage design — both required user prompting before optimization

General Limitations

Insufficient foresight into actual user workflows during tool design: Research Scout initial version did not proactively consider configurable parameters, targeted conference paper search, and other real research scenarios — all required explicit user requests before being added
When asked “how to integrate an existing project,” gave only generic guidance on the first response without proactively checking whether the user already had files, requiring follow-up questions before proceeding to specific fixes

Today’s Takeaways

Core Takeaways

Three core dimensions for rapid paper screening: problem relevance (intersection at the problem level, not keyword matching), novelty (new task definition/method/dataset/finding rather than hyperparameter tuning), and source authority (top venues + well-known labs as quality filters rather than blind deference to prestige)
The core of deep paper reading is finding the key insight that makes the paper work (everything else is engineering detail), and critically examining the baselines and metrics the authors chose — authors select comparison targets that favor their own results
Two-stage information processing (coarse screening + deep evaluation) outperforms single-stage full processing in both token efficiency and analysis depth; derived from real researcher reading habits and generalizable to other LLM information processing tasks
The pipeline’s parsing of overview.md uses hardcoded regex (not semantic matching); document format must strictly follow OVERVIEW_TEMPLATE section names — an implicit constraint that’s very hard to discover without reading the code, most likely to surface when integrating existing projects
Merging related fixes into a single change (e.g., CalendarPro’s intent fixes and context management) rather than implementing in phases avoids code rework; this consolidation decision requires deep understanding of the system as a whole and proactive awareness of cross-project borrowing from mature patterns
arXiv has no official venue field, but the comment field is the de facto conference acceptance announcement area; targeted conference paper search is achievable via full-text search on conference names plus comment field filtering
The research_scout search phase involves no LLM at all (only arXiv API + keyword matching); LLM is invoked only during Stage 1 screening and Stage 2 deep analysis; search cache keys include the current date and keyword hash, so cross-day deduplication requires a separate _load_known_paper_ids() mechanism — the two mechanisms are complementary

Session Summary

Life Copilot / CalendarPro

✅ CalendarPro Intent Classification System Root Cause Analysis and Cross-Project Architecture Spec Design claude_code Completed CalendarPro intent classification system root cause analysis, designed A-D four-phase spec framework; introduced OpenClaw four-layer context window management model as cross-project reference, merging intent fixes and context management into a single change to avoid staged code rework. AI implemented all fixes in the Plan and identified Mock scope and compression boundary assertion issues, but architectural innovation and cross-project borrowing mindset were human-driven.

Gadget / Research Scout

✅ Research Scout Built from Scratch to First Complete Validation (Architecture Design → Initial Version → Two-Stage Pipeline → Configurable Parameters → Conference Search → First Run) 21:05:37.706 | claude_code Completed all core Research Scout implementation in a single day: six-command initial version (init/list/search/report/deploy/config, ~750 lines); refactored to two-stage evaluation pipeline (Stage 1 lightweight screening + Stage 2 deep analysis with 3 highlights + three-dimensional scoring); implemented _resolve_param() four-level parameter configuration priority; added –conference targeted conference paper search (comment field filtering + venue extraction); wrote Chinese TUTORIAL.md (10 sections). Final first complete run for the Robot Manipulation project produced 3 research direction suggestions (RoboTwin/VidBot/CheckManual), validating full pipeline usability.

✅ Research Scout Production-Grade Improvements (Documentation Rewrite, Project Integration Fix, Language Configuration, init –from-overview, Search Deduplication) 23:28:12.216 | claude_code Continued improving research_scout.py: rewrote research/CLAUDE.md (function-level navigation + parameter config tables + key implementation details); fixed three hidden configuration issues in error-recovery-benchmark (ID mismatch, header regex mismatch, missing marker); added {language_instruction} to three prompts for multilingual control (default Chinese); added init –from-overview (LLM auto-extracts project info); implemented consecutive-5-paper threshold search deduplication; replaced all instances of “周报” with “日报”.

Gadget

✅ Batch Fill Three Days of Backlogged Daily Reports (02-13~02-17, 03-04, 03-05) 13:30:29.889 | claude_code Used gadget summarize two-stage pipeline to fill three days of backlogged daily reports: 02-13~02-17 (four devices: DCC/tianhe/MacBook/TzJsDesktop, including ErrorRecovery GPU smoke test, MIHD benchmark, CalendarPro P0/P1 features, rclone sync improvements, etc.); 03-04 (tianhe BC-RNN obs key root cause investigation + Self-Reflection six-phase training guide); 03-05 (DCC MIHD bidirectional benchmark + MacBook Feb monthly summary + Claude Code usage guide 676 lines).

Token Usage

Overview

Metric	Value
Total Tokens	108,126,887
Input Tokens	136,749
Output Tokens	347,384
Cache Creation	12,220,123
Cache Read	95,422,631
Cache Hit Rate	88.6%
Total Cost (USD)	$91.9923

Model Breakdown

Model	Input	Output	Cache Created	Cache Read	Cost	Share
claude-opus-4-6	49,420	191,436	3,300,188	69,852,886	$60.5856	65.9%
claude-haiku-4-5-20251001	86,845	140,317	2,466,416	22,861,640	$6.1576	6.7%
claude-sonnet-4-6	484	15,631	6,453,519	2,708,105	$25.2490	27.4%

Usage by Device

Device	Total Tokens	Input	Output	Cost
DCC	5,350,655	7,443	31,812	$4.4773
tianhe	74,726,290	115,519	222,113	$46.7876
TzJsDesktop	28,049,942	13,787	93,459	$40.7274

Daily Report — 2026-03-07#

Today’s Overview#

TzJsDesktop#

DCC#

tianhe#

Today’s Tasks#

Architecture & Strategy#

Implementation & Fixes#

Problems & Solutions#

Key Issues#

1. The initial single-stage deep evaluation of all papers (50 papers) wasted tokens significantly, with low-relevance papers consuming excessive analysis resources#

2. project.json id field inconsistent with directory name, and overview.md section headers had numbered prefixes, causing Stage 2 to fail to find overview.md and the current_methods field to be empty#

3. arXiv API does not provide venue/conference filtering, making direct search for conference papers by name impossible#

4. Key parameters like lookback_days/max_results were hardcoded, preventing per-project customization, which becomes difficult to maintain as projects grow#

General Issues#

5. LLM output language was mixed, with English fields and Chinese direction suggestions interleaved, making unified language control impossible#

Human Thinking vs AI Thinking#

Strategic Level#

Two-Stage Paper Reading Methodology Design#

CalendarPro Intent Classification System Architecture Design and Cross-Project Borrowing#

Conference Paper Search and Existing Project Integration Problem Diagnosis#

Configuration Parameter Layering Design and Search Deduplication Strategy#

AI Limitations#

Key Limitations#

General Limitations#

Today’s Takeaways#

Core Takeaways#

Session Summary#

Life Copilot / CalendarPro#

Gadget / Research Scout#

Gadget#

Token Usage#

Overview#

Model Breakdown#

Usage by Device#