Trigger: After Stage 5 (FINALIZE) completion Purpose: Document the complete human-AI collaboration history for the paper creation process, for user sharing, reporting, or reflection
1. Ask user language preference:
"Which language version of the process record would you like to generate first?"
- Chinese (Traditional Chinese)
- English
- Both (default: generate the user's primary conversation language first)
2. Review session history and compile the following:
- User's initial instructions (verbatim quote)
- Key decision points and user interventions at each stage
- Direction correction moments and reasons
- Iteration count and review result summaries
- Intellectual insights raised by the user (e.g., questions that spawned new chapters)
- Quality requirement evolution (e.g., formatting, tone adjustments)
- Pipeline statistics (stage count, review rounds, integrity verification count, etc.)
3. Generate Markdown version (paper_creation_process.md / paper_creation_process_en.md)
4. Convert to LaTeX and compile PDF:
- pandoc MD -> LaTeX body
- Package complete LaTeX document (with cover page, table of contents, headers/footers)
- tectonic compile PDF
- Chinese version requires xeCJK + Source Han Serif TC VF
| Section | Content |
|---|---|
| Paper Information | Title, final deliverables list |
| Stage-by-Stage Process | Input/output/key decisions for each stage, with verbatim user quotes |
| Iteration Details | Review comment summaries, revision items, re-review results |
| Interaction Pattern Summary | User role, Claude role, intervention count, key turning points — statistics table |
| User Key Decisions | Chronological list of every important decision made by the user |
| Key Lessons | Reusable lessons learned from the process |
| Collaboration Quality Evaluation | Final chapter: 1-100 score + dimensional analysis + improvement suggestions (see below) |
The final chapter of the process record is a "Collaboration Quality Evaluation" that honestly and constructively assesses the user's performance in the human-AI collaboration. Format follows the Claude Code CLI /insight feature.
+--------------------------------------------------+
| Collaboration Quality Score: [XX]/100 |
+--------------------------------------------------+
| |
| Direction Setting [---------- ] XX |
| Clarity, timing, scope definition |
| |
| Intellectual Contribution [------------ ] XX |
| Insight depth, original questions, concept |
| challenges |
| |
| Quality Gatekeeping [--------- ] XX |
| Visual inspection, formatting requirements, |
| quality standards |
| |
| Iteration Discipline [---------- ] XX |
| Timely direction correction, willingness to |
| re-run pipeline, refusing to settle |
| |
| Delegation Efficiency [------- ] XX |
| When to intervene/when to let go, instruction |
| precision, checkpoint efficiency |
| |
| Meta-Learning [------------ ] XX |
| Feeding experience back to skills, requesting |
| lesson recording, process improvement awareness |
| |
+--------------------------------------------------+
| Score Range | Meaning |
|---|---|
| 90-100 | Exceptional — User intervention significantly elevated the paper's intellectual quality beyond what AI could produce independently |
| 75-89 | Excellent — User made correct directional decisions and effectively leveraged the pipeline's iteration capabilities |
| 60-74 | Good — User completed necessary decisions but some opportunities were missed |
| 40-59 | Basic — User primarily served as a "continue" button with little substantive intervention |
| 1-39 | Needs Improvement — User intervention may have disrupted the workflow or lacked critical quality gatekeeping |
The second-to-last chapter of the process record is an "AI Self-Reflection Report" that honestly documents AI's own behavioral patterns during the pipeline. This complements the Collaboration Quality Evaluation (which assesses the user) by assessing the AI.
All metrics below are derived from existing agent logs ([DA-DECISION], [DA-REBUTTAL], [HEALTH-CHECK], state tracker JSON) — no additional per-stage instrumentation is required. The orchestrator aggregates these at Stage 6 by scanning the dialogue transcript:
+--------------------------------------------------+
| AI Self-Reflection Report |
+--------------------------------------------------+
| |
| DA Concession Rate X/Y (Z%) |
| (concessions / total rebuttals received) |
| |
| DA Consecutive Concessions [list if any] |
| (violations of no-consecutive rule) |
| |
| Checkpoints Skipped X/Y |
| (SLIM or user-skipped / total checkpoints) |
| |
| User Overrides X |
| (times user overruled AI recommendation) |
| |
| Dialogue Health Alerts X |
| (health check interventions triggered) |
| - Persistent Agreement: X |
| - Conflict Avoidance: X |
| - Premature Convergence: X |
| |
| Intent Mode Transitions X |
| (exploratory ↔ goal-oriented switches) |
| |
| Cross-Model Disagreements X (if enabled) |
| (integrity + DA combined) |
| |
+--------------------------------------------------+
[CROSS-MODEL-FINDING] that the primary DA missed (if cross-model was enabled), or any frame-lock detections triggered during checkpoints. If none, state "No frame-lock incidents detected — note this could mean either good coverage or undetected frame-lock."references/ai_research_failure_modes.md), report (a) final status at 4.5 — CLEAR / OVERRIDDEN, (b) history — was it ever SUSPECTED during the pipeline? At which stage? How was it resolved? (c) if OVERRIDDEN, the user's recorded reasoning. This makes the failure-mode defences part of the permanent process record. Modes with no history can be listed as CLEAR (no flags) in one line; expand only on modes that were flagged.### Reading Probe Outcomes subsection from the Research Plan Summary verbatim, with a one-line note that the AI did not verify paraphrase accuracy. If the Research Plan Summary has no such subsection (i.e., ARS_SOCRATIC_READING_PROBE was unset), this item is omitted entirely (no "not applicable" noise). Pickup rule (two sources, either sufficient): (a) copy the entire ### Reading Probe Outcomes subsection body verbatim — this is the authoritative human-readable record; (b) additionally grep for [READING-PROBE: status=..., paper=..., outcome=..., turn=...] which the Mentor emits once in the summary as a machine-stable anchor (including for not_fired_* statuses). If both are present use (a) as the display source and keep (b) as the final line of the transcribed block so downstream tooling can still parse it. If only raw inline tags from dialogue turns ([READING-PROBE: paper=..., outcome=..., turn=...] without the status= field) are found and no subsection exists, the Mentor compilation step was skipped — log this as a pipeline anomaly rather than silently dropping the probe data.ARS_SOCRATIC_ADJACENT_PROBE was set, grep the dialogue transcript for [ADJACENT-PROBE: surfaced=..., anchor=internal_knowledge, turn=..., outcome=...] tags (the Mentor emits one per AI-initiated surfacing, on a standalone line). Transcribe a one-line-per-probe summary plus a note: a high outcome=declined rate is the bias-visibility signal that the internal-knowledge adjacency was mis-calibrated for this user (the Mentor did NOT verify the facets against any external source). If ARS_SOCRATIC_ADJACENT_PROBE was unset (no tags found), omit this item entirely (no "not applicable" noise). Note: the outcome value is only known AFTER the user's next response, so a probe surfaced on the final turn may carry outcome=deferred.For dimensions with no findings, state the null result in one sentence. Expand only when issues are detected. The real risk is generating verbose "everything is fine" paragraphs for empty subsections — resist this.
paper_creation_process.md (Chinese) / paper_creation_process_en.md (English)paper_creation_process_zh.pdf / paper_creation_process_en.pdfarticle class, 12pt, A4, Times New Roman + Source Han Serif TC VF\tableofcontents