Bibliographic record
Abstract
이 논문은 "감성 괴델 프레임워크(Emotional Gödel Framework, EGF)"라는 새로운 인공지능 감성 추론 프레임워크를 제안하는 연구입니다. 본 프레임워크는 최근의 자기 진화형 AI(Self-Evolving AI) 및 감성 AI(Affective AI) 연구 동향과 이론을 통합하여, 인공지능이 스스로 감성적 추론을 학습하고 진화하는 구조를 설계합니다. 본 논문은 최신 연구인 Darwin Gödel Machine(DGM), Self-Challenging Language Model Agents, ProRL 등과의 연계성을 분석하고, 감성 영역에서의 고유한 차별성을 갖는 Emotional Gödel Framework(EGF)를 제안합니다. EGF는 감성 주체성(Affective Agency), 자기 재귀적 코드 수정(Self-Recursive Code Modification), 감성 스티어링(Emotion Steering)을 핵심 구성 요소로 포함합니다. 본 연구는 개념적 시뮬레이션과 평가 지표를 제시하며, 향후 감성 기반 AI 시스템의 윤리적 설계 및 인간-중심적 상호작용 설계에 기여할 수 있습니다. 이 논문은 국제적 DOI 등록을 통해 인용 가능하며, 향후 KCI 등재 학술지에 정식 제출 예정입니다. This paper proposes a novel "Emotional Gödel Framework (EGF)" for designing self-evolving affective reasoning AI systems. Integrating recent advances in self-evolving AI (such as Darwin Gödel Machine), Self-Challenging Language Model Agents, and ProRL, this framework enables AI systems to recursively modify their reasoning structures and develop affective agency. The EGF introduces three core components: Affective Agency, Self-Recursive Code Modification, and Emotion Steering. Conceptual simulations and evaluation criteria are presented to demonstrate the potential of EGF in ethical and human-centered AI design. This version is a DOI-registered preprint, with formal submission to a KCI-indexed academic journal planned.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".