Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.042 |
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 it