Architectural Concepts for Integrating Fundamental Drives and Emotions Into Artificial Intelligence
Why this work is in the frame
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Bibliographic record
Abstract
Current large language models display limited emotional intelligence, often mimicking affective patterns without genuine understanding, which raises manipulation and safety risks. We argue that artificial intelligence (AI) should prioritize long-term human well-being over short-term engagement, and that advancing toward artificial general intelligence (AGI) requires embedding fundamental drives and artificial emotions in model architectures. Building on Lazarus’s cognitive-rational theory, we propose a framework with an emotional module and a rational module, where artificial drives guide affective appraisal and decision-making. This enables alignment of artificial emotions with core values—such as human well-being, fairness, and environmental preservation—anchoring AI safety at the architectural level rather than through post hoc fixes. We discuss technical and ethical challenges, including data needs and reward modeling, and call for open science and regulation to ensure human-centered AGI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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