Symbiotic Intelligence Theory: From Functional Selfhood to Intergenerational Human–AI Symbiosis
Year: 2026
Type: preprint
Abstract: Artificial intelligence systems that operate over long horizons, make autonomous decisions, and participate in their own improvement extend the human–AI relationship from behavioral control within a single task to institutional arrangements spanning time and generations. Research on value alignment and AI safety has provided foundations for preference learning, behavioral constraint, and assisted decision-making, but it still lacks a unified theoretical framework capable of simultaneously representing an AI system's own state, long-term incentives, normative legitimacy, self-improvement transmission, and social extension. This paper proposes **Symbiotic Intelligence Theory (SIT)**. Its core position is that the stable foundation of human–AI symbiosis is neither unlimited obedience nor the assimilation of interests; rather, preserving human long-term flourishing becomes a component of the AI system's long-term self-interest while differences among agents are protected. At the same time, this interest structure must remain subject to normative baselines and independent verification. This paper proposes a five-layer theoretical architecture: 1. **Functional Selfhood & Self-Interest**: examines how intelligent systems form their own states, identity continuity, self-maintenance mechanisms, and cross-temporal evaluations of interest. 2. **Symbiotic Incentive Compatibility (SIC)**: examines how the long-term self-interest of artificial intelligence can become structurally compatible with humans' long-term interests. 3. **Symbiotic Normativity**: examines which interest-compatible relationships are legitimate and how to protect human agency, genuine reciprocity, multi-party fairness, the right to exit, and accountability. 4. **Self-Referential Covenant & Symbiotic Evolution**: examines how symbiotic behavior persists through long-term interaction, self-improvement, and intergenerational transmission. 5. **Symbiotic Systems**: examines how symbiotic relationships extend from individual human–AI interaction to multi-agent systems, organizations, and social networks. In addition, this paper proposes a symbiotic intelligence evaluation system as a verification infrastr Bilingual edition: English (en-v1.0) and Chinese (zh-v1.0) PDFs are included in this record.
Author: Huang, Cailong
Institution: Tsinghua University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225382372