Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation

Yubo Li; Weiyi Song

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arXiv · 2509.12179

Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation

Yubo Li·Weiyi Song

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Abstract

Current AI alignment through RLHF follows a single directional paradigm that AI conforms to human preferences while treating human cognition as fixed. We propose a shift to co-alignment through Bidirectional Cognitive Alignment (BiCA), where humans and AI mutually adapt. BiCA uses learnable protocols, representation mapping, and KL-budget constraints for controlled co-evolution. In collaborative navigation, BiCA achieved 85.5% success versus 70.3% baseline, with 230% better mutual adaptation and 332% better protocol convergence. Emergent protocols outperformed handcrafted ones by 84%, while bidirectional adaptation unexpectedly improved safety (+23% out-of-distribution robustness). The 46% synergy improvement demonstrates optimal collaboration exists at the intersection, not union, of human and AI capabilities, validating the shift from single-directional to co-alignment paradigms.

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Yubo Li, Weiyi Song. 2025-09-15. Co-Alignment: Rethinking Alignment as Bidirectional Human-AI Cognitive Adaptation. https://arxiv.org/abs/2509.12179

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