Contextual Value Alignment via Multilayer Combinatorial Fusion

2026-08-07

AI Summary

This work addresses the challenge that current large language models struggle to align with human values across diverse moral contexts, as single-agent architectures and uniform reward mechanisms fail to capture ethical pluralism and the dynamics of multi-agent moral reasoning. To overcome this limitation, the authors propose a Multi-layer Compositional Fusion framework for Contextual Value Alignment (MCF-CVA), which employs multiple moral agents representing distinct value systems and integrates their outputs through an Expand-Aggregate-Reduce (EAR) iterative mechanism operating in both Euclidean score space and Kemeny ranking space. This approach uniquely combines cognitive diversity, multi-space fusion, and multi-layer iteration to achieve context-sensitive value alignment. Experimental results demonstrate that MCF-CVA significantly outperforms single-agent baselines, single-layer multi-agent methods, and existing aggregation strategies, establishing its effectiveness and robustness on standard evaluation metrics.

Abstract

Aligning large language models (LLMs) with human values remains a major challenge, especially for trustworthy AI. While existing approaches such as RLHF, CAI, and their variants have achieved promising results, they often rely on a single-agent framework and a unified reward system. This limits their ability to capture ethical pluralism, adapt to diverse moral contexts, and reflect the dynamics of multi-agent moral reasoning. In this work, we propose a framework that utilizes multilayer combinatorial fusion for contextual value alignment (MCF-CVA). At the first layer of the framework, it instantiates multiple moral agents, each fine-tuned to represent a distinctive value. Their outputs are then expanded combinatorially using both score- and rank-combinations as well as average and weighted aggregations. These combined models are then reduced to the same number of initial moral agents. This expansion and reduction (EAR) process continues for multi-layers until a stopping criterion is reached. The MCF-CVA framework leverages cognitive diversity between agents to mitigate conflicts and redundancies across multiple agents, producing responses that better reflect contextual human values. The framework using the EAR algorithm is performed on the dual architecture of Euclidean score space and Kemeny rank space. Empirical evaluations demonstrated that the proposed framework outperforms single-agent baselines, multi-agent single-layer results, and previous aggregation approaches on standard metrics, showing that the MCF-CVA framework provides a robust and effective mechanism for advancing contextual value alignment in LLMs.

Problem

• value alignment
• ethical pluralism
• moral reasoning
• large language models
• contextual values

Innovation

• multilayer combinatorial fusion
• contextual value alignment
• multi-agent moral reasoning
• expansion and reduction (EAR)
• cognitive diversity