Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support

• URL: http://arxiv.org/abs/2512.07801v3
• Date: Wed, 10 Dec 2025 14:42:17 GMT
• Title: Collaborative Causal Sensemaking: Closing the Complementarity Gap in Human-AI Decision Support
• Abstract summary: LLM-based agents are increasingly deployed for expert decision support.<n>Yet human-AI teams in high-stakes settings do not yet reliably outperform the best individual.<n>We argue this complementarity gap reflects a fundamental mismatch: current agents are trained as answer engines, not as partners in the collaborative sensemaking through which experts actually make decisions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based agents are increasingly deployed for expert decision support, yet human-AI teams in high-stakes settings do not yet reliably outperform the best individual. We argue this complementarity gap reflects a fundamental mismatch: current agents are trained as answer engines, not as partners in the collaborative sensemaking through which experts actually make decisions. Sensemaking (the ability to co-construct causal explanations, surface uncertainties, and adapt goals) is the key capability that current training pipelines do not explicitly develop or evaluate. We propose Collaborative Causal Sensemaking (CCS) as a research agenda to develop this capability from the ground up, spanning new training environments that reward collaborative thinking, representations for shared human-AI mental models, and evaluation centred on trust and complementarity. Taken together, these directions shift MAS research from building oracle-like answer engines to cultivating AI teammates that co-reason with their human partners over the causal structure of shared decisions, advancing the design of effective human-AI teams.

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