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

ArXi:2512.07801v5 Announce Type: replace-cross LLM-based agents are increasingly deployed for expert decision, 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.