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This paper introduces JarvisBench, a benchmark for evaluating a continuous, real-time spoken mediation layer in long-horizon AI agent workflows, and presents a modular Jarvis prototype tested on WildClaw tasks with various LLM-based worker agents.
This paper introduces an automated mediator for human negotiation that uses a structured pipeline of LLM modules to conduct pre-mediation. In human-subject experiments, the system achieves preparation outcomes comparable to professional human mediators while reducing error in preference inference.
Mediator.ai is a tool that applies Nash bargaining game theory and LLMs to facilitate fair cooperative negotiation, generating and scoring candidate agreements against both parties' stated needs until an optimal solution is found.