Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistants
Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of realistic user simulation frameworks hinders their development. Existing approaches model apps as flat tool-calling APIs, failing to capture the stateful and sequential nature of user interaction in digital environments and making realistic user simulation infeasible. We introduce Proactive Agent Research Environment (Pare), a framework for building and evaluating proactive agents in digital environments. Pare models applications as finite state machines with…
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that…
In today’s fast-paced digital landscape, the seamless operation and performance of software applications is crucial for businesses. Downtime, glitches and service interruptions can result in significant revenue loss and damage a company’s reputation. This is where modern, advanced monitoring solutions like IBM Instana come into play. With its cutting-edge capabilities,…
A stapler slides across a desk to meet a waiting hand, or a knife edges out of the way just before someone leans against a countertop. It sounds like magic, but in Carnegie Mellon University's Human-Computer Interaction Institute (HCII), researchers are combining AI and robotic mobility to give everyday objects…