ARTFEED — Contemporary Art Intelligence

ProActor: A Framework for Proactive Task Scheduling Agents

other · 2026-05-26

ProActor introduces a unified framework for proactive task scheduling in conversational agents, shifting from reactive systems that await instructions to agents that anticipate user needs and trigger actions autonomously. The framework includes a domain-agnostic annotation methodology for scalable reinforcement learning, systematic proactiveness metrics, and optimization using GRPO with RULER-based rewards. The paper is published on arXiv under ID 2605.24900.

Key facts

  • ProActor is a framework for proactive task scheduling agents.
  • It integrates automated annotation for scalable RL.
  • It uses systematic proactiveness metrics.
  • Optimization uses GRPO with RULER-based rewards.
  • The paper is on arXiv with ID 2605.24900.
  • It aims to shift from reactive to proactive agents.
  • The approach is domain-agnostic.
  • It focuses on conversational task scheduling.

Entities

Institutions

  • arXiv

Sources