The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock

Large language‑model agents can already tackle complex interactive tasks, but the reasoning process and tool execution take time. In rapidly changing environments, a correct action can become useless if it arrives too late. To quantify this issue, the authors introduce RealtimeBench, a benchmark of 180 exercises spread across six game types in which the matches continue while the agent generates its response. Comparative results show that the GPT‑6.1‑sol model outperforms Jev when the games are paused, but it loses its edge when the simulation keeps running.

To address this, they propose a refinement method called TORSI that separates slow learning from fast execution. A “master” agent reviews mistakes and tests fixes in paused scenarios, producing reusable instructions. A faster “student” agent acts during gameplay without consulting the master; its updates are accepted only if they improve validation scores. After two iterations, Jev’s average score rises from 13.51 to 24.19 on a 0‑to‑100 scale, with improvements observed across all six game types. The same set of instructions also boosts the performance of three additional models without retraining.

This work demonstrates that organizing deliberation, learning, and action at different temporal rhythms enables AI agents to maintain effectiveness in real‑time evolving settings—a significant step toward truly operational intelligence in today’s world.

Read the original article on arXiv cs.AI

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