LATIDIA · Investigación

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

arXiv: 2610.20665v1Tipo de anuncio: nuevo Resumen: los agentes del modelo de lenguaje grande (LLM) pueden resolver tareas interactivas complejas, pero el razonamiento y las llamadas a herramientas llevan tiempo. En entornos en continuo cambio, un

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arXiv:2610.20665v1 Announce Type: new Abstract: Large language model (LLM) agents can solve complex interactive tasks, yet reasoning and tool calls take time. In continuously changing environments, an otherwise correct action may arrive too late. We introduce RealtimeBench, with 180 tasks across six game types, to evaluate agents while games continue running during inference. Paired evaluations show that GPT-6.1-sol outperforms the faster Jev when games are paused, but falls behind when they keep running. We propose Two-Timescale Offline Recursive Self-Improvement (TORSI): a slow teacher studies failures and tests corrections in paused games between attempts, then writes reusable instructions for a fast student. The student acts without teacher calls during gameplay, and