AI

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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 16/10/2026, 04:03:24 UTC.

Briefings

This article presents findings from a 2026 study on LLM agents operating in live environments. The research introduces RealtimeBench, an evaluation framework using 180 tasks across six game types to test agent performance while games run continuously. Key findings indicate that GPT-6.1-sol outperforms the faster Jev model when games pause, but underperforms when games keep running. The authors propose Two-Timescale Offline Recursive Self-Improvement (TORSI), a method where a slow teacher analyzes failures in paused games to generate instructions for a fast student, which then acts autonomously during gameplay.

Key points

• GPT-6.1-sol outperforms the faster Jev model when game environments are paused during inference.
• GPT-6.1-sol falls behind in performance when game environments continue running during inference.

Context

The study evaluates LLM agents in continuously changing environments using a live clock mechanism. This approach allows researchers to observe how agents perform under real-time pressure, distinguishing between paused and active game states. The findings suggest that effective agent behavior requires balancing deliberation with action across different timescales.

Limitations

The evidence is limited to a single preprint from 2026 with no peer review or experimental replication. Results are based on a snapshot of one specific evaluation run, and the study does not account for long-term agent degradation or external system failures beyond the game environment.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 16/10/2026, 04:40:16 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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