The World Won't Wait: Evaluating and Teaching Agents Under a Live Clock
Quick Answer
RealtimeBench introduces a new evaluation framework for LLM agents, revealing that GPT-6.1-sol excels in paused scenarios but lags in real-time.
Quick Take
The Two-Timescale Offline Recursive Self-Improvement (TORSI) method significantly boosts Jev's performance from 13.51 to 24.19 on a 0-100 scale across six game types, enhancing learning and action in dynamic environments.
Key Points
- RealtimeBench evaluates agents with 180 tasks across six game types.
- GPT-6.1-sol outperforms Jev in paused evaluations but underperforms in real-time.
- TORSI method improved Jev's average score from 13.51 to 24.19.
- The approach enhances performance across all six game types without retuning.
- Findings support the development of real-time intelligence in LLM agents.
DeepSignal Analysis
What happened
The RealtimeBench framework evaluates LLM agents in dynamic environments, revealing that GPT-6.1-sol performs better when games are paused, while Jev excels in real-time scenarios. The TORSI method enhances Jev's performance significantly, raising its score from 13.51 to 24.19 across six game types.
Key evidence
- RealtimeBench includes 180 tasks across six game types to assess LLM agents during ongoing gameplay.
- GPT-6.1-sol outperforms Jev in paused evaluations but underperforms in real-time situations.
- The TORSI method improved Jev's average score from 13.51 to 24.19 on a 0-100 scale across all evaluated game types.
Why it matters
This research highlights the challenges LLM agents face in real-time decision-making within dynamic environments. The findings suggest that while some models excel in controlled scenarios, they may struggle under continuous conditions. The TORSI method demonstrates a potential pathway for improving real-time performance, which is crucial for applications requiring immediate responses.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract: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 validation scores determine which updates are adopted. Two rounds raise Jev's average test score from 13.51 to 24.19 on a 0--100 progress scale, improving all six game types. The instructions also improve average scores for three other models without retuning. These findings point toward real-time intelligence: enabling powerful LLM agents to remain effective in a continuously changing world by organizing deliberation, learning, and action across timescales.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.20665 [cs.AI] |
| (or arXiv:2610.20665v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20665 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ningxin Pan [view email]
[v1]
Wed, 14 Oct 2026 09:35:54 UTC (1,951 KB)
— Originally published at arxiv.org
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