arXiv:2509.22504v3 Announce Type: replace-cross Abstract: As language model (LM) agents become increasingly capable and adopted in real-world applications, there is a growing need for scalable evaluation frameworks beyond costly, manually designed benchmarks. We propose information-theoretic evaluation based on empowerment, an information-theoretic measure of an agent's influence on future states through its actions. To handle the unique challenges of text-based environments, we introduce EELMA (Estimating Empowerment of Language Model Agents), an algorithm for approximating effective empowerm
The increasing deployment of language model agents in real-world applications necessitates robust and scalable evaluation frameworks beyond current manual methods.
This research provides a foundational approach for quantitatively evaluating the capabilities and influence of AI agents, which is crucial for their safe and effective deployment.
The ability to systematically measure an LM agent's empowerment offers a standardized method for comparing and improving agent performance across diverse tasks and environments.
- · AI developers
- · AI safety researchers
- · Companies deploying LM agents
- · Manual evaluation methods
- · Inadequate evaluation frameworks
Improved evaluation leads to more reliable and capable language model agents.
Enhanced agent reliability accelerates the adoption and integration of AI agents into complex workflows, potentially collapsing certain white-collar tasks.
Widely adopted and highly capable AI agents could fundamentally reshape labor markets and industry structures by automating increasingly sophisticated cognitive work.
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Read at arXiv cs.LG