Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
arXiv:2607.01084v1 Announce Type: new Abstract: While Large Language Model (LLM) agents demonstrate proficiency in static benchmarks, their deployment in real-world scenarios is hindered by the dynamic nature of user queries, tool sets, and interaction dynamics. To address this generalization gap, we formalize OpenAgent (Tool-Use Agent in Open-World), a problem setting characterized by distributional shifts across query, action, observation, and domain dimensions. To systematically diagnose its impact, we construct a controlled sandbox environment where we define fine-grained environmental shi
The increasing deployment of LLM agents highlights a critical gap between static benchmark performance and real-world dynamic environments, necessitating a focus on generalization.
This research addresses a fundamental limitation in AI agents, directly impacting their commercial viability and the speed of their adoption in complex, real-world scenarios.
The understanding of AI agent performance shifts from static benchmarks to dynamic 'open-world' generalization, requiring new development paradigms and testing methodologies.
- · AI research institutions specializing in generalization
- · Companies developing robust, adaptive AI agent platforms
- · Early adopters willing to stress-test agentic systems
- · Developers relying solely on static benchmark performance
- · Companies with brittle, non-adaptive AI agent deployments
Further research and development will focus on creating AI agents capable of robust generalization beyond controlled environments.
The commercial deployment of AI agents will be accelerated as issues of fragility in dynamic settings are systematically addressed.
New AI safety and ethics frameworks will emerge to account for the unpredictable behaviors of generalized agents in open-world settings.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI