Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
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 shifts across a four-tier hierarchy, Perception, In
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- Linked via arxiv authorSong-Lin Lv →
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
- Linked via arxiv authorWeiming Wu →
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
- Linked via arxiv authorRui Zhu →
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
- Linked via arxiv authorZi-Jian Cheng →
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use
- Linked via arxiv authorLan-Zhe Guo →
Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use