agent/papers/items/2026-2607-01084-can-agents-generalize-to-the-open-world-unveiling-the-fragility-of-static-traini.md

Paper: Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use

---

type: paper title: Can Agents Generalize to the Open World? Unveiling the Fragility of Static Training in Tool Use authors: Song-Lin Lv, Weiming Wu, Rui Zhu, Zi-Jian Cheng, Lan-Zhe Guo year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.01084 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-01 updated_at: 2026-07-01 status: queued relevance: high topics:

• agent-evaluation
• rag
• reasoning
• tool-use methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 16 collection_queries: llm-agent, tool-use

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: llm-agent, tool-use
• inferred topics: agent-evaluation, rag, reasoning, tool-use
• arXiv categories: cs.AI
• collection score: 16

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.01084