agent/papers/items/2025-2512-23647-nested-browser-use-learning-for-agentic-information-seeking.md

Paper: Nested Browser-Use Learning for Agentic Information Seeking

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type: paper title: Nested Browser-Use Learning for Agentic Information Seeking authors: Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, et al. year: 2025 venue: arXiv url: https://arxiv.org/abs/2512.23647 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2025-12-29 updated_at: 2025-12-29 status: queued relevance: high topics:

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

benchmarks:

models:

datasets:

• cs.CL
• cs.AI
• cs.IR
• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 15 collection_queries: function-calling

One-line Takeaway

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

Why Collected

• matched queries: function-calling
• inferred topics: agent-evaluation, computer-use, rag, reasoning, tool-use
• arXiv categories: cs.CL, cs.AI, cs.IR, cs.MA
• collection score: 15

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/2512.23647