[Paper] Nested Browser-Use Learning for Agentic Information Seeking

Source: arXiv - 2512.23647v1

Overview

The paper “Nested Browser‑Use Learning for Agentic Information Seeking” tackles a practical bottleneck in modern AI assistants: most agents can only fetch raw snippets or URLs via APIs, missing out on the wealth of information hidden behind interactive web pages. By introducing a lightweight, hierarchical browser‑action framework called NestBrowse, the authors enable agents to control browsing at a high level while still digging deep into complex, dynamic sites—opening the door to richer, more reliable information‑seeking capabilities.

Key Contributions

• Nested Browser‑Action API – a minimal yet complete set of actions that separates control flow (e.g., “click this button”) from content exploration (e.g., “scroll and read the page”).
• NestBrowse Learning Paradigm – trains agents to issue nested actions, allowing them to reason about “when to open a new page” versus “how to extract data from the current page.”
• Empirical Validation on Deep‑Web Benchmarks – demonstrates consistent performance gains over traditional ReAct‑style agents on tasks that require multi‑step navigation, form filling, and pagination.
• Efficiency & Flexibility Analyses – shows that the nested design reduces the number of required API calls and can be plugged into existing LLM‑based agents with minimal code changes.

Methodology

1. Action Space Design

   • High‑level actions (open_page, close_page) manage the browser stack.
   • Low‑level actions (click, type, scroll, extract) operate within the currently active page.
   • The nesting creates a tree‑like execution trace: each new page becomes a child node, preserving context while keeping the parent’s reasoning intact.

2. Training Loop

   • The authors generate synthetic browsing trajectories using a rule‑based “oracle” that solves each benchmark task.
   • These trajectories are converted into sequences of nested actions and fed to a standard LLM (e.g., GPT‑4) fine‑tuned with supervised learning.
   • During inference, the model predicts the next action, the browser simulator executes it, returns a concise observation (e.g., extracted text, DOM snapshot), and the loop repeats.

3. Evaluation Setup

   • Benchmarks include DeepWebQA, Multi‑Page Retrieval, and Form‑Filling Search, each requiring at least three navigation steps and interaction with dynamic content.
   • Baselines: vanilla ReAct agents (API‑only), tool‑calling agents with flat browser actions, and a handcrafted rule‑based crawler.

Results & Findings

• Higher accuracy across all tasks, especially where deep navigation (>3 hops) is required.
• ~30 % fewer API calls compared with flat‑browser agents, thanks to the nesting that avoids redundant page reloads.
• Robustness to layout changes: the hierarchical context helps the model recover when a page’s DOM shifts after a click.

Practical Implications

• Richer ChatGPT‑style assistants – developers can now embed a NestBrowse module to let the assistant “look up” information that lives behind login walls, infinite scrolls, or interactive charts, delivering more up‑to‑date answers.
• Enterprise knowledge retrieval – internal tools that need to scrape data from legacy web portals (e.g., ticketing systems, inventory dashboards) can be automated without writing custom scrapers for each site.
• Reduced engineering overhead – the API is intentionally small; integrating it into existing LangChain or LlamaIndex pipelines requires only a few wrapper functions.
• Cost efficiency – fewer round‑trips to the browser mean lower compute time and lower API usage bills for hosted LLM services.

Limitations & Future Work

• Simulation vs. Real Browsers – Experiments were run on a headless Chromium simulator; performance on heavily JavaScript‑driven sites (e.g., SPAs) may differ.
• Scalability of Action Sequences – Very long navigation trees (>10 levels) can still cause context overflow in current LLM token limits.
• Security & Ethics – Automated browsing raises concerns about unintended scraping of copyrighted or private content; the authors call for policy‑aware action filters.
• Future directions include extending NestBrowse to multi‑agent collaboration (e.g., one agent handles navigation while another focuses on reasoning) and exploring reinforcement‑learning fine‑tuning to reduce reliance on synthetic oracle trajectories.

Authors

• Baixuan Li
• Jialong Wu
• Wenbiao Yin
• Kuan Li
• Zhongwang Zhang
• Huifeng Yin
• Zhengwei Tao
• Liwen Zhang
• Pengjun Xie
• Jingren Zhou
• Yong Jiang

Paper Information

• arXiv ID: 2512.23647v1
• Categories: cs.CL, cs.AI, cs.IR, cs.MA
• Published: December 29, 2025
• PDF: Download PDF