Region4Web: Rethinking Observation Space Granularity for Web Agents
Authors: Donguk Kwon, Dongha Lee
Summary
Web agents typically observe web pages at the granularity of individual elements (buttons, links, etc.), which forces them to infer functional organization at every step. The authors propose Region4Web, which groups elements into functional regions (parts of the page serving distinct purposes) and presents these as a compact "digest" that persists across reasoning steps. On WebArena, this region-level observation reduces input length and improves task success across different LLMs and agent architectures, regardless of model size.
Main takeaways:
- Current web agents observe pages at element-level granularity, leaving functional structure implicit
- Grouping elements into functional regions (e.g., navigation bar, search box, content area) gives agents a better basis for understanding page state
- PageDigest delivers this as a persistent per-page summary rather than re-processing elements every step
- Shorter observations and higher success rates across LLM backbones on WebArena benchmark
Relevance
Not directly related to my persona/midtraining work—included because it's a useful example of how observation granularity affects agent behavior, though my focus is on behavioral conditioning rather than task-oriented web agents.
Threat model
Potential threat/caveat for clean result "Fine-tuning one persona on a two-marker chunk and another on the start marker plants the end marker at every donor answer's end, not chained to the start (LOW confidence)": this item discusses benchmark.
Abstract
arXiv:2605.07134v1 Announce Type: new Abstract: Web agents perceive web pages through an observation space, yet its granularity has remained an underexamined design choice. Existing work treats observation at the same element-level granularity as the action space, leaving the page's functional organization implicit and forcing the agent to infer it from element-level signals at every step. We argue observation should instead operate at the granularity of functional regions, parts of the page that each serve a distinct purpose. We propose Region4Web, a framework that reorganizes the AXTree into functional regions through hierarchical decomposition and semantic abstraction, exposing the page's functional organization as the basis for page state understanding. Moreover, we propose PageDigest, a web-specific inference pipeline that delivers this region-level observation to the actor agent as a compact per-page digest that persists across steps. On the WebArena benchmark, PageDigest substantially reduces observation length while improving overall task success rate across diverse backbone large language models (LLMs) and established agent methods, regardless of backbone capacity. These results show that operating at the granularity of functional regions delivers a more compact and informative basis for the actor agent than element-level processing alone.