Signal-Driven Observation for Long-Horizon Web Agents
Quick Answer
The paper introduces Signal-Driven Observation (SDO) for web agents, addressing context degradation by selectively querying the DOM for relevant elements only when triggered by specific events, enhancing reasoning efficiency during long-horizon tasks.
Quick Take
The paper introduces Signal-Driven Observation (SDO) for web agents, addressing context degradation by selectively querying the DOM for relevant elements only when triggered by specific events, enhancing reasoning efficiency during long-horizon tasks.
Key Points
- SDO reduces context degradation by querying only relevant DOM elements.
- Triggered by events like URL changes or action failures, SDO optimizes observation frequency.
- The approach emphasizes observation compression as a key design decision.
- Open problems related to SDO are identified for further community exploration.
Article Excerpt
From source RSS / original summaryarXiv:2606. 06708v1 Announce Type: new Abstract: Web agents operating over long horizons ingest raw DOM and accessibility trees -- routinely tens of thousands of tokens -- at every action step, causing progressive context degradation that erodes reasoning well before tasks complete. We argue that this coupling of observation frequency to action frequency is an architectural mistake.
Drawing on the insight from Recursive Language Models that querying a document outperforms reading it wholesale, we propose Signal-Driven Observation (SDO): a dedicated sub-call reads the full DOM but returns only task-relevant elements and their selectors, and is re-invoked only when a lightweight signal detector fires -- triggered by URL transitions, newly visible interactive elements, action failures, or exogenous browser events.
We outline the open problems SDO introduces and call on the community to treat observation compression as a core architectural decision in web agent design.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?
The REFLECT benchmark reveals that current LLM judges are unreliable, achieving below 55% accuracy in evaluating reasoning and evidence use, highlighting the need for improved evaluation methods for deep research agents.