# FocusAgent: Simple Yet Effective Ways of Trimming the Large Context of Web Agents

arXiv: https://arxiv.org/abs/2510.03204 · HTML: https://mlsanity.com/paper/2510.03204/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 3 Oct 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 30 of 31)

- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal](https://mlsanity.com/paper/2402.04249/) (2024)
- [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023)
- [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://mlsanity.com/paper/2302.00093/) (2023)
- [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://mlsanity.com/paper/2410.09024/) (2024)
- [Language Models can Solve Computer Tasks](https://mlsanity.com/paper/2303.17491/) (2023)
- [WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?](https://mlsanity.com/paper/2403.07718/) (2024)
- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V](https://mlsanity.com/paper/2310.11441/) (2023)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [AI Agents That Matter](https://mlsanity.com/paper/2407.01502/) (2024)
- [WebLINX: Real-World Website Navigation with Multi-Turn Dialogue](https://mlsanity.com/paper/2402.05930/) (2024)
- [Multimodal Web Navigation with Instruction-Finetuned Foundation Models](https://mlsanity.com/paper/2305.11854/) (2023)
- [Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents](https://mlsanity.com/paper/2504.00906/) (2025)
- [WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks](https://mlsanity.com/paper/2504.18575/) (2025)
- [Is Your LLM Secretly a World Model of the Internet? Model-Based Planning for Web Agents](https://mlsanity.com/paper/2411.06559/) (2024)
- [EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakage](https://mlsanity.com/paper/2409.11295/) (2024)
- [Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents](https://mlsanity.com/paper/2503.00061/) (2025)
- [Attacking Vision-Language Computer Agents via Pop-ups](https://mlsanity.com/paper/2411.02391/) (2024)
- [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024)
- [SafeArena: Evaluating the Safety of Autonomous Web Agents](https://mlsanity.com/paper/2503.04957/) (2025)
- [WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration](https://mlsanity.com/paper/2408.15978/) (2024)
- [BM25S: Orders of magnitude faster lexical search via eager sparse scoring](https://mlsanity.com/paper/2407.03618/) (2024)
- [AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents](https://mlsanity.com/paper/2503.09780/) (2025)
- [Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systems](https://mlsanity.com/paper/2407.13032/) (2024)
- [SteP: Stacked LLM Policies for Web Actions](https://mlsanity.com/paper/2310.03720/) (2023)
- [Refusal-Trained LLMs Are Easily Jailbroken As Browser Agents](https://mlsanity.com/paper/2410.13886/) (2024)
- [RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments](https://mlsanity.com/paper/2505.21936/) (2025)
- [DoomArena: A framework for Testing AI Agents Against Evolving Security Threats](https://mlsanity.com/paper/2504.14064/) (2025)
- [Towards Enterprise-Ready Computer Using Generalist Agent](https://mlsanity.com/paper/2503.01861/) (2025)
- [R2D2: Remembering, Replaying and Dynamic Decision Making with a Reflective Agentic Memory](https://mlsanity.com/paper/2501.12485/) (2025)

## Built on by (most cited first, 16 of 16)

- [The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis](https://mlsanity.com/paper/2602.10453/) (2026)
- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation](https://mlsanity.com/paper/2604.00892/) (2026)
- [Nested Browser-Use Learning for Agentic Information Seeking](https://mlsanity.com/paper/2512.23647/) (2025)
- [Read More, Think More: Revisiting Observation Reduction for Web Agents](https://mlsanity.com/paper/2604.01535/) (2026)
- [Diagnosing and Mitigating Context Rot in Long-horizon Search](https://mlsanity.com/paper/2606.29718/) (2026)
- [Squeez: Task-Conditioned Tool-Output Pruning for Coding Agents](https://mlsanity.com/paper/2604.04979/) (2026)
- [Region4Web: Rethinking Observation Space Granularity for Web Agents](https://mlsanity.com/paper/2605.07134/) (2026)
- [MalURLBench: A Benchmark Evaluating Agents' Vulnerabilities When Processing Web URLs](https://mlsanity.com/paper/2601.18113/) (2026)
- [ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction](https://mlsanity.com/paper/2605.11212/) (2026)
- [WebChallenger: A Reliable and Efficient Generalist Web Agent](https://mlsanity.com/paper/2606.10423/) (2026)
- [Weasel: Out-of-Domain Generalization for Web Agents via Importance-Diversity Data Selection](https://mlsanity.com/paper/2605.20291/) (2026)
- [Revisiting Observation Reduction for Web Agents: Comprehensive Evaluation with a Lightweight Framework](https://mlsanity.com/paper/2605.29397/) (2026)
- [Signal-Driven Observation for Long-Horizon Web Agents](https://mlsanity.com/paper/2606.06708/) (2026)
- [Learning Simple Test-Time Environments for LLM Web Agents](https://mlsanity.com/paper/2608.29305/) (2026)
- [Efficient GUI Agents: A Systems Survey of Observation, Memory, Action, and Runtime Optimization](https://mlsanity.com/paper/2609.02309/) (2026)

## Abstract (arXiv)

Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This saturates context limits and increases computational cost processing; moreover, processing full pages exposes agents to security risks such as prompt injection. Existing pruning strategies either discard relevant content or retain irrelevant context, leading to suboptimal action prediction. We introduce FocusAgent, a simple yet effective approach that leverages a lightweight LLM retriever to extract the most relevant lines from accessibility tree (AxTree) observations, guided by task goals. By pruning noisy and irrelevant content, FocusAgent enables efficient reasoning while reducing vulnerability to injection attacks. Experiments on WorkArena and WebArena benchmarks show that FocusAgent matches the performance of strong baselines, while reducing observation size by over 50%. Furthermore, a variant of FocusAgent significantly reduces the success rate of prompt-injection attacks, including banner and pop-up attacks, while maintaining task success performance in attack-free settings. Our results highlight that targeted LLM-based retrieval is a practical and robust strategy for building web agents that are efficient, effective, and secure.