# HANSEL: Extracting Breadcrumbs from Web Agent Trajectories for Interactive Verification arXiv: https://arxiv.org/abs/2606.18671 ยท HTML: https://mlsanity.com/paper/2606.18671/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. - Builds on 28 of its 54 references that are in the map. - First posted on arXiv 17 Jun 2026. ## Placed in - LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/) ## Builds on (most cited first, 28 of 28) - [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021) - [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://mlsanity.com/paper/2207.01206/) (2022) - [Mind2Web: Towards a Generalist Agent for the Web](https://mlsanity.com/paper/2306.06070/) (2023) - [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023) - [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023) - [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://mlsanity.com/paper/2401.01614/) (2024) - [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020) - [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](https://mlsanity.com/paper/2110.01691/) (2021) - [An Illusion of Progress? Assessing the Current State of Web Agents](https://mlsanity.com/paper/2504.01382/) (2025) - [AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?](https://mlsanity.com/paper/2407.15711/) (2024) - [AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials](https://mlsanity.com/paper/2412.09605/) (2024) - [AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories](https://mlsanity.com/paper/2504.08942/) (2025) - [Are self-explanations from Large Language Models faithful?](https://mlsanity.com/paper/2401.07927/) (2024) - [Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation](https://mlsanity.com/paper/2510.11977/) (2025) - [On the Planning Abilities of Large Language Models (A Critical Investigation with a Proposed Benchmark)](https://mlsanity.com/paper/2302.06706/) (2023) - [Interactive Debugging and Steering of Multi-Agent AI Systems](https://mlsanity.com/paper/2503.02068/) (2025) - [Learning to Navigate the Web](https://mlsanity.com/paper/1812.09195/) (2018) - [Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant](https://mlsanity.com/paper/2502.01390/) (2025) - [Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning](https://mlsanity.com/paper/2202.05402/) (2022) - [Magentic-UI: Towards Human-in-the-loop Agentic Systems](https://mlsanity.com/paper/2507.22358/) (2025) - [Cocoa: Co-Planning and Co-Execution with AI Agents](https://mlsanity.com/paper/2412.10999/) (2024) - [Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability](https://mlsanity.com/paper/1902.03245/) (2019) - [CowPilot: A Framework for Autonomous and Human-Agent Collaborative Web Navigation](https://mlsanity.com/paper/2501.16609/) (2025) - [Chain-of-Thought Unfaithfulness as Disguised Accuracy](https://mlsanity.com/paper/2402.14897/) (2024) - [Morae: Proactively Pausing UI Agents for User Choices](https://mlsanity.com/paper/2508.21456/) (2025) - [Evaluating Long-Context Reasoning in LLM-Based WebAgents](https://mlsanity.com/paper/2512.04307/) (2025) - [Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces](https://mlsanity.com/paper/2510.22922/) (2025) - [XAgen: An Explainability Tool for Identifying and Correcting Failures in Multi-Agent Workflows](https://mlsanity.com/paper/2512.17896/) (2025) ## Built on by (most cited first, 1 of 1) - [Plans They Abandon, Reports They Author: The Narrative Layer of Autonomous Agents](https://mlsanity.com/paper/2609.12205/) (2026) ## Abstract (arXiv) AI web agents can perform complex, multi-step tasks such as searching for products, comparing options, and making purchases on behalf of users. However, verifying the correctness of an agent's output remains difficult. Existing transparency mechanisms, including full trajectory logs, source links, screenshots, and LLM-generated summaries, treat verification as a passive reading task, leaving users to sift through overwhelming logs or trust potentially unfaithful explanations. We present HANSEL (Highlighting Agent Navigation Steps as Evidence Links), a system that extracts interactive, verifiable evidence from web-agent trajectories. Given an agent trajectory, HANSEL extracts evidence pages and snippets and presents them as navigable, interactive views with relevant page state preserved (e.g., applied filters, search queries, and scroll positions), enabling users to verify how the agent arrived at its answer. When the agent's answer cannot be traced to any visited page, HANSEL explicitly flags this gap. A technical evaluation on 45 tasks from AssistantBench and Online-Mind2Web shows that HANSEL achieves 83.7% precision and 88.8% recall in identifying evidence pages, while reducing trajectory volume by 61.6%. In a controlled user study with 14 participants, HANSEL significantly reduced task completion time and perceived effort compared to a standard agent interface, while participants rated it significantly higher on usability, verification ease, and error identification. Our results demonstrate that reframing verification as an interactive activity, rather than passive consumption of agent explanations, leads to more efficient human oversight of AI agents.