arXiv:2608.06704cs.AIcs.HC2026-08

让网页助手真正听话:用意图合约规范操作边界,确保行为符合用户要求。

WebRider: Persona-Conditioned Intent Controllers for Live-Web Assistance

论文配图:WebRider: Persona-Conditioned Intent Controllers for Live-Web Assistance
图 1 · 摘自论文原文
  • 提出意图合约,明确定义任务目标、约束和用户偏好
  • 99.2%任务完成率但仅38.8%遵守全部规则,揭示当前系统漏洞
  • 适合需要可审计、可解释的自动化网页操作场景

委托网页任务不仅需正确答案,更需传递完整执行策略:验证什么、如何应对不确定性、哪些偏好重要、何时停止。然而现有实时网页代理仅评估最终结果,忽视定义委托行为的策略约束。一个看似正确的答案可能隐藏策略违规。全面审计显示,强控制器完成99.2%任务,但仅在38.8%情况下完全遵守策略。完成不等于合规。WebRider通过意图合约形式化委托策略——包含目标、约束、证据义务、答案格式及任务本地人格控制,即使页面变化也必须保持。系统采用分层架构:顶层控制器维护合约,中层生成受保护的可执行动作,底层通过浏览器、搜索、地图工具执行。基准测试RiderBench在42个公开网站上评估4,096个实时合约,同时审计内部合约状态与用户可见体验,判断策略是否保留且行为是否符合人格设定。受保护的中间接口亦提供高质量训练信号;基于该接口训练的8B动作策略模型,在固定控制器下优于仅依赖可执行动作的基线。通过将浏览路径视为第一类对象,WebRider实现可审计、可人工判断、可学习的系统,不混淆动作执行与最终决策。

原文摘要 · Abstract (English)

Delegating a web task involves more than asking a question; it requires transferring a policy: what to verify, how to handle uncertainty, which preferences matter, and when to stop. Yet, current live-web agents are evaluated solely on the final answer, ignoring the policy constraints that define the delegation. A plausible final answer can conceal violations of that policy. Our full live audit reveals this critical gap: a strong controller completes 99.2% of tasks but honors all policy constraints in only 38.8% of cases. Finishing does not imply fidelity. WebRider bridges this gap by formalizing the delegated policy as an intent contract---an operational record of goals, constraints, evidence obligations, answer form, and task-local persona controls that must hold even as web pages change. WebRider employs a hierarchical architecture: a top-layer controller maintains the contract, a middle layer realizes intentions as guarded executable actions, and a tool layer executes these actions via browser, search, and maps tools. Our benchmark, RiderBench, evaluates this design on 4,096 live-web contracts across 42 public websites, auditing both the internal contract state and the visible user experience to determine if a rollout preserved its policy and if the steps were persona-consistent. The guarded middle interface also serves as a high-quality training signal; an 8B action-policy model trained through this interface outperforms executable-only baselines under a fixed controller. By making the browsing path a first-class object, WebRider enables a system that is auditable, human-judgeable, and learnable without conflating action realization with final-answer decisions. Dataset URL: hf.co/datasets/WebRider/WebRider.

网页代理意图合约可审计

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