# RecVerse：面向真实购物行为保真模拟的 GUI 智能体

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-08-21 08:00
- AIHOT 分数：52
- AIHOT 链接：https://aihot.virxact.com/items/cmt6psf3m1fivro73rrso13rv
- 原文链接：https://arxiv.org/abs/2608.20707

## AI 摘要

RecVerse 是一款基于 GUI 的购物行为模拟智能体，通过截图感知页面并生成多轮轨迹，以解决现有模拟器在长会话记忆与优化目标上的两大挑战。它采用认知启发的分层记忆（工作记忆、情景记忆、偏好记忆），并以轨迹级强化学习目标优化整个会话，使行为分布与购物意图更贴近真实用户。

## 正文

Simulating realistic user shopping behavior underpins offline evaluation and reinforcement learning in e-commerce scenarios. While recent LLM- and VLM-based simulators have made encouraging progress, reproducing a real browsing session remains difficult for two reasons. (i) Memory Challenge: a shopping session spans dozens of pages, yet existing agents either discard long-range observation histories, losing the evolving user state, or naively concatenate them, overwhelming the context window and even degrading simulation quality. (ii) Optimization Challenge: current user simulators are typically supervised to match each logged action via imitation or step-level rewards; the resulting sessions often display unrealistic patterns, such as over-exploration or excessive passivity, which per-step supervision can neither detect nor correct. To address the above challenges, we present RecVerse, a GUI-grounded simulation agent that perceives pages through screenshots and produces faithful multi-turn trajectories. For the memory challenge, RecVerse adopts a cognitive-inspired hierarchical memory: Working Memory for short-term focus, Episodic Memory for in-session traces, and Preference Memory for high-level intent, with memory updates treated as actions so that the agent adaptively learns when and what to memorize. For the optimization challenge, RecVerse is optimized with a trajectory-level RL objective that scores entire sessions, aligning both macro-level action-type distributions and micro-level shopping intent with real users. We further release USB (User Simulation Benchmark), an interactive e-commerce GUI trajectory dataset for multi-turn user simulation. Experiments show that RecVerse significantly outperforms existing baselines in both behavioral fidelity and intent consistency.