ShopGym introduces ShopArena to convert live storefronts into self-contained sandbox shops and ShopGuru to synthesize 224 benchmark tasks, with validation showing structural preservation and positive correlation of agent performance between synthetic and live shops.
Deepshop: A benchmark for deep research shopping agents.ArXiv, abs/2506.02839
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representative citing papers
RecoAtlas is a benchmark that evaluates LLM recommendation agents on behavior-grounded metrics for relevance, complementarity, and diversity in addition to semantic coherence.
Weblica scales RL training for visual web agents by building thousands of reproducible environments through HTTP caching for stable replays and LLM synthesis from real sites, yielding an 8B model that beats similar open baselines on navigation benchmarks.
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
RAG, MCP, and NLWeb interfaces let LLM web agents achieve higher F1 scores (0.75-0.77 vs 0.67) and much lower token usage and runtime than HTML in controlled e-commerce tasks.
WebMall is the first offline multi-shop benchmark for evaluating LLM web agents on complex comparison shopping tasks across heterogeneous product data from multiple simulated e-shops.
SnapGuard detects prompt injection attacks on screenshot-based web agents via visual stability indicators and contrast-polarity textual signals, reaching F1 0.75 while running 8x faster than GPT-4o with no added memory cost.
The paper proposes Amazon-Bench, a functionality-grounded benchmark for web agents in e-commerce that generates diverse task queries from webpage elements and evaluates both task performance and safety risks.
citing papers explorer
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ShopGym: An Integrated Framework for Realistic Simulation and Scalable Benchmarking of E-Commerce Web Agents
ShopGym introduces ShopArena to convert live storefronts into self-contained sandbox shops and ShopGuru to synthesize 224 benchmark tasks, with validation showing structural preservation and positive correlation of agent performance between synthetic and live shops.
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RecoAtlas: From Semantic Plausibility to Set-Level Utility in LLM Recommendation Agents
RecoAtlas is a benchmark that evaluates LLM recommendation agents on behavior-grounded metrics for relevance, complementarity, and diversity in addition to semantic coherence.
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Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
Weblica scales RL training for visual web agents by building thousands of reproducible environments through HTTP caching for stable replays and LLM synthesis from real sites, yielding an 8B model that beats similar open baselines on navigation benchmarks.
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MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
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MCP vs RAG vs NLWeb vs HTML: A Comparison of the Effectiveness and Efficiency of Different Agent Interfaces to the Web (Technical Report)
RAG, MCP, and NLWeb interfaces let LLM web agents achieve higher F1 scores (0.75-0.77 vs 0.67) and much lower token usage and runtime than HTML in controlled e-commerce tasks.
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WebMall -- A Multi-Shop Benchmark for Evaluating Web Agents
WebMall is the first offline multi-shop benchmark for evaluating LLM web agents on complex comparison shopping tasks across heterogeneous product data from multiple simulated e-shops.
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SnapGuard: Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
SnapGuard detects prompt injection attacks on screenshot-based web agents via visual stability indicators and contrast-polarity textual signals, reaching F1 0.75 while running 8x faster than GPT-4o with no added memory cost.
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A Functionality-Grounded Benchmark for Evaluating Web Agents in E-commerce Domains
The paper proposes Amazon-Bench, a functionality-grounded benchmark for web agents in e-commerce that generates diverse task queries from webpage elements and evaluates both task performance and safety risks.