<｜begin▁of▁sentence｜><｜User｜>

## Tools

You have access to a set of tools you can use to answer the user's question.
You can invoke functions by writing a "<｜DSML｜function_calls>" block like the following as part of your reply to the user:
<｜DSML｜function_calls>
<｜DSML｜invoke name="$FUNCTION_NAME">
<｜DSML｜parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</｜DSML｜parameter>
...
</｜DSML｜invoke>
<｜DSML｜invoke name="$FUNCTION_NAME2">
...
</｜DSML｜invoke>
</｜DSML｜function_calls>

String and scalar parameters should be specified as is without any escaping or quotes, while lists and objects should use JSON format. The "string" attribute should be set to "true" for string type parameters and "false" for other types (numbers, booleans, arrays, objects).

If the thinking_mode is enabled, then after function results you should strongly consider outputting a thinking block. Here is an example:

<｜DSML｜function_calls>
...
</｜DSML｜function_calls>

<function_results>
...
</function_results>

<think>...thinking about results</think>

Here are the functions available in JSONSchema format:
<functions>
{"name": "search", "description": "Searches for information related to query and displays topn results.", "parameters": {"type": "object", "properties": {"query": {"type": "string", "description": "The search query string"}, "topn": {"type": "integer", "description": "Number of top results to display", "default": 10}, "source": {"type": "string", "description": "Source to search within", "enum": ["web", "news"], "default": "web"}}, "required": ["query"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
{"name": "open", "description": "Opens the link id from the page indicated by cursor starting at line number loc, showing num_lines lines. Valid link ids are displayed with the formatting: 【{id}†.*】. If cursor is not provided, the most recent page is implied. If id is a string, it is treated as a fully qualified URL associated with source. If loc is not provided, the viewport will be positioned at the beginning of the document or centered on the most relevant passage, if available. Use this function without id to scroll to a new location of an opened page.", "parameters": {"type": "object", "properties": {"id": {"description": "Link ID (number) or fully qualified URL (string)", "anyOf": [{"type": "integer"}, {"type": "string"}], "default": -1}, "cursor": {"type": "integer", "description": "Page cursor indicator", "default": -1}, "loc": {"type": "integer", "description": "Starting line number", "default": -1}, "num_lines": {"type": "integer", "description": "Number of lines to show", "default": -1}, "view_source": {"type": "boolean", "description": "Whether to view source", "default": false}, "source": {"type": "string", "description": "Source associated with the URL", "enum": ["web", "news"], "default": "web"}}, "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
{"name": "find", "description": "Finds exact matches of pattern in the current page, or the page given by cursor.", "parameters": {"type": "object", "properties": {"pattern": {"type": "string", "description": "The exact pattern to search for"}, "cursor": {"type": "integer", "description": "Page cursor to search in", "default": -1}}, "required": ["pattern"], "additionalProperties": false, "$schema": "http://json-schema.org/draft-07/schema#"}}
</functions>


# The user's message is: 帮我调研一下，目前有哪些针对search agent的benchmark？详细介绍各自的特点、使用场景、例题。

<system-reminder>
## Today’s Date
2025-11-27, Thursday.
</system-reminder><｜Assistant｜><think>用户想调研目前针对搜索智能体（search agent）的基准测试（benchmark），要求详细介绍各自的特点、使用场景和例题。我需要提供一份全面的概述，涵盖现有的基准测试，例如WebGPT、WebShop、ALFWorld、HotpotQA、FEVER、TriviaQA、Natural Questions、SearchQA、MS MARCO、BEIR、LoTTE、MIRACL、Zero-shot IR基准等。同时，需要区分这些基准测试是针对检索任务、问答任务，还是具体针对搜索智能体的评估。此外，还应考虑那些专门为评估搜索智能体（即能够与环境交互、执行搜索、浏览网页、使用工具的智能体）而设计的基准测试，例如WebGPT、WebShop、ALFWorld、Mind2Web、WebArena、VisualWebArena、SearchBench、WebVLN、WebNav等。我需要详细介绍每个基准测试的特点、使用场景，并举例说明。为了确保信息的全面性和时效性，我将同时进行两项搜索：一项是针对“search agent benchmark”的通用搜索，以获取广泛的基准测试信息；另一项是针对“搜索智能体 基准测试”的中文搜索，以获取更符合用户语言习惯和可能更具体的中文资料。</think>

<｜DSML｜function_calls>
<｜DSML｜invoke name="search">
<｜DSML｜parameter name="query" string="true">search agent benchmark 2024</｜DSML｜parameter>
<｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter>
<｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter>
</｜DSML｜invoke>
<｜DSML｜invoke name="search">
<｜DSML｜parameter name="query" string="true">搜索智能体 基准测试</｜DSML｜parameter>
<｜DSML｜parameter name="topn" string="false">10</｜DSML｜parameter>
<｜DSML｜parameter name="source" string="true">web</｜DSML｜parameter>
</｜DSML｜invoke>
</｜DSML｜function_calls><｜end▁of▁sentence｜>

<function_results>
<result>[0] search agent benchmark 2024 (Search_Results/search agent benchmark 2024)
**viewing lines [0 - 39] of 39**

L0: 
L1: URL: Search_Results/search agent benchmark 2024
L2: # Search Results
L3: 
L4:   * 【0†HSCodeComp: A Realistic and Expert-level Benchmark for ...; publish_date:
L5:  none†arxiv.org】 Oct 22, 2025 — To fill this gap, we introduce HSCodeComp, the 
L6: first realistic, expert-level e-commerce benchmark designed to evaluate deep 
L7: search agents in ...
L8:   * 【1†open-compass/GTA - A Benchmark for General Tool Agents; publish_date: 
L9: none†github.com】 GTA is a benchmark to evaluate the tool-use capability of LLM-
L10: based agents in real-world scenarios. It features three main aspects.
L11:   * 【2†Benchmarking real-time trust scoring across five AI Agent ...; 
L12: publish_date: none†cleanlab.ai】 Aug 20, 2025 — This article evaluates 5 AI Agent
L13:  architectures over the BOLAA (ICLR 2024) benchmark, and assesses the effects of
L14:  adding automated trust ...
L15:   * 【3†10 AI agent benchmarks; publish_date: none†www.evidentlyai.com】 Jul 11, 
L16: 2025 — We put together 10 AI agent benchmarks designed to assess how well 
L17: different LLMs perform as agents in real-world scenarios, ...
L18:   * 【4†A state-of-the-art search API purpose-built for agents; publish_date: 
L19: none†parallel.ai】 Jul 31, 2025 — To evaluate real-world performance of the 
L20: Parallel Search MCP Server, we created the WISER-Search benchmark which blends 
L21: WISER-Fresh (queries ...
L22:   * 【5†AI Agent Benchmarks are Broken; publish_date: none†medium.com】 We break 
L23: down the failure modes in current AI agent benchmarks and introduce a checklist 
L24: that minimizes the gamability of AI agent benchmarks.
L25:   * 【6†Benchmarks and Tree Search for Multimodal LLM Web Agents; publish_date: 
L26: none†dpfried.github.io】 2024, When is Tree Search Useful? ○ Dealing with 
L27: destructive actions. ○ Some things on the web are very difficult to undo, e.g., 
L28: ordering an item. 56.
L29:   * 【7†-Bench: Benchmarking AI agents for the real-world; publish_date: 
L30: none†sierra.ai】 Jun 20, 2024 — τ-bench measures an agent's ability to interact 
L31: with (simulated) human users and programmatic APIs while following domain-
L32: specific policies in a consistent ...
L33:   * 【8†Browser Use = state of the art Web Agent; publish_date: none†browser-
L34: use.com】 Dec 15, 2024 — Browser Use has achieved state-of-the-art performance on
L35:  the WebVoyager benchmark, with an impressive 89.1% success rate across 586 
L36: diverse web tasks.
L37:   * 【9†FutureSearch Benchmarks; publish_date: none†evals.futuresearch.ai】 Find 
L38: the original source of a given claim. Example: From , more than 8 out of 1000 
L39: users clicked on a phishing link monthly in 2024, up 190% vs 2023.</result>
<result>[1] 搜索智能体 基准测试 (Search_Results/搜索智能体 基准测试)
**viewing lines [0 - 33] of 33**

L0: 
L1: URL: Search_Results/搜索智能体 基准测试
L2: # Search Results
L3: 
L4:   * 【0†WideSearch：揭示AI 智能体缺失的「广度」能力; publish_date: none†zhuanlan.zhihu.com】 Aug 
L5: 16, 2025 — 为系统评估智能体在该任务上的能力，论文构建了第一个专门的基准测试 WideSearch ，包含200 个源于真实世界、横跨18 
L6: 个领域的高质量任务。 通过对超过10 个 ...
L7:   * 【1†GAIA: 一个严苛的智能体基准- HuggingFace; publish_date: none†www.cnblogs.com】 Jul 9,
L8:  2024 — 我们使用一个用库构建的代码智能体 在GAIA 基准上进行测试，这可以说是最困难、最全面的智能体基准测试……最终我们取得了第一名的成绩！ 
L9: GAIA: 一个严苛的 ...
L10:   * 【2†AI搜索智能体遭遇新挑战：滑铁卢大学团队提出更公平透明的 ...; publish_date: none†www.techwalker.com】 
L11: Aug 14, 2025 — 
L12: 目前评测AI搜索智能体主要依靠BrowseComp这样的基准测试，它就像一场实时的开卷考试，让AI在真实的网络环境中搜索信息来回答复杂问题。听起来很合理 ...
L13:   * 【3†Agentic AI基础设施实践经验系列（六）：Agent质量评估 - AWS; publish_date: 
L14: none†aws.amazon.com】 Sep 19, 2025 — TAU-bench 
L15: 是一个评估AI智能体在真实世界环境中可靠性的基准测试。它评估智能体是否能够在动态的多轮对话中与用户进行交互，理解需求并完成任务。T-bench ...
L16:   * 【4†DeepAgent：能自己找工具的通用推理智能体 - 高瓴人工智能学院; publish_date: none†ai.ruc.edu.cn】 
L17: Nov 6, 2025 — 在八大基准测试中，DeepAgent在绝大多数任务上全面领先所有基线模型。 
L18: 开放环境优势:在最具挑战的“开放工具检索”场景下(如ToolBench)，其成功率达到64%，远 ...
L19:   * 【5†BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试; publish_date: none†blog.csdn.net】 Sep 
L20: 22, 2025 — 该基准测试由OpenAI团队开发，旨在推动更可信赖和可靠的AI代理研究。 核心特点. 
L21: 挑战性问题设计：BrowseComp的问题设计遵循严格的难度标准：. 人类创建者确保 ...
L22:   * 【6†什么是GAIA？; publish_date: none†huggingface.co】 什么是GAIA？ GAIA 
L23: 是一个用于评估AI助手在需要核心能力组合的真实世界任务上的表现的基准，这些核心能力包括推理、多模态理解、网页浏览和熟练的工具使用。
L24:   * 【7†SuperCLUE-Agent: Agent智能体中文原生任务能力测评基准; publish_date: 
L25: none†www.cluebenchmarks.com】 AI 
L26: Agent是一个能够自主理解、规划决策和执行复杂任务的智能体，有代表性的Agent比如AutoGPT、AgentGPT等。 
L27: 现有关于Agent能力的测评，主要是在英文场景或任务的测评。目前还 ...
L28:   * 【8†Gaia2 与ARE：赋能社区的智能体评测; publish_date: none†hub.baai.ac.cn】 Sep 24, 2025 — 
L29: 快速评估任意智能体：基于真实或模拟数据，测试不同规则、工具、内容和验证方式下的表现 · 测试智能体的 工具调用与编排能力：可结合本地应用或MCP 工具 · 生成
L30:  ...
L31:   * 【9†许多AI 智能体评测基准并不可靠; publish_date: none†my.oschina.net】 Aug 13, 2025 — 
L32: 当前AI智能体基准测试存在严重缺陷，亟需更严谨的评估框架。作者提出AI智能体基准测试核查清单（ABC），包含43个项目，旨在识别基准测试潜在陷阱，构建可靠评估 
L33: ...</result>
</function_results>

<think>第一个搜索结果（针对“search agent benchmark 2024”）显示了一些相关条目，例如“HSCodeComp: A Realistic and Expert-level Benchmark for ...”、“open-compass/GTA - A Benchmark for General Tool Agents”、“10 AI agent benchmarks”、“WISER-Search benchmark”、“Benchmarks and Tree Search for Multimodal LLM Web Agents”、“τ-bench”、“WebVoyager benchmark”、“FutureSearch Benchmarks”。这些可能都与搜索智能体相关。第二个搜索结果（针对“搜索智能体 基准测试”）显示了一些中文资料，例如“WideSearch”、“BrowseComp”、“GAIA”、“SuperCLUE-Agent”等。这些都可能与搜索智能体相关。为了全面了解这些基准测试，我们需要同时打开并分析其中几个最有潜力的结果。具体来说，我们将打开第一个搜索结果中的“HSCodeComp”（索引0）和“open-compass/GTA”（索引1），以及第二个搜索结果中的“WideSearch”（索引0）和“BrowseComp”（索引5）。</think>

<｜DSML｜function_calls>
<｜DSML｜invoke name="open">
<｜DSML｜parameter name="cursor" string="false">0</｜DSML｜parameter>
<｜DSML｜parameter name="id" string="false">0</｜DSML｜parameter>
</｜DSML｜invoke>
<｜DSML｜invoke name="open">
<｜DSML｜parameter name="cursor" string="false">0</｜DSML｜parameter>
<｜DSML｜parameter name="id" string="false">1</｜DSML｜parameter>
</｜DSML｜invoke>
<｜DSML｜invoke name="open">
<｜DSML｜parameter name="cursor" string="false">1</｜DSML｜parameter>
<｜DSML｜parameter name="id" string="false">0</｜DSML｜parameter>
</｜DSML｜invoke>
<｜DSML｜invoke name="open">
<｜DSML｜parameter name="cursor" string="false">1</｜DSML｜parameter>
<｜DSML｜parameter name="id" string="false">5</｜DSML｜parameter>
</｜DSML｜invoke>
</｜DSML｜function_calls><｜end▁of▁sentence｜>

<function_results>
<result>[2]  (https://ar5iv.org/html/2510.19631v1)
**viewing lines [0 - 119] of 983**

L0: 
L1: URL: https://ar5iv.org/html/2510.19631v1
L2: # HSCodeComp: A Realistic and Expert-level Benchmark for Deep Search Agents in 
L3: Hierarchical Rule Application
L4: 
L5: Yiqian Yang† Tian Lan† Qianghuai Jia∗ Li Zhu Hui Jiang Hang Zhu Longyue Wang 
L6: Weihua Luo Kaifu Zhang
L7: 
L8: Alibaba International Digital Commerce∗* Corresponding Author: Qianghuai Jia 
L9: (qianghuai.jqh@alibaba-inc.com)
L10: †\dagger Equal Contribution: Yiqian Yang
L11: 
L12:  Tian Lan
L13: 
L14: ###### Abstract
L15: 
L16: Abstract
L17: 
L18: Effective deep search agents must not only access open-domain and domain-
L19: specific knowledge but also apply complex rules—such as legal clauses, medical 
L20: manuals and tariff rules. These rules often feature vague boundaries and 
L21: implicit logic relationships, making precise application challenging for agents.
L22:  However, this critical capability is largely overlooked by current agent 
L23: benchmarks. To fill this gap, we introduce HSCodeComp, the first realistic, 
L24: expert-level e-commerce benchmark designed to evaluate deep search agents in 
L25: hierarchical rule application. In this task, the deep reasoning process of 
L26: agents is guided by these rules to predict 10-digit Harmonized System Code 
L27: (HSCode) of products with noisy but realistic descriptions. These codes, 
L28: established by the World Customs Organization, are vital for global supply chain
L29:  efficiency. Built from real-world data collected from large-scale e-commerce 
L30: platforms, our proposed HSCodeComp comprises 632 product entries spanning 
L31: diverse product categories, with these HSCodes annotated by several human 
L32: experts. Extensive experimental results on several state-of-the-art LLMs, open-
L33: source, and closed-source agents reveal a huge performance gap: best agent 
L34: achieves only 46.8% 10-digit accuracy, far below human experts at 95.0%. 
L35: Besides, detailed analysis demonstrates the challenges of hierarchical rule 
L36: application, and test-time scaling fails to improve performance further.
L37: 
L38: ## 1 Introduction
L39: 
L40: Deep search agents have demonstrated significant value in solving complex real-
L41: world problems, where robust external knowledge utilization constitutes a 
L42: critical capability [Wu et al., 2025, Tao et al., 2025, Li et al., 2025b]. To 
L43: evaluate this capability, numerous established benchmarks are proposed to assess
L44:  agents in utilizing open-domain data (e.g., GAIA [Mialon et al., 2023b] and 
L45: BrowseComp [Wei et al., 2025]) and domain-specific data (e.g., WebMall [Peeters 
L46: et al., 2025a], FinSearchComp [Hu et al., 2025a] and MedBrowseComp [Yu et al., 
L47: 2025b]).
L48: 
L49: Beyond open-domain and domain-specific data, agents also need to effectively 
L50: apply rules that encode human expert knowledge, particularly in scenarios like 
L51: law, medical and e-commerce [Li et al., 2025a, Chen et al., 2025b, Yao et al., 
L52: 2022, Chollet et al., 2025]. For instance, legal case adjudication require 
L53: interpreting abstract legal provisions, and accurate e-commerce product 
L54: classification in depends on tariff rules [Grainger, 2024]. Previous works have 
L55: defined rule application as using specific logical rules with supporting facts 
L56: to derive conclusions [Wang et al., 2024, Servantez et al., 2024]. In contrast, 
L57: we define it as a core capability for deep search agents, where human-written 
L58: rules are systematically applied to guide complex reasoning and decision-making 
L59: [Sadowski and Chudziak, 2025]. Building on this observation, we categorize 
L60: knowledge data for deep search agents into three levels (Figure 1, left), with 
L61: increasing knowledge complexity: (1) Level 1: Open-domain Data - Tests 
L62: understanding and deep reasoning abilities of agents on long-form web content. 
L63: Established benchmarks include GAIA [Mialon et al., 2023b] and BrowseComp [Wei 
L64: et al., 2025]; (2) Level 2: Structured Data - Assesses agents to precisely 
L65: utilize structured data such as databases and knowledge graphs, as seen in 
L66: domain-specific benchmarks like WebMall [Peeters et al., 2025a], MedBrowseComp 
L67: [Chen et al., 2025b] and FinSearchComp [Hu et al., 2025a]; (3) Level 3: Rule 
L68: Data - Evaluates agents to apply complex and abstract rules [Chollet et al., 
L69: 2025]. This level presents two key challenges: (a) making accurate decisions 
L70: when rules contain vague natural language descriptions [Sadowski and Chudziak, 
L71: 2025]; and (b) reasoning about logical dependencies among rules, such as 
L72: exception clauses and cross-category relationships [Guha et al., 2023]. Despite 
L73: the importance of rule application in real-world scenarios, current agent 
L74: benchmarks largely overlook its evaluation.
L75: 
L76: To fill this gap, we introduce HSCodeComp (short for the Harmonized System Code 
L77: (HSCode) Competition), the first realistic, expert-level e-commerce benchmark 
L78: designed to evaluate agents in predicting complete 10-digit Harmonized System 
L79: Code (HSCode) of the product, using hierarchical rules (e.g., eWTP tariff 
L80: rules111https://www.ewtp.com/web/smart/hscode). HSCodes organize products 
L81: through a hierarchical structure spanning over 5,000 distinct codes across 
L82: multiple classification levels, representing the global standard for classifying
L83:  traded international goods, established by the World Customs Organization and 
L84: implemented across more than 200 countries for customs clearance and tariff 
L85: determination [Grainger, 2024, Nath et al., 2025]. Built from the data of the 
L86: large-scale e-commerce platforms, our proposed HSCodeComp comprises 632 
L87: carefully curated product entries, encompassing 27 unique HS chapters and 32 
L88: distinct first-level categories. These HSCodes have been rigorously annotated by
L89:  multiple e-commerce domain experts, ensuring that HSCodeComp is expert-level. 
L90: Accurately predicting the exact 10-digit HSCode presents significant challenges:
L91:  agents must perform multi-hop hierarchical reasoning with complex tariff rules 
L92: while processing noisy but realistic product descriptions that often contain 
L93: abbreviations, language variations, or incomplete information.
L94: 
L95: Extensive experiments on the state-of-the-art baselines, including 14 advanced 
L96: foundation models, 6 advanced open-source agent systems and 3 closed-source 
L97: agent systems, demonstrate that HSCode prediction task remains a substantial 
L98: challenge for current AI approaches. As shown in the Figure 1 (right), even the 
L99: best-performing system (SmolAgent [Roucher et al., 2025] with GPT-5) achieves 
L100: only 46.8% accuracy, substantially below the 95.0% accuracy attained by human 
L101: experts. Further detailed analysis reveals that existing agent systems lack 
L102: critical capabilities required for this complex hierarchical rule applications. 
L103: Notably, test-time scaling approach—which has proven effective in other 
L104: reasoning tasks [Guo et al., 2025, Liu et al., 2025]—fail to improve performance
L105:  on HSCodeComp. These observations demonstrate the challenging nature of our 
L106: proposed HSCodeComp, highlighting the need for more effective designs of agent 
L107: systems. To facilitate future research, we will publicly release codes and the 
L108: benchmark dataset of HSCodeComp.
L109: 
L110: ## 2 Related Works
L111: 
L112: ### 2.1 Previous Works in HSCode Prediction
L113: 
L114: Previous works treat HSCode prediction as the e-commerce text classification 
L115: task [Grainger, 2024], using pre-trained BERT models [Liao et al., 2024, Shubham
L116:  et al., 2022] or Large Language Models (LLMs) prompting [Hussain and Ahmed, 
L117: 2023]. However, these approaches fail to leverage domain-specific knowledge, 
L118: especially the rules written by human experts [Hussain and Ahmed, 2023, Judy, 
L119: 2024]. Besides, existing HSCode benchmarks face two critical limitations [Judy, </result>
<result>Error fetching URL `https://github.com/open-compass/GTA`</result>
<result>Error fetching URL `https://zhuanlan.zhihu.com/p/1939316761775301093`</result>
<result>[3]  (https://blog.csdn.net/qq_41472205/article/details/151974603)
**viewing lines [0 - 167] of 187**

L0: 
L1: URL: https://blog.csdn.net/qq_41472205/article/details/151974603
L2: # BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试
L3: 
L4: BrowseComp：AI浏览能力评估基准
L5: 
L6: 最新推荐文章于 2025-11-12 13:40:20 发布
L7: 
L8: 原创 于 2025-09-22 22:33:04 发布 · 1.3k 阅读
L9: 
L10: · 9
L11: · 25 · 
L12: CC 4.0 BY-SA版权
L13: 
L14: 版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
L15: 
L16: ## BrowseComp：为浏览智能体设计的简单而具挑战性的基准测试
L17: 
L18: 在人工智能从基础聊天机器人向推理器和智能体发展的进程中，具备浏览互联网能力的人工智能模型正变得越来越重要。今天，我们将介绍一个名为BrowseComp的创新基准
L19: 测试，它专门设计用于评估AI代理在复杂网络浏览任务中的能力。
L20: 
L21: ### 什么是BrowseComp？
L22: 
L23: BrowseComp（全称Browsing Competition）是一个包含1,266个挑战性问题的基准测试集，专门用于衡量AI代理在互联网上持续导航、寻找难
L24: 以找到的纠缠信息的能力。该基准测试由OpenAI团队开发，旨在推动更可信赖和可靠的AI代理研究。
L25: 
L26: #### 核心特点
L27: 
L28: 挑战性问题设计：BrowseComp的问题设计遵循严格的难度标准：
L29: 
L30: - 人类创建者确保问题在10分钟内无法被人解决
L31: - 现有模型（包括带浏览功能的ChatGPT和早期版本的OpenAI Deep Research）无法解决
L32: - 通过5次简单Google搜索无法在结果首页找到答案
L33: 
L34: 简单易验证：尽管问题极具挑战性，但答案形式简单——都是短字符串，便于自动验证模型输出的正确性。
L35: 
L36: ### 为什么需要BrowseComp？
L37: 
L38: #### 现有基准的局限性
L39: 
L40: 传统的信息检索基准（如TriviaQA、HotpotQA等）主要关注易于查找的信息，随着语言模型的进步，这些基准已经趋于饱和。而BrowseComp专注于那些需
L41: 要浏览大量网站才能解决的"硬核"问题。
L42: 
L43: #### 模拟真实挑战
L44: 
L45: BrowseComp问题通常采用"逆向设计"方法：创建者从一个已知事实出发，构建一个搜索空间巨大但验证简单的问题。例如：
L46: 
L47: “找出2018-2023年间在EMNLP会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题”
L48: 
L49: 这类问题验证简单，但解决起来需要检查数千篇论文并调查每位作者的背景。
L50: 
L51: ### 数据集特点
L52: 
L53: #### 主题多样性
L54: 
L55: BrowseComp涵盖了广泛的主题领域（如图2所示），包括历史、科学、文化等。创建者被鼓励基于个人兴趣设计问题，这有助于提高数据质量和参与度。
L56: 
L57: #### 质量保证
L58: 
L59: 为确保答案的唯一性，创建者需要：
L60: 
L61: - 对问题内容有足够了解，确信没有其他有效答案
L62: - 如果不确定，则添加更多约束条件
L63: - 接受其他创建者的验证反馈
L64: 
L65: ### 人类表现基准
L66: 
L67: 为了衡量BrowseComp的难度，研究人员让人类创建者尝试解决问题（不能解答自己创建的问题）。结果显示：
L68: 
L69: - **70.8%**的问题在2小时搜索后人类选择放弃
L70: - **29.2%**的问题被成功解决
L71: - 在解决的问题中，**86.4%**的人类答案与参考答案一致
L72: 
L73: 这表明BrowseComp确实极具挑战性，即使是熟悉数据集的人类专家也难以在有限时间内解决大部分问题。
L74: 
L75: ### AI模型表现评估
L76: 
L77: #### 各模型对比
L78: 
L79: 研究人员评估了多种模型在BrowseComp上的表现：
L80: 
L81: 模型 | 准确率(%) | 校准误差(%) 
L82: ---|---|---
L83: GPT-4o | 0.6 | 69 
L84: GPT-4o（带浏览） | 1.9 | 82 
L85: GPT-4.5 | 0.9 | 68 
L86: OpenAI o1 | 9.9 | 65 
L87: Deep Research | 51.5 | 91 
L88: 
L89: #### 关键发现
L90: 
L91: - 基础模型表现不佳：GPT-4o和GPT-4.5准确率接近零，凸显了基准的难度
L92: - 浏览功能带来有限提升：启用浏览功能的GPT-4o准确率略有提高，但仍很低
L93: - 推理能力的重要性：OpenAI o1虽然没有浏览能力，但凭借更强的推理能力获得较高准确率
L94: - 专业模型的优势：专门为持久网络浏览训练的Deep Research模型解决了约一半的问题
L95: 
L96: #### 计算资源与性能关系
L97: 
L98: 研究表明，BrowseComp性能随测试时计算资源的增加而平滑提升（如图1所示）。这与智能体模型的特性一致——更多计算资源允许模型浏览更多网站，从而提高找到正确
L99: 答案的机会。
L100: 
L101: ### 进阶策略分析
L102: 
L103: #### 聚合策略的效果
L104: 
L105: 通过让模型多次尝试同一问题并采用投票策略，可以显著提升性能：
L106: 
L107: - 多数投票：选择样本中最常见的答案
L108: - 加权投票：根据模型置信度加权投票
L109: - 最佳选择：选择置信度最高的答案
L110: 
L111: 这些方法将Deep Research的性能提升了15-25%，表明模型通常能够识别自己的正确答案。
L112: 
L113: #### 任务难度分布
L114: 
L115: 分析显示，BrowseComp中的任务难度分布广泛：
L116: 
L117: - 16%的任务被Deep Research完美解决（100%通过率）
L118: - 14%的任务完全失败（0%通过率）
L119: - 其余任务处于中间难度水平
L120: 
L121: ### BrowseComp的意义与局限性
L122: 
L123: #### 作为评估工具的价值
L124: 
L125: BrowseComp可被视为浏览智能体的"编程竞赛"——虽然不全面，但对核心浏览能力提供了有用的衡量。正如在编程竞赛中表现出色的模型很可能在其他编码任务中表现良
L126: 好，能够解决BrowseComp的模型在定位难以查找信息方面应该具有很强的能力。
L127: 
L128: #### 已知局限性
L129: 
L130: - 不涉及真实用户查询分布：如生成长回答或解决查询歧义等挑战
L131: - 答案唯一性假设：虽然尽力确保，但不能完全保证没有其他有效答案
L132: - 单模态限制：当前仅关注文本信息检索，未来可扩展至图像、视频等模态
L133: 
L134: ### 结论与展望
L135: 
L136: BrowseComp为评估AI代理的网络浏览能力提供了一个具有挑战性且易于使用的基准。它强调的持久性、创造性和战略性浏览能力，正是未来AI智能体在实际应用中所需
L137: 的核心技能。
L138: 
L139: 随着更多研究团队在BrowseComp上测试他们的模型，我们期待看到浏览智能体技术的快速进步，最终使AI能够更可靠地帮助我们获取那些深藏在互联网角落的宝贵信息。
L140: 
L141: 访问地址：BrowseComp已在GitHub上开源，地址为 https://github.com/openai/simple-evals
L142: 
L143: 添加红包
L144: 
L145: 请填写红包祝福语或标题
L146: 
L147: 个
L148: 
L149: 红包个数最小为10个
L150: 
L151: 元
L152: 
L153: 红包金额最低5元
L154: 
L155: 当前余额3.43元 前往充值 >
L156: 
L157: 需支付：10.00元
L158: 
L159: 成就一亿技术人!
L160: 
L161: hope_wisdom
L162:  发出的红包 
L163: 实付元
L164: 
L165: 点击重新获取
L166: 
L167: 扫码支付</result>
</function_results>

<think>打开的HSCodeComp页面详细介绍了这个基准测试，它是一个用于评估深度搜索智能体在分层规则应用方面能力的基准测试，特点包括：基于真实世界电商数据、任务复杂、需要多跳层次推理、使用嘈杂的产品描述，并且标注由专家完成。使用场景是评估智能体在规则应用方面的能力，例如关税代码分类。例题是预测产品的10位HS编码。打开的BrowseComp页面详细介绍了这个基准测试，它是一个用于评估浏览智能体（即能够在互联网上导航、搜索信息的智能体）的基准测试。其特点包括：包含1266个具有挑战性的问题，需要持续导航和搜索难以查找的信息；答案形式简单，便于自动验证；问题设计确保人类在10分钟内无法解决，且现有模型也无法解决；涵盖了广泛的主题领域。使用场景是评估智能体在复杂网络浏览任务中的能力。例题是：“找出2018-2023年间在EMNLP会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题”。另外两个打开的页面（open-compass/GTA和WideSearch）出现了错误，可能链接已失效或需要其他访问方式。为了进一步获取其他相关基准测试的信息，我们需要同时进行多项搜索。考虑到用户可能对搜索智能体基准测试有广泛需求，我们将同时搜索“WebVoyager benchmark”、“WebArena benchmark”、“GAIA benchmark”、“ToolBench benchmark”、“HotpotQA benchmark”、“FEVER benchmark”、“TriviaQA benchmark”、“Natural Questions benchmark”、“MS MARCO benchmark”、“BEIR benchmark”、“MIRACL benchmark”、“Zero-shot IR benchmark”、“WebGPT benchmark”、“WebShop benchmark”、“ALFWorld benchmark”、“Mind2Web benchmark”、“VisualWebArena benchmark”、“SearchBench benchmark”、“WebVLN benchmark”、“WebNav benchmark”。这些搜索将帮助我们全面了解现有基准测试的概况。</think>

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<function_results>
<result>[4] WebVoyager benchmark (Search_Results/WebVoyager benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/WebVoyager benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebVoyager Benchmark; publish_date: none†www.trykura.com】 Performance 
L5: measured across various websites in WebVoyager shows Kura consistently 
L6: outperforming other agents, with particularly strong results on e-commerce ...
L7:   * 【1†WebVoyager: Building an End-to-End Web Agent with ...; publish_date: 
L8: none†github.com】 WebVoyager is an innovative Large Multimodal Model (LMM) 
L9: powered web agent that can complete user instructions end-to-end by interacting 
L10: with real-world ...
L11:   * 【2†AI Browser Agent Leaderboard | Steel.dev; publish_date: 
L12: none†leaderboard.steel.dev】 See how various AI browser agents stack up based on 
L13: their accuracy in completing web-based tasks on the WebVoyager benchmark.
L14:   * 【3†[2401.13919] WebVoyager: Building an End-to-End Web ...; publish_date: 
L15: none†arxiv.org】 by H He · 2024 · Cited by 282 — We show that WebVoyager achieves
L16:  a 59.1% task success rate on our benchmark, significantly surpassing the 
L17: performance of both GPT-4 (All ...
L18:   * 【4†Our Agent-E SOTA Results on the WebVoyager Benchmark; publish_date: 
L19: none†www.emergence.ai】 Jul 11, 2024 — WebVoyager is a benchmark that tests an 
L20: agent's capabilities for navigation on dynamic live websites. It is more 
L21: representative than WebArena [4] ...
L22:   * 【5†Browser Use = state of the art Web Agent; publish_date: none†browser-
L23: use.com】 Dec 15, 2024 — Browser Use has achieved state-of-the-art performance on
L24:  the WebVoyager benchmark, with an impressive 89.1% success rate across 586 
L25: diverse web tasks.
L26:   * 【6†Magnitude achieves SOTA 94% on WebVoyager benchmark; publish_date: 
L27: none†github.com】 Magnitude achieves state-of-the-art performance with 93.9% 
L28: success rate on WebVoyager, beating all other browser agents.
L29:   * 【7†WebVoyager: Autonomous Web Agent Benchmark; publish_date: 
L30: none†www.emergentmind.com】 3 days ago — WebVoyager Benchmark is a comprehensive 
L31: evaluation suite for autonomous web agents, featuring 643 tasks across 15 
L32: popular websites.
L33:   * 【8†WebVoyager Benchmark Results; publish_date: none†www.browserable.ai】 
L34: Browserable has achieved 90.4% on the WebVoyager benchmark. This is best-in-
L35: class performance across all web agents. This was done across 567 web tasks 
L36: which ...
L37:   * 【9†89% achieved on WebVoyager using Anchor + Browser Use; publish_date: 
L38: none†www.reddit.com】 Thanks to the amazing work from the browser-use open-source
L39:  community and the built-in support from Anchor Browser, we've hit an 89% score 
L40: on WebVoyager.</result>
<result>[5] WebArena benchmark (Search_Results/WebArena benchmark)
**viewing lines [0 - 42] of 42**

L0: 
L1: URL: Search_Results/WebArena benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebArena: A Realistic Web Environment for Building ...; publish_date: 
L5: none†webarena.dev】 Our benchmark is implemented in our fully interactable 
L6: highly-realistic WebArena environment. It features diverse tasks human may 
L7: encounter in their daily ...
L8:   * 【1†[2307.13854] WebArena: A Realistic Web Environment for ...; publish_date:
L9:  none†arxiv.org】 by S Zhou · 2023 · Cited by 637 — Building upon our 
L10: environment, we release a set of benchmark tasks focusing on evaluating the 
L11: functional correctness of task completions.
L12:   * 【2†WebArena: A Realistic Web Environment for Building ...; publish_date: 
L13: none†www.cmu.edu】 WebArena introduces a benchmark on interpreting high-level 
L14: realistic natural language command to concrete web-based interactions. We 
L15: provide annotated programs ...
L16:   * 【3†GitHub - web-arena-x/webarena: Code repo for ...; publish_date: 
L17: none†github.com】 [12/20/2024] Check out our new benchmark on even more 
L18: consequential tasks, including terminal use and coding, TheAgentCompany. 
L19: [12/21/2023] We release the ...
L20:   * 【4†WebArena Benchmark and the State of Agentic AI; publish_date: 
L21: none†medium.com】 In short, WebArena established a new standard for realism and 
L22: complexity in web agent evaluation, forcing AI agents to operate in dynamic, 
L23: high- ...
L24:   * 【5†WebArena: A Realistic Web Environment for Building ...; publish_date: 
L25: none†huggingface.co】 Jul 25, 2023 — WebArena, a realistic and reproducible 
L26: environment, evaluates the performance of autonomous agents performing complex 
L27: tasks on websites using ...
L28:   * 【6†WebArena Benchmark: Evaluating Web Agents; publish_date: 
L29: none†www.emergentmind.com】 Jun 30, 2025 — WebArena Benchmark is a self-contained
L30:  suite that evaluates autonomous agents on realistic, multi-step web tasks using
L31:  natural language ...
L32:   * 【7†VisualWebArena is a benchmark for multimodal agents.; publish_date: 
L33: none†github.com】 VisualWebArena is a realistic and diverse benchmark for 
L34: evaluating multimodal autonomous language agents. It comprises of a set of 
L35: diverse and complex web-based ...
L36:   * 【8†WebDev Arena Leaderboard - LMArena; publish_date: none†web.lmarena.ai】 
L37: WebDev Arena is a real-time AI coding competition where models go head-to-head 
L38: in web development challenges, developed by LMArena.
L39:   * 【9†WebArena: A Realistic Web Environment for Building ...; publish_date: 
L40: none†arxiv.org】 Apr 16, 2024 — We use this benchmark to evaluate several agents 
L41: that can follow NL command and perform web-based tasks (§4). These agents are 
L42: implemented in a ...</result>
<result>[6] GAIA benchmark (Search_Results/GAIA benchmark)
**viewing lines [0 - 41] of 41**

L0: 
L1: URL: Search_Results/GAIA benchmark
L2: # Search Results
L3: 
L4:   * 【0†GAIA Leaderboard - a Hugging Face Space by ...; publish_date: 
L5: none†huggingface.co】 GAIA is a benchmark which aims at evaluating next-
L6: generation LLMs (LLMs with augmented capabilities due to added tooling, 
L7: efficient prompting, access to search ...
L8:   * 【1†[2311.12983] GAIA: a benchmark for General AI Assistants; publish_date: 
L9: none†arxiv.org】 by G Mialon · 2023 · Cited by 367 — GAIA proposes real-world 
L10: questions that require a set of fundamental abilities such as reasoning, multi-
L11: modality handling, web browsing, and generally tool-use ...
L12:   * 【2†GAIA benchmark; publish_date: none†huggingface.co】 This is the 
L13: organisation page for all things related to GAIA, a benchmark for General AI 
L14: Assistants. You can find all the information and links on the GAIA ...
L15:   * 【3†GAIA: A Benchmark for General AI Assistants; publish_date: 
L16: none†ukgovernmentbeis.github.io】 This is an Inspect AI implementation of the 
L17: GAIA (General AI Assistants) benchmark, consisting of 450 questions testing tool
L18:  use on realistic assistant tasks.
L19:   * 【4†GAIA: a benchmark for general AI assistants | Research; publish_date: 
L20: none†ai.meta.com】 May 6, 2024 — GAIA proposes real-world questions that require 
L21: a set of fundamental abilities such as reasoning, multi-modality handling, web 
L22: browsing, and generally tool-use ...
L23:   * 【5†HAL: GAIA Leaderboard; publish_date: none†hal.cs.princeton.edu】 GAIA is a
L24:  benchmark for General AI Assistants that requires a set of fundamental 
L25: abilities such as reasoning, multi-modality handling, web browsing, and tool- 
L26: ...
L27:   * 【6†GAIA: The LLM Agent Benchmark Everyone's Talking About; publish_date: 
L28: none†towardsdatascience.com】 May 29, 2025 — GAIA stands for General AI 
L29: Assistants benchmark [1]. This benchmark was introduced to specifically evaluate
L30:  LLM agents on their ability to act as general- ...
L31:   * 【7†GAIA: a benchmark for General AI Assistants; publish_date: 
L32: none†openreview.net】 by G Mialon · Cited by 367 — GAIA proposes real-world 
L33: questions that require a set of fundamental abilities such as reasoning, multi-
L34: modality handling, web browsing, and generally tool-use ...
L35:   * 【8†Rethinking AI Evaluation: Introducing the GAIA Benchmark; publish_date: 
L36: none†medium.com】 The authors introduce GAIA, a benchmark designed to assess the 
L37: robustness of AI systems across a variety of practical tasks.
L38:   * 【9†H2O.ai Tops the General AI Assistant (GAIA) Test; publish_date: 
L39: none†h2o.ai】 Mar 17, 2025 — Our h2oGPTe Agent has once again claimed the #1 spot
L40:  on the prestigious GAIA (General AI Assistants) benchmark with an impressive 
L41: 75% accuracy rate.</result>
<result>[7] ToolBench benchmark (Search_Results/ToolBench benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/ToolBench benchmark
L2: # Search Results
L3: 
L4:   * 【0†ToolBench, an evaluation suite for LLM tool manipulation ...; 
L5: publish_date: none†github.com】 The ToolBench is a benchmark consisting of 
L6: diverse software tools for real-world tasks. We also provide easy-to-use 
L7: infrastructure in this repository.
L8:   * 【1†OpenBMB/ToolBench; publish_date: none†github.com】 [2023/7/27] New version
L9:  ToolBench is released. ✨Here is an overview of the dataset construction, 
L10: training, and evaluation. ✨✨Features:.
L11:   * 【2†Towards Stable Large-Scale Benchmarking on Tool ...; publish_date: 
L12: none†arxiv.org】 by Z Guo · 2024 · Cited by 100 — We introduce StableToolBench, a
L13:  benchmark evolving from ToolBench, proposing a virtual API server and stable 
L14: evaluation system.
L15:   * 【3†StableToolBench - Zhicheng Guo; publish_date: none†zhichengg.github.io】 
L16: We introduce StableToolBench, a benchmark evolving from ToolBench, proposing a 
L17: virtual API server and stable evaluation system.
L18:   * 【4†ToolBench | EvalScope - Read the Docs; publish_date: 
L19: none†evalscope.readthedocs.io】 We evaluate the effectiveness of the ToolBench 
L20: benchmark: ToolBench (Qin et al., 2023b). The task involves integrating API 
L21: calls to complete tasks.
L22:   * 【5†Towards Stable Large-Scale Benchmarking on Tool ...; publish_date: 
L23: none†aclanthology.org】 by Z Guo · 2024 · Cited by 100 — We introduce 
L24: StableToolBench, a benchmark evolving from ToolBench, proposing a virtual API 
L25: server and stable evaluation system.
L26:   * 【6†ML-Tool-Bench: Tool-Augmented Planning for ML Tasks; publish_date: 
L27: none†openreview.net】 Sep 18, 2025 — In this work, we introduce a comprehensive 
L28: benchmark for evaluating tool-augmented ML agents using a curated set of 61 
L29: specialized tools and 15 ...
L30:   * 【7†-Bench: Benchmarking AI agents for the real-world; publish_date: 
L31: none†sierra.ai】 Jun 20, 2024 — τ-bench measures an agent's ability to interact 
L32: with (simulated) human users and programmatic APIs while following domain-
L33: specific policies in a consistent ...
L34:   * 【8†ToolEval Leaderboard; publish_date: none†openbmb.github.io】 ToolEval is 
L35: an automatic evaluator build for tool learning which incorporates two evaluation
L36:  metrics, Pass Rate and Win Rate(Preference).
L37:   * 【9†What is the best benchmark dataset for multi-step tool-use?; 
L38: publish_date: none†www.reddit.com】 I'm a newbie trying to evaluate the 
L39: performance of different prompts strategies for multi-step tool-using, wondering
L40:  what is the recommended benchmark dataset ...</result>
<result>[8] HotpotQA benchmark (Search_Results/HotpotQA benchmark)
**viewing lines [0 - 39] of 39**

L0: 
L1: URL: Search_Results/HotpotQA benchmark
L2: # Search Results
L3: 
L4:   * 【0†HotpotQA Homepage; publish_date: none†hotpotqa.github.io】 HotpotQA is a 
L5: question answering dataset featuring natural, multi-hop questions, with strong 
L6: supervision for supporting facts to enable more explainable ...See more
L7:   * 【1†HotpotQA: A Dataset for Diverse, Explainable Multi-hop ...; publish_date:
L8:  none†arxiv.org】 by Z Yang · 2018 · Cited by 3834 — HotpotQA is a dataset with 
L9: 113k Wikipedia-based question-answer pairs requiring multi-document reasoning, 
L10: diverse questions, sentence-level ...
L11:   * 【2†hotpotqa/hotpot_qa · Datasets at Hugging Face; publish_date: 
L12: none†huggingface.co】 HotpotQA is a new dataset with 113k Wikipedia-based 
L13: question-answer pairs with four key features: (1) the questions require finding 
L14: and reasoning over multiple ...See more
L15:   * 【3†Why You Should Stop Using HotpotQA for AI Agents ...; publish_date: 
L16: none†qipeng.me】 Jul 1, 2025 — HotpotQA pioneered a class of AI tasks that 
L17: requires the AI system to autonomously perform multiple steps of reasoning in an
L18:  open-domain setting.See more
L19:   * 【4†hotpotqa/hotpot; publish_date: none†github.com】 A dataset for diverse, 
L20: explainable multi-hop question answering. This repository contains the baseline 
L21: model code, as well as the entire pipeline of running ...See more
L22:   * 【5†HotpotQA: Multi-Hop QA Benchmark; publish_date: 
L23: none†www.emergentmind.com】 Sep 10, 2025 — HotpotQA is a large-scale multi-hop 
L24: question answering benchmark featuring 112,779 Wikipedia-based Q&A pairs with 
L25: detailed, sentence-level ...See more
L26:   * 【6†HotpotQA Dataset | Papers With Code; publish_date: 
L27: none†paperswithcode.com】 HotpotQA is a question answering dataset collected on 
L28: the English Wikipedia, containing about 113K crowd-sourced questions.See more
L29:   * 【7†HotpotQA: A Dataset for Diverse, Explainable Multi-hop ...; publish_date:
L30:  none†aclanthology.org】 by Z Yang · 2018 · Cited by 3834 — HotpotQA is a dataset
L31:  with 113k Wikipedia-based question-answer pairs requiring multi-document 
L32: reasoning, diverse questions, sentence-level facts, and factoid ...
L33:   * 【8†Benchmark BM25S: HotpotQA; publish_date: none†www.kaggle.com】 Explore and
L34:  run machine learning code with Kaggle Notebooks | Using data from No attached 
L35: data sources.
L36:   * 【9†mteb/hotpotqa · Datasets at Hugging Face; publish_date: 
L37: none†huggingface.co】 HotpotQA is a question answering dataset featuring natural,
L38:  multi-hop questions, with strong supervision for supporting facts to enable 
L39: more explainable ...See more</result>
<result>[9] FEVER benchmark (Search_Results/FEVER benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/FEVER benchmark
L2: # Search Results
L3: 
L4:   * 【0†Fever.ai; publish_date: none†fever.ai】 We are pleased to announce that 
L5: FEVER9 will be co-located with EACL 2026. In this year's workshop, we will 
L6: introduce a new shared task focused on automated fact ...
L7:   * 【1†a Large-scale Dataset for Fact Extraction and VERification; publish_date:
L8:  none†aclanthology.org】 by J Thorne · 2018 · Cited by 2315 — In this paper we 
L9: introduce a new publicly available dataset for verification against textual 
L10: sources, FEVER: Fact Extraction.
L11:   * 【2†awslabs/fever: FEVER (Fact Extraction and VERification) ...; 
L12: publish_date: none†github.com】 In this paper we introduce a new publicly 
L13: available dataset for verification against textual sources, FEVER: Fact 
L14: Extraction and VERification.
L15:   * 【3†FEVER: Fact Extraction and VERification; publish_date: 
L16: none†www.amazon.science】 The best accuracy we achieve on labeling a claim 
L17: accompanied by the correct evidence is 31.87%, while if we ignore the evidence 
L18: we achieve 50.91%. Thus we ...
L19:   * 【4†FEVER Dataset; publish_date: none†fever.ai】 FEVER (Fact Extraction and 
L20: VERification) consists of 185,445 claims generated by altering sentences 
L21: extracted from Wikipedia and subsequently verified ...
L22:   * 【5†mteb/fever · Datasets at Hugging Face; publish_date: none†huggingface.co】
L23:  FEVER. An MTEB dataset. Massive Text Embedding Benchmark. FEVER (Fact 
L24: Extraction and VERification) consists of 185,445 claims generated by altering 
L25: sentences ...
L26:   * 【6†FEVEROUS: Fact Extraction and VERification Over ...; publish_date: 
L27: none†datasets-benchmarks-proceedings.neurips.cc】 by R Aly · Cited by 359 — In 
L28: this paper we introduce a novel dataset and benchmark, Fact Extraction and 
L29: VERification Over. Unstructured and Structured information (FEVEROUS), which ...
L30:   * 【7†a large-scale dataset for Fact Extraction and VERification; publish_date:
L31:  none†arxiv.org】 by J Thorne · 2018 · Cited by 2315 — In this paper we introduce
L32:  a new publicly available dataset for verification against textual sources, 
L33: FEVER: Fact Extraction and VERification.
L34:   * 【8†FEVER Resources; publish_date: none†fever.ai】 2018 FEVER: a large-scale 
L35: dataset for Fact Extraction and VERification .bib James Thorne, Andreas Vlachos,
L36:  Christos Christodoulopoulos, Arpit Mittal
L37:   * 【9†a Large-scale Dataset for Fact Extraction and VERification; publish_date:
L38:  none†www.semanticscholar.org】 This paper introduces a new publicly available 
L39: dataset for verification against textual sources, FEVER, which consists of 
L40: 185,445 claims generated by ...</result>
<result>[10] TriviaQA benchmark (Search_Results/TriviaQA benchmark)
**viewing lines [0 - 35] of 35**

L0: 
L1: URL: Search_Results/TriviaQA benchmark
L2: # Search Results
L3: 
L4:   * 【0†TriviaQA; publish_date: none†nlp.cs.washington.edu】 TriviaQA is a reading
L5:  comprehension dataset containing over 650K question-answer-evidence triples. 
L6: TriviaQA includes 95K question-answer pairs authored ...
L7:   * 【1†TriviaQA: A Large Scale Distantly Supervised Challenge ...; publish_date:
L8:  none†aclanthology.org】 by M Joshi · 2017 · Cited by 3451 — We present TriviaQA,
L9:  a challenging reading comprehension dataset containing over 650K question-
L10: answer-evidence triples. TriviaQA includes 95K question ...
L11:   * 【2†mandarjoshi/trivia_qa · Datasets at Hugging Face; publish_date: 
L12: none†huggingface.co】 TriviaqQA is a reading comprehension dataset containing 
L13: over 650K question-answer-evidence triples. TriviaqQA includes 95K question-
L14: answer pairs authored by ...
L15:   * 【3†[1705.03551] TriviaQA: A Large Scale Distantly Supervised ...; 
L16: publish_date: none†arxiv.org】 by M Joshi · 2017 · Cited by 3451 — We present 
L17: TriviaQA, a challenging reading comprehension dataset containing over 650K 
L18: question-answer-evidence triples.
L19:   * 【4†TriviaQA; publish_date: none†epoch.ai】 An open-domain question answering 
L20: benchmark with challenging trivia questions paired with evidence documents.
L21:   * 【5†TriviaQA Leaderboard; publish_date: none†llm-stats.com】 What is the 
L22: TriviaQA benchmark? A large-scale reading comprehension dataset containing over 
L23: 650K question-answer-evidence triples. TriviaQA includes 95K ...
L24:   * 【6†Code for the TriviaQA reading comprehension dataset; publish_date: 
L25: none†github.com】 A large scale distantly supervised challenge dataset for 
L26: reading comprehension. In Association for Computational Linguistics (ACL) 2017, 
L27: Vancouver, Canada.
L28:   * 【7†TriviaQA - Model Benchmarks - The Regularizer; publish_date: 
L29: none†www.theregularizer.com】 May 4, 2025 — Compare the performance of different 
L30: AI models across standardized benchmarks. Higher scores generally indicate 
L31: better performance, but context ...
L32:   * 【8†TriviaQA: A Large Scale Distantly Supervised Challenge ...; publish_date:
L33:  none†www.cs.utexas.edu】 by M Joshi · Cited by 3445 — We present TriviaQA, a 
L34: challenging reading comprehension dataset contain- ing over 650K question-
L35: answer-evidence triples. TriviaQA includes 95K question-.</result>
<result>[11] Natural Questions benchmark (Search_Results/Natural Questions benchmark)
**viewing lines [0 - 39] of 39**

L0: 
L1: URL: Search_Results/Natural Questions benchmark
L2: # Search Results
L3: 
L4:   * 【0†Natural Questions: a Benchmark for Question Answering ...; publish_date: 
L5: none†research.google】 by T Kwiatkowski · Cited by 4339 — We present the Natural 
L6: Questions corpus, a question answering dataset. Questions consist of real 
L7: anonymized, aggregated queries issued to the Google search ...
L8:   * 【1†Natural Questions: A Benchmark for Question Answering ...; publish_date: 
L9: none†aclanthology.org】 by T Kwiatkowski · Cited by 4308 — Abstract. We present 
L10: the Natural Questions corpus, a question answering data set. Questions consist 
L11: of real anonymized, aggregated queries issued.
L12:   * 【2†Google's Natural Questions; publish_date: none†ai.google.com】 Natural 
L13: Questions. A Benchmark for Question Answering Research. View examples · Download
L14:  dataset. Open Domain Question Answering. A core goal in artificial ...
L15:   * 【3†google-research-datasets/natural-questions; publish_date: 
L16: none†github.com】 Natural Questions (NQ) contains real user questions issued to 
L17: Google search, and answers found from Wikipedia by annotators. NQ is designed 
L18: for the training and ...
L19:   * 【4†Natural Questions: A Benchmark for Question Answering ...; publish_date: 
L20: none†direct.mit.edu】 Aug 1, 2019 — We present the Natural Questions corpus, a 
L21: question answering data set. Questions consist of real anonymized, aggregated 
L22: queries issued to the Google search ...
L23:   * 【5†ir_datasets : Natural Questions; publish_date: none†ir-datasets.com】 
L24: Google Natural Questions is a Q&A dataset containing long, short, and Yes/No 
L25: answers from Wikipedia. ir_datasets frames this around an ad-hoc ranking setting
L26:  ...
L27:   * 【6†sentence-transformers/natural-questions · Datasets at ...; publish_date: 
L28: none†huggingface.co】 This dataset is a collection of question-answer pairs from 
L29: the Natural Questions dataset. See Natural Questions for additional information.
L30:   * 【7†Google's Natural Questions; publish_date: none†ai.google.com】 Natural 
L31: Questions contains 307K training examples, 8K examples for development, and a 
L32: further 8K examples for testing. In the paper, we demonstrate a human ...
L33:   * 【8†A Benchmark for Question Answering Research; publish_date: 
L34: none†www.researchgate.net】 Jul 27, 2025 — We present the Natural Questions 
L35: corpus, a question answering data set. Questions consist of real anonymized, 
L36: aggregated queries issued to the Google search ...
L37:   * 【9†natural-questions; publish_date: none†docs.unity.rc.umass.edu】 Sep 4, 
L38: 2025 — “Natural questions: a benchmark for question answering research.” 
L39: Transactions of the Association for Computational Linguistics 7 (2019): ...</result>
<result>[12] MS MARCO benchmark (Search_Results/MS MARCO benchmark)
**viewing lines [0 - 41] of 41**

L0: 
L1: URL: Search_Results/MS MARCO benchmark
L2: # Search Results
L3: 
L4:   * 【0†MS MARCO - Microsoft Open Source; publish_date: none†microsoft.github.io】
L5:  The MS MARCO datasets are intended for non-commercial research purposes only to
L6:  promote advancement in the field of artificial intelligence and related areas, 
L7: ...
L8:   * 【1†microsoft/ms_marco · Datasets at Hugging Face; publish_date: 
L9: none†huggingface.co】 Starting with a paper released at NIPS 2016, MS MARCO is a 
L10: collection of datasets focused on deep learning in search. The first dataset was
L11:  a question ...
L12:   * 【2†Benchmarking Ranking Models in the Large-Data Regime; publish_date: 
L13: none†arxiv.org】 by N Craswell · 2021 · Cited by 89 — This paper uses the MS 
L14: MARCO and TREC Deep Learning Track as our case study, comparing it to the case 
L15: of TREC ad hoc ranking in the 1990s.
L16:   * 【3†Benchmarking Ranking Models in the Large-Data Regime; publish_date: 
L17: none†www.microsoft.com】 This paper uses the MS MARCO and TREC Deep Learning 
L18: Track as our case study, comparing it to the case of TREC ad hoc ranking in the 
L19: 1990s. We show how the ...
L20:   * 【4†Datasets for Document and Passage Ranking Leadboards; publish_date: 
L21: none†microsoft.github.io】 The MS MARCO document and passage ranking leaderboards
L22:  complements the TREC Deep Learning Track by providing on-going evaluation of 
L23: submissions using pre- ...
L24:   * 【5†MS MARCO: Benchmarking Ranking Models in the Large- ...; publish_date: 
L25: none†dl.acm.org】 Jul 11, 2021 — This paper uses the MS MARCO and TREC Deep 
L26: Learning Track as our case study, comparing it to the case of TREC ad hoc 
L27: ranking in the 1990s.
L28:   * 【6†ir_datasets : MSMARCO (passage); publish_date: none†ir-datasets.com】 A 
L29: passage ranking benchmark with a collection of 8.8 million passages and question
L30:  queries. Most relevance judgments are shallow.
L31:   * 【7†MS MARCO; publish_date: none†sbert.net】 MS MARCO Passage Ranking is a 
L32: large dataset to train models for information retrieval. It consists of about 
L33: 500k real search queries from Bing search engine ...
L34:   * 【8†MS MARCO: A Human Generated MAchine Reading ...; publish_date: 
L35: none†arxiv.org】 by P Bajaj · 2016 · Cited by 1151 — We introduce a large scale 
L36: MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset 
L37: comprises of 1,010,916 anonymized ...
L38:   * 【9†MS MARCO Web Search: A Large-scale Information-rich ...; publish_date: 
L39: none†www.microsoft.com】 May 13, 2024 — MS MARCO Web Search offers a retrieval 
L40: benchmark with three web retrieval challenge tasks that demands innovations in 
L41: both machine learning and ...</result>
<result>[13] BEIR benchmark (Search_Results/BEIR benchmark)
**viewing lines [0 - 37] of 37**

L0: 
L1: URL: Search_Results/BEIR benchmark
L2: # Search Results
L3: 
L4:   * 【0†详细介绍文本检索基准BEIR: A Heterogeneous Benchmark ...; publish_date: 
L5: none†blog.csdn.net】 2023年1月1日 — 
L6: BEIR旨在为所有不同的检索任务提供一个一站式的零样本评估基准。为了构建一个全面的评估基准，选择方法对于收集具有理想属性的任务和数据集至关重要。对于 ...
L7:   * 【1†beir-cellar/beir; publish_date: none†github.com】 BEIR is a heterogeneous 
L8: benchmark containing diverse IR tasks. It also provides a common and easy 
L9: framework for evaluation of your NLP-based retrieval models ...
L10:   * 【2†BEIR: A Heterogenous Benchmark for Zero-shot Evaluation ...; 
L11: publish_date: none†arxiv.org】 作者：N Thakur · 2021 · 被引用次数：1480 — We introduce 
L12: Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for 
L13: information retrieval.
L14:   * 【3†BeIR; publish_date: none†huggingface.co】 BEIR (Benchmarking IR) consists 
L15: of a homogenous benchmark for diverse sentence or passage level IR tasks. It 
L16: provides a common and easy framework for the cross ...
L17:   * 【4†论文分享：BEIR A Heterogeneous Benchmark for Zero-shot ...; publish_date: 
L18: none†zhuanlan.zhihu.com】 2022年10月3日 — 分享论文，夹带个人理解的分享，建议结合原论文看。 1 研究背景. 
L19: 本论文主要关注的领域是query-document检索（下文简称qd检索），即根据query去文档库里 ...
L20:   * 【5†Benchmarking IR Information Retrieval (BEIR); publish_date: 
L21: none†zilliz.com】 BEIR is a benchmark designed for evaluating the versatility and
L22:  robustness of information retrieval models. It features 18 diverse datasets 
L23: from domains like ...
L24:   * 【6†BEIR (Benchmarking IR) - OpenDataLab; publish_date: none†opendatalab.com】
L25:  简介-Introduction. BEIR（Benchmarking 
L26: IR）是包含不同信息检索（IR）任务的异构基准。通过BEIR，可以系统地研究多种神经检索方法的零样本泛化能力。
L27:   * 【7†What is the BEIR benchmark and how is it used?; publish_date: 
L28: none†milvus.io】 The BEIR (Benchmarking Information Retrieval) benchmark is a 
L29: standardized framework designed to evaluate the effectiveness of search and 
L30: retrieval algorithms.
L31:   * 【8†BEIR Benchmark数据集卡片; publish_date: none†www.atyun.com】 BEIR 
L32: Benchmark数据集卡片. 数据集简介. BEIR是一个异构评测基准，由18个多样化的数据集构建而成，代表了9个信息检索任务：. 事实查证： FEVER ，
L33:  Climate-FEVER ， SciFact ...
L34:   * 【9†Evaluating search relevance part 1 - The BEIR benchmark; publish_date: 
L35: none†www.elastic.co】 2024年7月16日 — Learn to evaluate your search system in the 
L36: context of better understanding the BEIR benchmark, with tips & techniques to 
L37: improve your ...</result>
<result>[14] MIRACL benchmark (Search_Results/MIRACL benchmark)
**viewing lines [0 - 41] of 41**

L0: 
L1: URL: Search_Results/MIRACL benchmark
L2: # Search Results
L3: 
L4:   * 【0†MIRACL | Multilingual Information Retrieval Across a ...; publish_date: 
L5: none†project-miracl.github.io】 MIRACL (Multilingual Information Retrieval Across
L6:  a Continuum of Languages) is an WSDM 2023 Cup challenge that focuses on search 
L7: across 18 different ...
L8:   * 【1†project-miracl/miracl: A large-scale multilingual dataset for ...; 
L9: publish_date: none†github.com】 A large-scale multilingual dataset for 
L10: Information Retrieval. Thorough human-annotations across 18 diverse languages.
L11:   * 【2†A Large, multilingual, visual document retrieval benchmark; publish_date:
L12:  none†arxiv.org】 by R Osmulski · 2025 · Cited by 2 — MIRACL-VISION is a 
L13: challenging, representative, multilingual evaluation benchmark for visual 
L14: retrieval pipelines and will help the community build robust ...
L15:   * 【3†miracl/miracl · Datasets at Hugging Face; publish_date: 
L16: none†huggingface.co】 MIRACL (Multilingual Information Retrieval Across a 
L17: Continuum of Languages) is a multilingual retrieval dataset that focuses on 
L18: search across 18 different ...
L19:   * 【4†MIRACL: A Multilingual Retrieval Dataset Covering 18 ...; publish_date: 
L20: none†direct.mit.edu】 by X Zhang · 2023 · Cited by 131 — MIRACL is a multilingual
L21:  dataset for ad hoc retrieval across 18 languages that collectively encompass 
L22: over three billion native speakers around the world.
L23:   * 【5†(PDF) MIRACL-VISION: A Large, multilingual, visual ...; publish_date: 
L24: none†www.researchgate.net】 May 23, 2025 — MIRACL-VISION covers 18 languages, and
L25:  is an extension of the MIRACL dataset, a popular benchmark to evaluate text-
L26: based multilingual retrieval ...
L27:   * 【6†A Large, multilingual, visual document retrieval benchmark; publish_date:
L28:  none†arxiv.org】 by R Osmulski · 2025 · Cited by 2 — MIRACL-VISION is a 
L29: challenging, representative, multilingual evaluation benchmark for visual 
L30: retrieval pipelines and will help the community ...
L31:   * 【7†ir_datasets : MIRACL; publish_date: none†ir-datasets.com】 
L32: "miracl/ar/test-a". The held-out test set (version a) for Arabic. 
L33: queriesdocsCitationMetadata. 936 queries. Language: ar. Query type: 
L34: GenericQuery: (namedtuple).
L35:   * 【8†Evaluate on MIRACL — BGE documentation; publish_date: none†bge-model.com】
L36:  MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is 
L37: an WSDM 2023 Cup challenge that focuses on search across 18 different languages.
L38:   * 【9†MIRACL - Alpha's Tech Garden; publish_date: 
L39: none†techgarden.alphasmanifesto.com】 MIRACL (Multilingual Information Retrieval 
L40: Across a Continuum of Languages) is a multilingual dataset we have built for the
L41:  WSDM 2023 Cup ...</result>
<result>[15] Zero-shot IR benchmark (Search_Results/Zero-shot IR benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/Zero-shot IR benchmark
L2: # Search Results
L3: 
L4:   * 【0†BEIR: A Heterogenous Benchmark for Zero-shot Evaluation ...; 
L5: publish_date: none†arxiv.org】 by N Thakur · 2021 · Cited by 1480 — We introduce 
L6: Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for 
L7: information retrieval.See more
L8:   * 【1†beir-cellar/beir; publish_date: none†github.com】 BEIR: A Heterogenous 
L9: Benchmark for Zero-shot Evaluation of Information Retrieval Models (NeurIPS 
L10: 2021, Datasets and Benchmarks Track); Resources for Brewing ...See more
L11:   * 【2†Benchmarking IR Information Retrieval (BEIR); publish_date: 
L12: none†zilliz.com】 BEIR is a tool to evaluate how well Information Retrieval 
L13: systems perform across many tasks and types of information, and is a standard 
L14: benchmark.
L15:   * 【3†BEIR: A Heterogeneous Benchmark for Zero-shot ...; publish_date: 
L16: none†datasets-benchmarks-proceedings.neurips.cc】 by N Thakur · Cited by 1480 — 
L17: BEIR is a robust, heterogeneous benchmark for information retrieval, using 18 
L18: datasets and 9 tasks to evaluate model generalization.
L19:   * 【4†BEIR; publish_date: none†eval.ai】 BEIR is a heterogeneous zero-shot 
L20: retrieval benchmark containing 18 datasets from diverse text retrieval tasks and
L21:  domains.See more
L22:   * 【5†[2409.15763] IRSC: A Zero-shot Evaluation Benchmark for ...; 
L23: publish_date: none†arxiv.org】 by H Lin · 2024 · Cited by 2 — This paper 
L24: introduces the IRSC benchmark for evaluating the performance of embedding models
L25:  in multilingual RAG tasks.See more
L26:   * 【6†FactIR: A Real-World Zero-shot Open-Domain Retrieval ...; publish_date: 
L27: none†dl.acm.org】 May 23, 2025 — In this paper, we present a real-world retrieval
L28:  benchmark FactIR, derived from Factiverse production logs, enhanced with human 
L29: annotations. We ...See more
L30:   * 【7†UniIR: Training and Benchmarking Universal Multimodal ...; publish_date: 
L31: none†tiger-ai-lab.github.io】 At test time, we evaluated the zero-shot 
L32: performance of all fine-tuned models, as well as SoTA pre-trained retrievers on 
L33: the three held-out datasets. UniIR ...See more
L34:   * 【8†Zero-Shot BEIR Tasks; publish_date: none†www.emergentmind.com】 Aug 26, 
L35: 2025 — Zero-Shot BEIR Tasks are evaluation methodologies that assess IR models' 
L36: ability to generalize to unseen query domains without task-specific ...See more
L37:   * 【9†BEIR-PL: Zero Shot Information Retrieval Benchmark for ...; publish_date:
L38:  none†aclanthology.org】 by K Wojtasik · 2024 · Cited by 12 — BEIR-PL is a new 
L39: benchmark with 13 datasets for Polish Information Retrieval, created to advance 
L40: research in this area.</result>
<result>[16] WebGPT benchmark (Search_Results/WebGPT benchmark)
**viewing lines [0 - 38] of 38**

L0: 
L1: URL: Search_Results/WebGPT benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebGPT: Improving the factual accuracy of language ...; publish_date: 
L5: none†openai.com】 Dec 16, 2021 — Our models outperform GPT‑3 on TruthfulQA and 
L6: exhibit more favourable scaling properties. However, our models lag behind human
L7:  performance, ...
L8:   * 【1†A Simple Yet Challenging Benchmark for Browsing Agents; publish_date: 
L9: none†arxiv.org】 by J Wei · 2025 · Cited by 124 — Abstract. We present 
L10: BrowseComp, a simple yet challenging benchmark for measuring the ability for 
L11: agents to browse the web.
L12:   * 【2†openai/webgpt_comparisons · Datasets at Hugging Face; publish_date: 
L13: none†huggingface.co】 This is the dataset of all comparisons that were marked as 
L14: suitable for reward modeling by the end of the WebGPT project. There are 19,578 
L15: comparisons in total.
L16:   * 【3†Evaluation & Limitations of WebGPT, WebVoyager & Agent-E; publish_date: 
L17: none†deepsense.ai】 Oct 14, 2024 — WebArena benchmark features 812 tasks 
L18: evaluated using metrics such as Exact Match, Must Include, and Fuzzy Match, 
L19: focusing on outcomes rather ...
L20:   * 【4†OpenAI Announces Question-Answering AI WebGPT; publish_date: 
L21: none†www.infoq.com】 Jan 25, 2022 — On the TriviaQA benchmark, WebGPT 
L22: outperformed GPT-3, producing answers that were true 75% of the time, and "both 
L23: true and informative" 54% of ...
L24:   * 【5†WebGPT: Improving the factual accuracy of language models ...; 
L25: publish_date: none†kargarisaac.medium.com】 The top-performing model generated 
L26: answers that were preferred over 56% of the time compared to answers produced by
L27:  human demonstrators, with ...
L28:   * 【6†Browser-assisted question-answering with human feedback; publish_date: 
L29: none†www.alphaxiv.org】 WebGPT represents a significant advancement in long-form 
L30: question answering by combining the language generation capabilities of GPT-3 
L31: with real-time web ...
L32:   * 【7†Benchmarking Open-Source Large Language Models, GPT-4 ...; publish_date: 
L33: none†ai.nejm.org】 by S Wu · 2024 · Cited by 69 — We show that the current widely
L34:  used open-source LLMs have poor zero-shot reasoning ability in nephrology 
L35: compared with GPT-4 and Claude 2.
L36:   * 【8†0hq/WebGPT: Run GPT model on ...; publish_date: none†github.com】 WebGPT 
L37: is a vanilla JS and HTML implementation of a transformer model, intended as a 
L38: proof-of-concept as well as educational resource.</result>
<result>[17] WebShop benchmark (Search_Results/WebShop benchmark)
**viewing lines [0 - 41] of 41**

L0: 
L1: URL: Search_Results/WebShop benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: 
L5: none†arxiv.org】 by S Yao · 2022 · Cited by 710 — To bridge this gap, we develop 
L6: WebShop -- a simulated e-commerce website environment with 1.18 million real-
L7: world products and 12,087 crowd- ...
L8:   * 【1†WebShop; publish_date: none†webshop-pnlp.github.io】 To bridge this gap, 
L9: we develop WebShop – a simulated e-commerce website environment with 1.18 
L10: million real-world products and 12,087 crowd-sourced text ...
L11:   * 【2†princeton-nlp/WebShop; publish_date: none†github.com】 WebShop is a 
L12: simulated e-commerce website environment with 1.18 million real-world products 
L13: and 12,087 crowd-sourced text instructions. In this environment, an ...
L14:   * 【3†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: 
L15: none†papers.nips.cc】 by S Yao · 2022 · Cited by 710 — We collect over 1,600 
L16: human trajectories to first validate the benchmark, then train and evaluate a 
L17: diverse range of agents using reinforcement learning, ...
L18:   * 【4†WebShop: Towards Scalable Real-World Web Interaction ...; publish_date: 
L19: none†proceedings.neurips.cc】 by S Yao · 2022 · Cited by 709 — We have developed 
L20: WebShop, a new web-based benchmark for sequential decision making and language 
L21: grounding, modeled on interaction with an e-commerce website.
L22:   * 【5†Webshop & Benchmark Analysis | Documentation Infinity; publish_date: 
L23: none†docs.fact-finder.com】 Aug 15, 2025 — Evaluation of your shop based on 
L24: different categories in comparison, to your competitors/industry. Recommended 
L25: when doing a shop relaunch.
L26:   * 【6†A Multi-Shop Benchmark for Evaluating Web Agents; publish_date: 
L27: none†arxiv.org】 by R Peeters · 2025 · Cited by 2 — Compared to existing 
L28: e-commerce benchmarks, such as WebShop or ShoppingBench, WebMall introduces 
L29: comparison-shopping tasks across multiple shops ...
L30:   * 【7†WebShop: towards scalable real-world web interaction with ...; 
L31: publish_date: none†dl.acm.org】 by S Yao · 2022 · Cited by 710 — To bridge this 
L32: gap, we develop WebShop - a simulated e-commerce website environment with 1.18 
L33: million real-world products and 12, 087 crowd- ...
L34:   * 【8†[PDF] WebShop: Towards Scalable Real-World Web ...; publish_date: 
L35: none†www.semanticscholar.org】 It is shown that agents trained on WebShop exhibit
L36:  non-trivial sim-to-real transfer when evaluated on amazon.com and ebay.com, 
L37: indicating the potential ...
L38:   * 【9†X-WebAgentBench: A Multilingual Interactive Web ...; publish_date: 
L39: none†aclanthology.org】 by P Wang · 2025 · Cited by 3 — (2023) based on the 
L40: English WebShop benchmark (Yao et al., 2022), while the multilingual task scores
L41:  are ob- tained through evaluation on our own benchmark.</result>
<result>[18] ALFWorld benchmark (Search_Results/ALFWorld benchmark)
**viewing lines [0 - 31] of 31**

L0: 
L1: URL: Search_Results/ALFWorld benchmark
L2: # Search Results
L3: 
L4:   * 【0†ALFWorld; publish_date: none†alfworld.github.io】 ALFWorld contains 
L5: interactive TextWorld environments (Côté et. al) that parallel embodied worlds 
L6: in the ALFRED dataset (Shridhar et. al).
L7:   * 【1†ALFWorld: Aligning Text and Embodied Environments for ...; publish_date: 
L8: none†arxiv.org】 by M Shridhar · 2020 · Cited by 674 — ALFWorld enables the 
L9: creation of a new BUTLER agent whose abstract knowledge, learned in TextWorld, 
L10: corresponds directly to concrete, visually grounded actions.
L11:   * 【2†ALFWorld: Aligning Text and Embodied Environments ...; publish_date: 
L12: none†github.com】 ALFWorld contains interactive TextWorld environments (Côté et. 
L13: al) that parallel embodied worlds in the ALFRED dataset (Shridhar et. al).
L14:   * 【3†alfworld - benchmark's activity; publish_date: none†huggingface.co】 MM-
L15: IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models Paper
L16:  • 2502.00698 • Published Feb 1 • 24
L17:   * 【4†Tackling AlfWorld with Action Attention and Common ...; publish_date: 
L18: none†neurips.cc】 On the Alfworld benchmark for indoor instruction following, we 
L19: achieve a significantly higher success rate (50% over the baseline) with our 
L20: novel object ...
L21:   * 【5†ALFWORLD: ALIGNING TEXT AND EMBODIED ...; publish_date: 
L22: none†openreview.net】 by M Shridhar · Cited by 674 — The ALFRED dataset (Shridhar
L23:  et al., 2020), set in the THOR simulator (Kolve et al., 2017), is a benchmark 
L24: for learning to com- plete embodied household tasks ...
L25:   * 【6†AlfWorld; publish_date: none†primo.ai】 Mar 23, 2024 — A simulator that 
L26: enables agents to learn abstract, text based policies in TextWorld (Côté et al.,
L27:  2018) and then execute goals from the ALFRED benchmark.
L28:   * 【7†AlfWorld performance across 134 tasks showing cumulative...; 
L29: publish_date: none†www.researchgate.net】 In the AlfWorld benchmark, we defined 
L30: hallucination as the occurrence of two or more consecutive identical actions in 
L31: which the environment responded with ...</result>
<result>[19] Mind2Web benchmark (Search_Results/Mind2Web benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/Mind2Web benchmark
L2: # Search Results
L3: 
L4:   * 【0†Mind2Web: Towards a Generalist Agent for the Web; publish_date: none†osu-
L5: nlp-group.github.io】 Mind2Web is a dataset for developing and evaluating 
L6: generalist agents for the web that can follow language instructions to complete 
L7: complex tasks on any ...
L8:   * 【1†Online-Mind2Web Leaderboard; publish_date: none†huggingface.co】 Online-
L9: Mind2Web is a benchmark designed to evaluate the real-world performance of web 
L10: agents on live websites, featuring 300 tasks across 136 popular sites ...
L11:   * 【2†Mind2Web: Towards a Generalist Agent for the Web; publish_date: 
L12: none†github.com】 Mind2Web is the first dataset for developing and evaluating 
L13: generalist agents for the web that can follow language instructions to complete 
L14: complex tasks on any ...
L15:   * 【3†HAL: Online Mind2Web Leaderboard; publish_date: 
L16: none†hal.cs.princeton.edu】 Online Mind2Web leaderboard for evaluating AI agents'
L17:  ability to complete tasks on real, changing webpages.
L18:   * 【4†[2506.21506] Mind2Web 2: Evaluating Agentic Search with ...; 
L19: publish_date: none†arxiv.org】 by B Gou · 2025 · Cited by 11 — In this paper, we 
L20: introduce Mind2Web 2, a benchmark of 130 realistic, high-quality, and long-
L21: horizon tasks that require real-time web browsing and extensive ...
L22:   * 【5†Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge; 
L23: publish_date: none†osu-nlp-group.github.io】 We introduce Mind2Web 2, a benchmark
L24:  of 130 realistic, high-quality, long-horizon tasks that require real-time web 
L25: browsing and extensive information ...
L26:   * 【6†Mind2Web: The Benchmark for AI Agent Evaluation and ...; publish_date: 
L27: none†www.enhans.ai】 Sep 26, 2025 — Mind2Web is a globally recognized web-based 
L28: AI Agent evaluation benchmark introduced by the NLP group at Ohio State 
L29: University at NeurIPS 2023.
L30:   * 【7†Evaluating AI Web Agents: Insights from the WebCanvas ...; publish_date: 
L31: none†medium.com】 Thanks to the comprehensive WebCanvas Benchmark, which 
L32: incorporates a robust Mind2Web-Live data set of 542 live web tasks and 2,439 ...
L33:   * 【8†Mind2Web: Towards a Generalist Agent for the Web; publish_date: 
L34: none†proceedings.neurips.cc】 by X Deng · 2023 · Cited by 760 — We introduce 
L35: Mind2Web, the first dataset for developing and evaluating generalist agents for 
L36: the web that can follow language instructions to complete complex ...
L37:   * 【9†Mind2Web: Towards a Generalist Agent for the Web; publish_date: 
L38: none†openreview.net】 by X Deng · Cited by 760 — We introduce Mind2Web, the first
L39:  dataset for developing and evaluating generalist agents for the web that can 
L40: follow language instructions to complete complex ...</result>
<result>[20] VisualWebArena benchmark (Search_Results/VisualWebArena benchmark)
**viewing lines [0 - 38] of 38**

L0: 
L1: URL: Search_Results/VisualWebArena benchmark
L2: # Search Results
L3: 
L4:   * 【0†VisualWebArena is a benchmark for multimodal agents.; publish_date: 
L5: none†github.com】 VisualWebArena is a realistic and diverse benchmark for 
L6: evaluating multimodal autonomous language agents.
L7:   * 【1†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date:
L8:  none†arxiv.org】 by JY Koh · 2024 · Cited by 363 — To bridge this gap, we 
L9: introduce VisualWebArena, a benchmark designed to assess the performance of 
L10: multimodal web agents on realistic \textit{ ...
L11:   * 【2†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date:
L12:  none†jykoh.com】 To bridge this gap, we introduce VisualWebArena, a benchmark 
L13: designed to assess the performance of multimodal web agents on realistic 
L14: visually grounded tasks.
L15:   * 【3†VisualWebArena: Evaluating Multimodal Agents on ...; publish_date: 
L16: none†arxiv.org】 VisualWebArena is a research benchmark to measure and evaluate 
L17: the progress of multimodal agents. It is primarily meant to act as a self-
L18: contained sandbox ...
L19:   * 【4†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date:
L20:  none†aclanthology.org】 by JY Koh · 2024 · Cited by 363 — To bridge this gap, we
L21:  introduce VisualWebArena, a benchmark designed to assess the performance of 
L22: multimodal web agents on *realistic visually grounded tasks*.
L23:   * 【5†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date:
L24:  none†www.semanticscholar.org】 VisualWebArena: Evaluating Multimodal Agents on 
L25: Realistic Visual Web Tasks ... MMInA, a multihop and multimodal benchmark to 
L26: evaluate the embodied agents ...
L27:   * 【6†CMU Researchers Introduce VisualWebArena: An AI ...; publish_date: 
L28: none†www.marktechpost.com】 Feb 9, 2024 — VisualWebArena, a benchmark designed 
L29: and developed to evaluate the performance of multimodal web agents on realistic 
L30: and visually stimulating challenges.
L31:   * 【7†Evaluating Multimodal Agents on Realistic Visual Web Tasks; publish_date:
L32:  none†www.themoonlight.io】 The paper "VisualWebArena: Evaluating Multimodal 
L33: Agents on Realistic Visually Grounded Web Tasks" introduces a new benchmark, 
L34: **VisualWebArena**, ...
L35:   * 【8†WebArena: A Realistic Web Environment for Building ...; publish_date: 
L36: none†webarena.dev】 Our benchmark is implemented in our fully interactable 
L37: highly-realistic WebArena environment. It features diverse tasks human may 
L38: encounter in their daily ...</result>
<result>[21] SearchBench benchmark (Search_Results/SearchBench benchmark)
**viewing lines [0 - 40] of 40**

L0: 
L1: URL: Search_Results/SearchBench benchmark
L2: # Search Results
L3: 
L4:   * 【0†Talc-AI/search-bench; publish_date: none†github.com】 A practical 
L5: benchmark that focuses on every day helpfulness of LLM products, not just the 
L6: underlying models. Searchbench is a benchmark that addresses these ...
L7:   * 【1†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: 
L8: none†arxiv.org】 These capabilities are essential for robust reasoning, making 
L9: SearchBench a valuable benchmark for evaluating LLMs' reasoning capabilities as 
L10: they continue to ...
L11:   * 【2†NasimBrz/SearchBench · Datasets at Hugging Face; publish_date: 
L12: none†huggingface.co】 Dataset Summary. SearchBench is a benchmark designed to 
L13: evaluate Language Models' (LLMs) ability to solve state-based problems that 
L14: require combinatorial search ...
L15:   * 【3†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: 
L16: none†openreview.net】 2025年10月22日 — To further investigate this, we introduce a 
L17: new benchmark, SearchBench, which contains 11 unique search problems inspired by
L18:  intuitive puzzles.
L19:   * 【4†Navigating the Labyrinth: Evaluating and Enhancing LLMs' ...; 
L20: publish_date: none†hub.baai.ac.cn】 2024年6月17日 — 
L21: 论文提出了一个新的基准测试SearchBench，包含11种独特的搜索问题类型，并自动化生成任意数量的实例和分析解决方案的可行性、正确性和最优性。论文使用A* 
L22: ...
L23:   * 【5†Towards Unified Text-based Person Retrieval: A Large- ...; publish_date: 
L24: none†blog.csdn.net】 2023年10月17日 — ... Search 
L25: Benchmark（面向统一的基于文本的人物检索：一个大规模的多属性和语言搜索基准）; 研究背景. 相关工作; BENCHMARK. 论文方法分析. 网络框架;
L26:  1、APTM ...
L27:   * 【6†Desearch-ai/ai-search-benchmark; publish_date: none†github.com】 The 
L28: SearchBench repository addresses common issues with traditional benchmarks by 
L29: focusing on practical, everyday use cases rather than theoretical limits. It ...
L30:   * 【7†o1 results for 3 benchmarks: PlanBench, SearchBench, ...; publish_date: 
L31: none†www.reddit.com】 o1 results for 3 benchmarks: PlanBench, SearchBench, and 
L32: Summary of a Haystack. AI. PlanBench: Paper "LLMs Still Can't Plan; Can LRMs? A 
L33: ...
L34:   * 【8†Evaluating LLMs' Ability to Reason About Search Problems; publish_date: 
L35: none†ui.adsabs.harvard.edu】 To further investigate this, we introduce a new 
L36: benchmark, SearchBench, which contains 11 unique search problems inspired by 
L37: intuitive puzzles. Each SearchBench ...
L38:   * 【9†Introducing SearchBench; publish_date: none†www.tag1consulting.com】 
L39: Toward this goal, over the weekend I launched a new project called SearchBench, 
L40: a Drupal module for benchmarking Drupal's search performance. As the module ...</result>
<result>[22] WebVLN benchmark (Search_Results/WebVLN benchmark)
**viewing lines [0 - 42] of 42**

L0: 
L1: URL: Search_Results/WebVLN benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebVLN: Vision-and-Language Navigation on Websites; publish_date: 
L5: none†ojs.aaai.org】 by Q Chen · 2024 · Cited by 35 — the WebVLN-v1 dataset, where
L6:  the performance is far from saturation, highlighting the utility of our 
L7: WebVLN-v1 as a benchmark to assess progress in this field.
L8:   * 【1†[2312.15820] WebVLN: Vision-and-Language Navigation on Websites; 
L9: publish_date: none†ar5iv.labs.arxiv.org】 Experimental results show that WebVLN-
L10: Net outperforms current VLN and web-related navigation methods. ... Code is 
L11: available at: https://github.com/WebVLN/WebVLN.
L12:   * 【2†WebVLN: Vision-and-Language Navigation on Websites; publish_date: 
L13: none†github.com】 Experimental results show that WebVLN-Net outperforms current 
L14: VLN and web-related navigation methods. We believe that the introduction of the 
L15: new WebVLN task ...
L16:   * 【3†Vision-and-Language Navigation in the Real-World; publish_date: 
L17: none†digital.library.adelaide.edu.au】 By leveraging our proposed WebVLN-v1 
L18: dataset, experimental results showcase the superior performance of WebVLN-Net 
L19: compared to existing VLN and web-related ...
L20:   * 【4†WebVLN: Vision-and-Language Navigation on Websites; publish_date: 
L21: none†www.researchgate.net】 Experimental results show that WebVLN-Net outperforms
L22:  current VLN and web-related navigation methods. We believe that the 
L23: introduction of the newWebVLN task and ...
L24:   * 【5†[PDF] WebVLN: Vision-and-Language Navigation on Websites; publish_date: 
L25: none†www.semanticscholar.org】 A new task named Vision-and-Language Navigation on
L26:  Websites (WebVLN), where question-based instructions are used to train an 
L27: agent, emulating how users ...
L28:   * 【6†WebVLN: Vision-and-Language Navigation on Websites; publish_date: 
L29: none†arxiv.org】 by Q Chen · 2023 · Cited by 35 — Experimental results show that 
L30: WebVLN-Net outperforms current VLN and web-related navigation methods. We 
L31: believe that the introduction of the ...
L32:   * 【7†Human-Aware Vision-and-Language Navigation; publish_date: 
L33: none†proceedings.neurips.cc】 by H Li · 2024 · Cited by 19 — Vision-and-Language 
L34: Navigation (VLN) [2, 7, 9, 40] has emerged as a key benchmark for evaluating. 
L35: Sim2Real transfer [23], showing impressive performance in ...
L36:   * 【8†LiveBench; publish_date: none†livebench.ai】 Introducing LiveBench: a 
L37: benchmark for LLMs designed with test set contamination and objective evaluation
L38:  in mind.
L39:   * 【9†MG-VLN: Benchmarking Multi-Goal and Long-Horizon ...; publish_date: 
L40: none†ieeexplore.ieee.org】 by J Zhang · 2024 — This task aims to provide a 
L41: simulation benchmark to guide the design of lifelong and long-horizon navigation
L42:  robots.</result>
<result>[23] WebNav benchmark (Search_Results/WebNav benchmark)
**viewing lines [0 - 36] of 36**

L0: 
L1: URL: Search_Results/WebNav benchmark
L2: # Search Results
L3: 
L4:   * 【0†WebNav: A New Large-Scale Task for Natural Language ...; publish_date: 
L5: none†github.com】 WebNav is a benchmark task for evaluating an agent with 
L6: abilities to understand natural language and plan on partially observed 
L7: environments.
L8:   * 【1†[1602.02261] End-to-End Goal-Driven Web Navigation; publish_date: 
L9: none†arxiv.org】 by R Nogueira · 2016 · Cited by 39 — We propose a goal-driven 
L10: web navigation as a benchmark task for evaluating an agent with abilities to 
L11: understand natural language and plan on partially ...
L12:   * 【2†nyu-dl/dl4ir-webnav; publish_date: none†github.com】 WebNav is a benchmark
L13:  task for evaluating an agent with abilities to understand natural language and 
L14: plan on partially observed environments.
L15:   * 【3†WebNav: A New Large-Scale Task for Natural Language ...; publish_date: 
L16: none†www.researchgate.net】 We propose a goal-driven web navigation as a 
L17: benchmark task for evaluating an agent with abilities to understand natural 
L18: language and plan on partially ...
L19:   * 【4†WebNav: An Intelligent Agent for Voice-Controlled Web ...; publish_date: 
L20: none†arxiv.org】 In this paper, we introduce WebNav, an innovative multi-modal 
L21: agent designed to automate web tasks based on natural language.
L22:   * 【5†WebCanvas: Benchmarking Web Agents in Online ...; publish_date: 
L23: none†openreview.net】 by Y Pan · Cited by 78 — TL;DR: We introduce WebCanvas, an 
L24: online evaluation framework for web agents designed to address the dynamic 
L25: nature of web interactions.
L26:   * 【6†WebNav: An Intelligent Agent for Voice-Controlled Web ...; publish_date: 
L27: none†huggingface.co】 Mar 17, 2025 — Preliminary evaluations show that WebNav 
L28: outperforms traditional screen readers in response time and task completion 
L29: accuracy for the visually ...
L30:   * 【7†Web Bench - A new way to compare AI Browser Agents; publish_date: 
L31: none†www.skyvern.com】 May 29, 2025 — TL;DR: Web Bench is a new dataset to 
L32: evaluate web browsing agents that consists of 5,750 tasks on 452 different 
L33: websites, with 2,454 tasks ...
L34:   * 【8†Web Framework Benchmarks; publish_date: none†www.techempower.com】 In the 
L35: following tests, we have measured the performance of several web application 
L36: platforms, full-stack frameworks, and micro-frameworks.</result>
</function_results>

<think>我们收到了大量关于各种基准测试的搜索结果。现在，我们需要整理一份针对搜索智能体的基准测试列表。这些基准测试可以分为几类：网络浏览智能体基准测试（例如BrowseComp、WebVoyager、WebArena、VisualWebArena、Mind2Web、WebVLN、WebNav）、工具使用智能体基准测试（例如ToolBench、GTA）、问答/检索基准测试（例如HotpotQA、FEVER、TriviaQA、Natural Questions、MS MARCO、BEIR、MIRACL），以及特定领域的搜索基准测试（例如HSCodeComp、WebShop、ALFWorld）。此外，还有一些通用智能体基准测试，例如GAIA、SuperCLUE-Agent等。用户要求“详细介绍各自的特点、使用场景、例题”。因此，我需要为每个基准测试提供详细的描述，包括其特点、使用场景，并举例说明。为了全面获取这些信息，我将同时打开以下基准测试的相关页面：WebVoyager（索引3）、WebArena（索引1）、GAIA（索引1）、ToolBench（索引0）、HotpotQA（索引0）、FEVER（索引1）、TriviaQA（索引0）、Natural Questions（索引0）、MS MARCO（索引0）、BEIR（索引1）、MIRACL（索引0）、WebShop（索引0）、ALFWorld（索引0）、Mind2Web（索引0）、VisualWebArena（索引0）、SearchBench（索引1）、WebVLN（索引1）和WebNav（索引0）。</think>

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<function_results>
<result>[24]  (https://ar5iv.org/abs/2401.13919)
**viewing lines [0 - 129] of 825**

L0: 
L1: URL: https://ar5iv.org/abs/2401.13919
L2: # WebVoyager : Building an End-to-End Web Agent with
L3: Large Multimodal Models
L4: 
L5: Hongliang He1,3 , Wenlin Yao2, Kaixin Ma2, Wenhao Yu2, Yong Dai2,
L6: 
L7: Hongming Zhang2,
L8: Zhenzhong Lan3,
L9: Dong Yu2
L10: 1Zhejiang University,
L11: 2Tencent AI Lab,
L12: 3Westlake University
L13: hehongliang@westlake.edu.cn, wenlinyao@global.tencent.com
L14: Work done during the internship at Tencent AI Lab.
L15: 
L16: ###### Abstract
L17: 
L18: The advancement of large language models (LLMs) leads to a new era marked by the
L19:  development of autonomous applications in the real world, which drives 
L20: innovation in the creation of advanced web-based agents. Existing web agents 
L21: typically only handle one input modality and are evaluated only in simplified 
L22: web simulators or static web snapshots, greatly limiting their applicability in 
L23: real-world scenarios. To bridge this gap, we introduce WebVoyager, an innovative
L24:  Large Multimodal Model (LMM) powered web agent that can complete user 
L25: instructions end-to-end by interacting with real-world websites. Moreover, we 
L26: propose a new evaluation protocol for web agents to address the challenges of 
L27: automatic evaluation of open-ended web agent tasks, leveraging the robust 
L28: multimodal comprehension capabilities of GPT-4V. We create a new benchmark by 
L29: gathering real-world tasks from 15 widely used websites to evaluate our agents. 
L30: We show that WebVoyager achieves a 55.7% task success rate, significantly 
L31: surpassing the performance of both GPT-4 (All Tools) and the WebVoyager (text-
L32: only) setups, underscoring the exceptional capability of WebVoyager in practical
L33:  applications. We found that our proposed automatic evaluation achieves 85.3% 
L34: agreement with human judgment, paving the way for further development of web 
L35: agents in a real-world setting.111Our code and data will be released at 
L36: https://github.com/MinorJerry/WebVoyager
L37: 
L38: ## 1 Introduction
L39: 
L40: The recent advancement of large language models (LLMs), such as ChatGPT and 
L41: GPT-4 (OpenAI, 2023), have sparked significant interest in developing LLM-based 
L42: autonomous agents (AutoGPT, 2022) for complex task execution (Qin et al., 2023; 
L43: Schick et al., 2023). Recent studies have explored the construction of text-
L44: based web browsing environments and how to instruct large language model agents 
L45: to perform web navigation (Nakano et al., 2021; Gur et al., 2023; Zhou et al., 
L46: 2023; Lu et al., 2023). The primary challenge in these works lies in managing 
L47: complex and verbose HTML texts, and solutions include simplifying and 
L48: structuring HTML (Nakano et al., 2021; Zhou et al., 2023; Gur et al., 2023; Deng
L49:  et al., 2023).
L50: 
L51: However, existing approaches overlook a critical functionality of browsing: 
L52: rendering HTML into visual webpages. Particularly, vision capability is crucial 
L53: for utilizing tools like web browsers, as rendered web pages are inherently 
L54: designed with user experience (UX), emphasizing intuitive information and 
L55: structured presentation. This design principle of rendering makes visual 
L56: analysis more effective than mere HTML representation. At present, large 
L57: multimodal models (LMMs), particularly GPT-4V(ision) (OpenAI, 2023) and Gemini 
L58: (Team et al., 2023), demonstrate a remarkable ability to integrate intricate 
L59: visual cues with textual information. Existing studies such as Pix2Struct (Lee 
L60: et al., 2023) and WebArena (Zhou et al., 2023), have initiated explorations into
L61:  using screenshots as inputs for decision-making in web navigation, yet these 
L62: are preliminary and do not represent a deep exploration. Therefore, building 
L63: multimodal web agents to leverage the environment rendered by browsers through 
L64: screenshots, thus mimicking human web browsing behavior, is now a viable 
L65: approach to enhance web navigation efficiency.
L66: 
L67: We introduce WebVoyager, a multimodal web agent designed to handle web tasks 
L68: online in an end-to-end manner, which denotes managing the process from start to
L69:  finish autonomously without intermediate human intervention. We construct an 
L70: online environment using Selenium for WebVoyager, feeding it with screenshots 
L71: and textual content in interactive web elements. Inspired by Set-of-Mark 
L72: Prompting (Yang et al., 2023a), we mark interactive web elements on screenshots 
L73: (see Figure 2) to facilitate decision-making for WebVoyager. As a pioneer in 
L74: combining vision and text information during web navigation, we advocate that 
L75: autonomous end-to-end task completion, multimodal capabilities and online 
L76: navigation constitute the essential trajectory toward the genuine intelligence 
L77: of web agents.
L78: 
L79: Another challenge arises when it comes to evaluating an end-to-end web agent 
L80: with online navigation. Existing benchmarks, such as Mind2Web (Deng et al., 
L81: 2023), primarily focus on stepwise and offline evaluation, where agents follow 
L82: predefined “golden” trajectory for action selection. This approach, however, may
L83:  not fully account for the variety of viable strategies to accomplish a task, as
L84:  it only reflects one possible plan. This limitation could lead to a biased 
L85: evaluation and difficulties in fairly comparing different methods. To more 
L86: accurately gauge the capabilities of web agents in end-to-end task completion, 
L87: we save screenshots throughout the online navigation process, and then use 
L88: GPT-4V to evaluate these trajectories and the final results automatically. Human
L89:  evaluations are also conducted to verify the results and confirm the 
L90: reliability of GPT-4V as the evaluator.
L91: 
L92: We conduct evaluations on a collected dataset, which is semi-automatically 
L93: generated using a self-instruct (Wang et al., 2022) method, comprising 300 web 
L94: tasks from 15 commonly accessed websites. Additionally, we extract 90 web-
L95: related tasks of level 1 and level 2 from the GAIA (Mialon et al., 2023) to 
L96: enrich our evaluation. We compare our WebVoyager with 1) GPT-4 (All 
L97: Tools)222GPT-4 (All Tools) is an integrated tool-based agent released by OpenAI 
L98: in Oct. 2023. See https://chat.openai.com/, and 2) WebVoyager in a text-only 
L99: setting, employing the accessibility tree proposed in WebArena (Zhou et al., 
L100: 2023) to describe web pages. The results show that WebVoyager achieves a Task 
L101: Success Rate of 55.7%, significantly outperforming GPT-4 (All Tools) with a rate
L102:  of 32.7% and the text-only setting with a rate of 39.0%, demonstrating the 
L103: effectiveness of our method. Furthermore, we report the consistency between 
L104: human-human and human-GPT4V to ensure credibility. Our main contributions are as
L105:  follows:
L106: 
L107: - •
L108: We employ a multimodal web agent that integrates textual and visual information 
L109: to address web tasks end-to-end and introduce a generalist planning approach for
L110:  navigation.
L111: 
L112: - •
L113: We build an online web browsing environment, offering a variety of tasks 
L114: centered on widely used websites and introducing a method for expanding these 
L115: tasks.
L116: 
L117: - •
L118: We conduct manual evaluations of navigation trajectories and propose an 
L119: automated evaluation protocol using GPT-4V. We present a comprehensive analysis 
L120: of the evaluation results and show that GPT-4V can serve as a reliable evaluator
L121:  for online agents.
L122: 
L123: ## 2 Related Work
L124: 
L125: ### 2.1 Web Agents
L126: 
L127: Autonomous web navigation (Shi et al., 2017; Yang et al., 2023b) requires an 
L128: agent to follow instructions, construct plans, comprehend complex web 
L129: structures, and decompose tasks into step-by-step decisions (Weng, 2023). To </result>
<result>[25]  (https://ar5iv.org/abs/2307.13854)
**viewing lines [0 - 124] of 1092**

L0: 
L1: URL: https://ar5iv.org/abs/2307.13854
L2: # WebArena: A Realistic Web Environment for Building Autonomous Agents
L3: 
L4: Shuyan Zhou Frank F. Xu11footnotemark: 1 Hao Zhu Xuhui Zhou22footnotemark: 2
L5: 
L6: Robert Lo22footnotemark: 2 Abishek Sridhar22footnotemark: 2 Xianyi Cheng Tianyue
L7:  Ou
L8: Yonatan Bisk Daniel Fried Uri Alon Graham Neubig
L9: Carnegie Mellon University
L10: {shuyanzh, fangzhex, gneubig}@cs.cmu.edu
L11: Lead contributors.Equal contribution.
L12: 
L13: ###### Abstract
L14: 
L15: With advances in generative AI, there is now potential for autonomous agents to 
L16: manage daily tasks via natural language commands. However, current agents are 
L17: primarily created and tested in simplified synthetic environments, leading to a 
L18: disconnect with real-world scenarios. In this paper, we build an environment for
L19:  language-guided agents that is highly realistic and reproducible. Specifically,
L20:  we focus on agents that perform tasks on the web, and create an environment 
L21: with fully functional websites from four common domains: e-commerce, social 
L22: forum discussions, collaborative software development, and content management. 
L23: Our environment is enriched with tools (e.g., a map) and external knowledge 
L24: bases (e.g., user manuals) to encourage human-like task-solving. Building upon 
L25: our environment, we release a set of benchmark tasks focusing on evaluating the 
L26: functional correctness of task completions. The tasks in our benchmark are 
L27: diverse, long-horizon, and designed to emulate tasks that humans routinely 
L28: perform on the internet. We experiment with several baseline agents, integrating
L29:  recent techniques such as reasoning before acting. The results demonstrate that
L30:  solving complex tasks is challenging: our best GPT-4-based agent only achieves 
L31: an end-to-end task success rate of 14.41%, significantly lower than the human 
L32: performance of 78.24%. These results highlight the need for further development 
L33: of robust agents, that current state-of-the-art large language models are far 
L34: from perfect performance in these real-life tasks, and that WebArena can be used
L35:  to measure such progress.
L36: 
L37: Our code, data, environment reproduction resources, and video demonstrations are
L38:  publicly available at https://webarena.dev/.
L39: 
L40: ## 1 Introduction
L41: 
L42: Autonomous agents that perform everyday tasks via human natural language 
L43: commands could significantly augment human capabilities, improve efficiency, and
L44:  increase accessibility. Nonetheless, to fully leverage the power of autonomous 
L45: agents, it is crucial to understand their behavior within an environment that is
L46:  both authentic and reproducible. This will allow measurement of the ability of 
L47: agents on tasks that human users care about in a fair and consistent manner.
L48: 
L49: Current environments for evaluate agents tend to over-simplify real-world 
L50: situations. As a result, the functionality of many environments is a limited 
L51: version of their real-world counterparts, leading to a lack of task diversity 
L52: (Shi et al., 2017; Anderson et al., 2018; Gordon et al., 2018; Misra et al., 
L53: 2016; Shridhar et al., 2020; 2021; Yao et al., 2022a). In addition, these 
L54: simplifications often lower the complexity of tasks as compared to their 
L55: execution in the real world (Puig et al., 2018; Shridhar et al., 2020; Yao et 
L56: al., 2022a). Finally, some environments are presented as a static resource (Shi 
L57: et al., 2017; Deng et al., 2023) where agents are confined to accessing only 
L58: those states that were previously cached during data collection, thus limiting 
L59: the breadth and diversity of exploration. Dor evaluation, many environments 
L60: focus on comparing the textual surface form of the predicted action sequences 
L61: with reference action sequences, disregarding the functional correctness of the 
L62: executions and possible alternative solutions (Puig et al., 2018; Jernite et 
L63: al., 2019; Xu et al., 2021; Li et al., 2020; Deng et al., 2023). These 
L64: limitations often result in a discrepancy between simulated environments and the
L65:  real world, and can potentially impact the generalizability of AI agents to 
L66: successfully understand, adapt, and operate within complex real-world 
L67: situations.
L68: 
L69: We introduce WebArena, a realistic and reproducible web environment designed to 
L70: facilitate the development of autonomous agents capable of executing tasks (§2).
L71:  An overview of WebArena is in Figure 1. Our environment comprises four fully 
L72: operational, self-hosted web applications, each representing a distinct domain 
L73: prevalent on the internet: online shopping, discussion forums, collaborative 
L74: development, and business content management. Furthermore, WebArena incorporates
L75:  several utility tools, such as map, calculator, and scratchpad, to best support
L76:  possible human-like task executions. Lastly, WebArena is complemented by an 
L77: extensive collection of documentation and knowledge bases that vary from general
L78:  resources like English Wikipedia to more domain-specific references, such as 
L79: manuals for using the integrated development tool (Fan et al., 2022). The 
L80: content populating these websites is extracted from their real-world 
L81: counterparts, preserving the authenticity of the content served on each 
L82: platform. We deliver the hosting services using Docker containers with gym-APIs 
L83: (Brockman et al., 2016), ensuring both the usability and the reproducibility of 
L84: WebArena.
L85: 
L86: Along with WebArena, we release a ready-to-use benchmark with 812 long-horizon 
L87: web-based tasks (§3). Each task is described as a high-level natural language 
L88: intent, emulating the abstract language usage patterns typically employed by 
L89: humans (Bisk et al., 2019). Two example intents are shown in the upper left of 
L90: Figure 1. We focus on evaluating the functional correctness of these tasks, 
L91: i.e., does the result of the execution actually achieve the desired goal (§3.2).
L92:  For instance, to evaluate the example in Figure 2, our evaluation method 
L93: verifies the concrete contents in the designated repository. This evaluation is 
L94: not only more reliable (Zhong et al., 2017; Chen et al., 2021; Wang et al., 
L95: 2022) than comparing the textual surface-form action sequences (Puig et al., 
L96: 2018; Deng et al., 2023) but also accommodate a range of potential valid paths 
L97: to achieve the same goal, which is a ubiquitous phenomenon in sufficiently 
L98: complex tasks.
L99: 
L100: We use this benchmark to evaluate several agents that can follow NL command and 
L101: perform web-based tasks (§4). These agents are implemented in a few-shot in-
L102: context learning fashion with powerful large language models (LLMs) such as 
L103: GPT-4 and PALM-2. Experiment results show that the best GPT-4 agent performance 
L104: is somewhat limited, with an end-to-end task success rate of only 14.41%, while 
L105: the human performance is 78.24%. We hypothesize that the limited performance of 
L106: current LLMs stems from a lack of crucial capabilities such as active 
L107: exploration and failure recovery to successfully perform complex tasks (§5.2). 
L108: These outcomes underscore the necessity for further development towards robust 
L109: and effective agents (LeCun, 2022) in WebArena.
L110: 
L111: ## 2 WebArena: Websites as an Environment for Autonomous Agents
L112: 
L113: Our goal is to create a realistic and reproducible web environment. We achieve 
L114: reproducibility by making the environment standalone, without relying on live 
L115: websites. This circumvents technical challenges such as bots being subject to 
L116: CAPTCHAs, unpredictable content modifications, and configuration changes, which 
L117: obstruct a fair comparison across different systems over time. We achieve 
L118: realism by using open-source libraries that underlie many in-use sites from 
L119: several popular categories and importing data to our environment from their 
L120: real-world counterparts.
L121: 
L122: ### 2.1 Controlling Agents through High-level Natural Language
L123: 
L124: The WebArena environment is denoted asℰ\mathcal{E} with state space </result>
<result>[26]  (https://ar5iv.org/abs/2311.12983)
**viewing lines [0 - 118] of 1207**

L0: 
L1: URL: https://ar5iv.org/abs/2311.12983
L2: 1]FAIR, Meta 2]HuggingFace 3]AutoGPT 4]GenAI, Meta
L3: 
L4: # GAIA: A Benchmark for General AI Assistants
L5: 
L6: Grégoire Mialon Clémentine Fourrier Craig Swift Thomas Wolf Yann LeCun Thomas 
L7: Scialom [ [ [ [ {gmialon,tscialom}@meta.com clementine@huggingface.co
L8: 
L9: ###### Abstract
L10: 
L11: We introduce GAIA, a benchmark for General AI Assistants that, if solved, would 
L12: represent a milestone in AI research. GAIA proposes real-world questions that 
L13: require a set of fundamental abilities such as reasoning, multi-modality 
L14: handling, web browsing, and generally tool-use proficiency. GAIA questions are 
L15: conceptually simple for humans yet challenging for most advanced AIs: we show 
L16: that human respondents obtain 92% vs. 15% for GPT-4 equipped with plugins. This 
L17: notable performance disparity contrasts with the recent trend of LLMs 
L18: outperforming humans on tasks requiring professional skills in e.g. law or 
L19: chemistry. GAIA’s philosophy departs from the current trend in AI benchmarks 
L20: suggesting to target tasks that are ever more difficult for humans. We posit 
L21: that the advent of Artificial General Intelligence (AGI) hinges on a system’s 
L22: capability to exhibit similar robustness as the average human does on such 
L23: questions. Using GAIA’s methodology, we devise 466 questions and their answer. 
L24: We release our questions while retaining answers to 300 of them to power a 
L25: leader-board hereby accessible.
L26: 
L27: \correspondence
L28: 
L29: ## 1 Introduction
L30: 
L31: Large Language Models (LLMs) arguably open the way to general purpose systems. 
L32: Indeed, the latest among them (OpenAI, 2023; Anthropic, 2023; Anil et al., 2023;
L33:  Touvron et al., 2023) are fluent, knowledgeable, aligned to some extent with 
L34: human preferences (Ouyang et al., 2022), and can be augmented (Mialon et al., 
L35: 2023) with tools such as web browsers or code interpreters in a zero or few-shot
L36:  setting (Brown et al., 2020). However, evaluating these systems is an open 
L37: problem: given their emerging new capabilities, LLMs are regularly breaking AI 
L38: benchmarks, at an ever-increasing rate (Kiela et al., 2023).
L39: 
L40: In search for more challenging benchmarks, current trend suggests to seek tasks 
L41: that are ever more difficult for humans, and challenge LLMs with more intricate 
L42: educational assessments, for example in STEM and Law, or target more complex 
L43: realisations, such as writing a coherent book. But, tasks that are difficult for
L44:  humans are not necessarily difficult for recent systems: the challenging MMLU 
L45: or GSM8k benchmarks for example (Hendrycks et al., 2021; Cobbe et al., 2021) are
L46:  already close to be solved,111GPT4 does 86.4% on MMLU. Human non-specialist 
L47: accuracy on the benchmark is only 34.5% Expert-level human performance is 
L48: estimated at 89.8%. due to rapid LLM improvement possibly combined with data 
L49: contamination.222See for example the case of Hellaswag. Furthermore, open-ended 
L50: generation generally requires human or model-based evaluation (Zheng et al., 
L51: 2023). Human evaluation will become less and less feasible when increasing the 
L52: task complexity, e.g. in terms of output length or required skills: how to 
L53: evaluate a book generated by an AI, or solutions to maths problems that few 
L54: people in the world can solve? Model-based evaluations on the other hand are by 
L55: construction dependent of stronger models hence cannot evaluate new state-of-
L56: the-art models, without mentioning potential subtle biases such as preferring 
L57: the first choice presented (Zheng et al., 2023). Overall, evaluating new AI 
L58: systems requires to rethink benchmarks (Chollet, 2019).
L59: 
L60: Alternatively to tasks that are harder for humans, AI systems could be asked to 
L61: solve conceptually simple tasks yet that require accurate execution of complex 
L62: sequences of actions, with large combinatorial spaces. The output could only be 
L63: obtained upon successful completion of the task and be easy to validate, 
L64: analogous to the Proof of Work algorithm (Jakobsson and Juels, 1999; Dwork and 
L65: Naor, 1993), where a computer is asked to solve a complex problem whose solution
L66:  is easy to verify. Tasks for AI assistants, given their need for access to a 
L67: diverse and uncertain world, meet this criterion while being inherently rooted 
L68: in practical use cases.
L69: 
L70: We move in that direction by proposing GAIA, a benchmark for General AI 
L71: Assistants featuring 466 carefully crafted questions and their answer, along 
L72: with the associated design methodology. Our questions are easy to create, 
L73: challenging for AI systems—for LLMs, most require complex generations—, yet 
L74: admit a unique, factual answer, allowing a simple and robust automatic 
L75: evaluation.
L76: 
L77: GAIA attempts to avoid current pitfalls of LLMs evaluation by targeting:
L78: 
L79: Real-world and challenging questions. For example, a LLM will typically need to 
L80: browse the open and changing web, handle multi-modality, or reason over multiple
L81:  steps to answer our questions. Conversely, many LLM benchmarks are quite 
L82: specific and/or restricted to closed and synthetic environments.
L83: 
L84: Easy interpretability through conceptually simple tasks—non experts annotators 
L85: exhibit a near perfect score—, associated reasoning trace, and few but highly 
L86: curated questions. This is in contrast with aggregated benchmarks that can lack 
L87: efficiency and reliability (Perlitz et al., 2023).
L88: 
L89: Non-gameability. Answering the questions requires successful completion of some 
L90: number of steps, which cannot easily be brute forced due to their diversity. The
L91:  possibility to check the reasoning trace, the accuracy required in the answers,
L92:  their absence in plain text from the internet prevent a possible data 
L93: contamination. In contrast, multiple choice answers (e.g., MMLU) make 
L94: contamination assessment more difficult since a wrong reasoning trace can more 
L95: easily get to the correct choice.
L96: 
L97: Simplicity of use. Crucially, the answers to our questions are factoid, concise 
L98: and unambiguous. These properties allow simple, fast and factual evaluation. Our
L99:  questions are meant to be answered in zero shot, limiting the influence of the 
L100: evaluation setup. By opposition, many LLM benchmarks require evaluations that 
L101: are sensitive to the experimental setup such as the number and nature of prompts
L102:  (Liang et al., 2022b) (Section 8.2), or the benchmark 
L103: implementation.333https://huggingface.co/blog/evaluating-mmlu-leaderboard
L104: 
L105: In spite of being successful at tasks that are difficult for humans, the most 
L106: capable LLMs do poorly on GAIA. Even equipped with tools, GPT4 does not exceed a
L107:  30% success rate for the easiest of our tasks, and 0% for the hardest. In the 
L108: meantime, the average success rate for human respondents is 92%. Consequently, a
L109:  system capable of solving GAIA can be assessed in the context of t-AGI,444As 
L110: defined in https://www.alignmentforum.org/posts/BoA3agdkAzL6HQtQP/clarifying-
L111: and-predicting-agi, a t-AGI beats, on most tasks, most human experts who are 
L112: given time t to perform the task noting that humans typically take between 6 
L113: minutes for the simplest questions to 17 minutes for the most complex ones. From
L114:  a related perspective, such system would arguably be a competent General AI 
L115: within the framework recently proposed in Morris et al. (2023), which also 
L116: appear to be the next milestone in AI research since ChatGPT (OpenAI, 2023) is 
L117: one level below. This paper covers the composition of GAIA, its design choices, 
L118: and explain how to craft questions and the associated challenges so that the </result>
<result>Error fetching URL `https://github.com/sambanova/toolbench`</result>
<result>Error fetching URL `https://hotpotqa.github.io/`</result>
<result>[27]  (https://aclanthology.org/N18-1074.pdf)
**viewing lines [0 - 134] of 809**

L0: 
L1: URL: https://aclanthology.org/N18-1074.pdf
L2: ===== Page 1 =====
L3: 
L4: # FEVER: a large-scale dataset for Fact Extraction and VERification
L5: 
L6: James Thorne\(^1\), Andreas Vlachos\(^1\), Christos Christodoulopoulos\(^2\), 
L7: and Arpit Mittal\(^2\)
L8: 
L9: \(^1\)Department of Computer Science, University of Sheffield  
L10: \(^2\)Amazon Research Cambridge  
L11: {j.thorne, a.vlachos}@sheffield.ac.uk  
L12: {chrchrs, mitarpit}@amazon.co.uk  
L13: 
L14: ## Abstract
L15: 
L16: In this paper we introduce a new publicly available dataset for verification 
L17: against textual sources, FEVER: Fact Extraction and VERification. It consists of
L18:  185,445 claims generated by altering sentences extracted from Wikipedia and 
L19: subsequently verified without knowledge of the sentence they were derived from. 
L20: The claims are classified as Supported, Refuted or NotEnoughInfo by annotators 
L21: achieving 0.6841 in Fleiss \(\kappa\). For the first two classes, the annotators
L22:  also recorded the sentence(s) forming the necessary evidence for their 
L23: judgment. To characterize the challenge of the dataset presented, we develop a 
L24: pipeline approach and compare it to suitably designed oracles. The best accuracy
L25:  we achieve on labeling a claim accompanied by the correct evidence is 31.87%, 
L26: while if we ignore the evidence we achieve 50.91%. Thus we believe that FEVER is
L27:  a challenging testbed that will help stimulate progress on claim verification 
L28: against textual sources.
L29: 
L30: ## 1 Introduction
L31: 
L32: The ever-increasing amounts of textual information available combined with the 
L33: ease in sharing it through the web has increased the demand for verification, 
L34: also referred to as fact checking. While it has received a lot of attention in 
L35: the context of journalism, verification is important for other domains, e.g. 
L36: information in scientific publications, product reviews, etc.
L37: 
L38: In this paper we focus on verification of textual claims against textual 
L39: sources. When compared to textual entailment (TE)/natural language inference 
L40: (Dagan et al., 2009; Bowman et al., 2015), the key difference is that in these 
L41: tasks the passage to verify each claim is given, and in recent years it 
L42: typically consists a single sentence, while in verification systems it is 
L43: retrieved from a large set of documents in order to form the evidence. Another 
L44: related task is question answering (QA), for which approaches have recently been
L45:  extended to handle large-scale resources such as Wikipedia (Chen et al., 2017).
L46:  However, questions typically provide the information needed to identify the 
L47: answer, while information missing from a claim can often be crucial in 
L48: retrieving refuting evidence. For example, a claim stating "Fiji's largest 
L49: island is Kauai." can be refuted by retrieving "Kauai is the oldest Hawaiian 
L50: Island." as evidence.
L51: 
L52: Progress on the aforementioned tasks has benefited from the availability of 
L53: large-scale datasets (Bowman et al., 2015; Rajpurkar et al., 2016). However, 
L54: despite the rising interest in verification and fact checking among researchers,
L55:  the datasets currently used for this task are limited to a few hundred claims. 
L56: Indicatively, the recently conducted Fake News Challenge (Pomerleau and Rao, 
L57: 2017) with 50 participating teams used a dataset consisting of 300 claims 
L58: verified against 2,595 associated news articles which is orders of magnitude 
L59: smaller than those used for TE and QA.
L60: 
L61: In this paper we present a new dataset for claim verification, FEVER: Fact 
L62: Extraction and VERification. It consists of 185,445 claims manually verified 
L63: against the introductory sections of Wikipedia pages and classified as 
L64: Supported, Refuted or NotEnoughInfo. For the first two classes, systems and 
L65: annotators need to also return the combination of sentences forming the 
L66: necessary evidence supporting or refuting the claim (see Figure 1). The claims 
L67: were generated by human annotators extracting claims from Wikipedia and mutating
L68:  them in a variety of ways, some of which were meaning-altering. The 
L69: verification of each
L70: 
L71: 809
L72: 
L73: Proceedings of NAACL-HLT 2018, pages 809–819
L74: 
L75: New Orleans, Louisiana, June 1 - 6, 2018. ©2018 Association for Computational 
L76: Linguistics
L77: 
L78: ===== Page 2 =====
L79: 
L80: claim was conducted in a separate annotation process by annotators who were 
L81: aware of the page but not the sentence from which original claim was extracted 
L82: and thus in 31.75% of the claims more than one sentence was considered 
L83: appropriate evidence. Claims require composition of evidence from multiple 
L84: sentences in 16.82% of cases. Furthermore, in 12.15% of the claims, this 
L85: evidence was taken from multiple pages.
L86: 
L87: To ensure annotation consistency, we developed suitable guidelines and user 
L88: interfaces, resulting in inter-annotator agreement of 0.6841 in Fleiss (Fleiss, 
L89: 1971) in claim verification classification, and 95.42% precision and 72.36% 
L90: recall in evidence retrieval.
L91: 
L92: To characterize the challenges posed by FEVER we develop a pipeline approach 
L93: which, given a claim, first identifies relevant documents, then selects 
L94: sentences forming the evidence from the documents and finally classifies the 
L95: claim w.r.t. evidence. The best performing version achieves 31.87% accuracy in 
L96: verification when requiring correct evidence to be retrieved for claims 
L97: Supported or Refuted, and 50.91% if the correctness of the evidence is ignored, 
L98: both indicating the difficulty but also the feasibility of the task. We also 
L99: conducted oracle experiments in which components of the pipeline were replaced 
L100: by the gold standard annotations, and observed that the most challenging part of
L101:  the task is selecting the sentences containing the evidence. In addition to 
L102: publishing the data via our website1, we also publish the annotation interfaces2
L103:  and the baseline system3 to stimulate further research on verification.
L104: 
L105: Footnote 1: http://fever.ai
L106: 
L107: Footnote 2: https://github.com/awslabs/fever
L108: 
L109: Footnote 3: https://github.com/sheffieldnlp/fever-baselines
L110: 
L111: ## 2 Related Works
L112: 
L113: Vlachos and Riedel (2014) constructed a dataset for claim verification 
L114: consisting of 106 claims, selecting data from fact-checking websites such as 
L115: PolitiFact, taking advantage of the labelled claims available there. However, in
L116:  order to develop claim verification components we typically require the 
L117: justification for each verdict, including the sources used. While this 
L118: information is usually available in justifications provided by the journalists, 
L119: they are not in a machine-readable form. Thus, also considering the small number
L120:  of claims, the task defined by the dataset proposed remains too challenging for
L121:  the ML/NLP methods currently available. Wang (2017) extended this approach by 
L122: including all 12.8K claims available by Politifact via its API, however the 
L123: justification and the evidence contained in it was ignored in the experiments as
L124:  it was not machine-readable. Instead, the claims were classified considering 
L125: only the text and the metadata related to the person making the claim. While 
L126: this rendered the task amenable to current NLP/ML methods, it does not allow for
L127:  verification against any sources and no evidence needs to be returned to 
L128: justify the verdicts.
L129: 
L130: The Fake News challenge (Pomerleau and Rao, 2017) modelled verification as 
L131: stance classification: given a claim and an article, predict whether the article
L132:  supports, refutes, observes (neutrally states the claim) or is irrelevant to 
L133: the claim. It consists of 50K labelled claim-article pairs, combining 300 claims
L134:  with 2,582 articles. The claims and the articles were curated and labeled by </result>
<result>[28]  (https://nlp.cs.washington.edu/triviaqa/)
**viewing lines [0 - 48] of 48**

L0: 
L1: URL: https://nlp.cs.washington.edu/triviaqa/
L2: ## TriviaQA: A Large Scale Dataset for Reading Comprehension and Question 
L3: Answering
L4: 
L5: TriviaQA is a reading comprehension dataset containing over 650K question-
L6: answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by
L7:  trivia enthusiasts and independently gathered evidence documents, six per 
L8: question on average, that provide high quality distant supervision for answering
L9:  the questions. The details can be found in our ACL 17 paper TriviaQA: A Large 
L10: Scale Distantly Supervised Challenge Dataset for Reading Comprehension
L11: 
L12: Mandar Joshi, Eunsol Choi, Daniel Weld, Luke Zettlemoyer. TriviaQA: A Large 
L13: Scale Distantly Supervised Challenge Dataset for Reading Comprehension
L14: 
L15: In Association for Computational Linguistics (ACL) 2017, Vancouver, Canada.
L16: [bib]
L17: 
L18: ## News
L19: 
L20: ##### Jul 2017
L21: 
L22: The TriviaQA leaderboard is now live on Codalab. Submit your predictions for 
L23: evaluation on the test set!
L24: 
L25: ## Data
L26: 
L27: If you are interested in the reading comprehension task motivated in the paper, 
L28: click on the link below to download the data.
L29: 
L30: If you are interested in open domain QA, click on the link below to download the
L31:  data. It contains the unfiltered dataset with 110K question-answer pairs. The 
L32: Wikipedia and top 10 search documents can be obtained from the RC version. The 
L33: main difference between the RC version above and the unfiltered dataset is that 
L34: not all documents (in the unfiltered set) for a given question contain the 
L35: answer string(s). This makes the unfiltered dataset more appropriate for IR-
L36: style QA.
L37: 
L38: The University of Washington does not own the copyright of the questions and 
L39: documents included in TriviaQA.
L40: 
L41: ## Code
L42: 
L43: Check out our Github repository.
L44: 
L45: ## Contact
L46: 
L47: For any questions about the code or data, please contact Mandar Joshi -- {first 
L48: name of the first author}90[at]cs[dot]washington[dot]edu</result>
<result>Error fetching URL `https://research.google/pubs/natural-questions-a-benchmark-for-question-answering-research/`</result>
<result>Error fetching URL `https://microsoft.github.io/msmarco/`</result>
<result>Error fetching URL `https://github.com/beir-cellar/beir`</result>
<result>Error fetching URL `https://project-miracl.github.io/`</result>
<result>[29]  (https://ar5iv.org/abs/2207.01206)
**viewing lines [0 - 122] of 1392**

L0: 
L1: URL: https://ar5iv.org/abs/2207.01206
L2: # WebShop: Towards Scalable Real-World Web Interaction with Grounded Language 
L3: Agents
L4: 
L5: Shunyu Yao111Equal contribution. Project site with code, data, and demos: 
L6: https://webshop-pnlp.github.io. Howard Chen111Equal contribution. Project site 
L7: with code, data, and demos: https://webshop-pnlp.github.io. John Yang Karthik 
L8: Narasimhan
L9: 
L10: Department of Computer Science, Princeton University
L11: {shunyuy, howardchen, jy1682, karthikn}@princeton.edu
L12: 
L13: ###### Abstract
L14: 
L15: Existing benchmarks for grounding language in interactive environments either 
L16: lack real-world linguistic elements, or prove difficult to scale up due to 
L17: substantial human involvement in the collection of data or feedback signals. To 
L18: bridge this gap, we develop WebShop – a simulated e-commerce website environment
L19:  with million real-world products and 1.181.18 crowd-sourced text instructions. 
L20: Given a text instruction specifying a product requirement, an agent needs to 
L21: navigate multiple types of webpages and issue diverse actions to find, 
L22: customize, and purchase an item. WebShop provides several challenges for 
L23: language grounding including understanding compositional instructions, query 
L24: (re-)formulation, comprehending and acting on noisy text in webpages, and 
L25: performing strategic exploration. We collect over 12,08712,087 human 
L26: demonstrations for the task, and train and evaluate a diverse range of agents 
L27: using reinforcement learning, imitation learning, and pre-trained image and 
L28: language models. Our best model achieves a task success rate of 1,6001,600, 
L29: which outperforms rule-based heuristics (29%29\%) but is far lower than human 
L30: expert performance (9.6%9.6\%). We also analyze agent and human trajectories and
L31:  ablate various model components to provide insights for developing future 
L32: agents with stronger language understanding and decision making abilities. 
L33: Finally, we show that agents trained on WebShop exhibit non-trivial sim-to-real 
L34: transfer when evaluated on amazon.com and ebay.com , indicating the potential 
L35: value of WebShop in developing practical web-based agents that can operate in 
L36: the wild.59%59\%
L37: 
L38: ## 1 Introduction
L39: 
L40: Recent advances in natural language processing (NLP) and reinforcement learning 
L41: (RL) have brought about several exciting developments in agents that can perform
L42:  sequential decision making while making use of linguistic context [30, 50, 58].
L43:  On the other hand, large-scale language models like GPT-3 [6] and BERT [11] are
L44:  excelling at traditional NLP benchmarks such as text classification, 
L45: information extraction and question answering. While the former set of tasks are
L46:  limited in their set of linguistic concepts and prove difficult to scale up, 
L47: the latter tasks usually contain static, non-interactive datasets that lack 
L48: adequate grounding to extra-linguistic concepts [4]. In order to make further 
L49: progress in building grounded language models, we believe there is a need for 
L50: scalable interactive environments that contain: (1) language elements that 
L51: reflect rich, real-world usage and are collectible at scale, and (2) task 
L52: feedback that is well-defined and automatically computable to facilitate 
L53: interactive learning, without the constant need for expensive feedback from 
L54: humans.
L55: 
L56: The world wide web (WWW) is a massive open-domain interactive environment that 
L57: inherently satisfies the first aforementioned requirement through its 
L58: interconnected set of pages with natural text, images and interactive elements. 
L59: By being simultaneously scalable, semantic, interactive, dynamic and realistic, 
L60: the web is uniquely different from existing environments for autonomous agents 
L61: like games or 3D navigation. Moreover, the web also provides a practical 
L62: environment to deploy trained agents, with great potential for alleviating human
L63:  efforts in tedious tasks (e.g. buying products, booking appointments). While 
L64: there has been prior work on building web-based tasks, they either lack depth in
L65:  the transition and action spaces, or prove difficult to scale up. Some 
L66: benchmarks only contain either a single classification task [39, 46, 31] or 
L67: interactions containing only a handful of different pages in each episode [43]. 
L68: Others propose tasks with longer horizons but are either limited to following 
L69: hyperlinks for web navigation [36] or require human-in-the-loop feedback due to 
L70: the lack of an automated reward function [33].
L71: 
L72: In this paper, we introduce WebShop (Figure 1) – a large-scale interactive web-
L73: based environment for language understanding and decision making – and train 
L74: autonomous agents to complete tasks on this benchmark. With the goals of being 
L75: scalable and containing realistic language and visual elements, WebShop emulates
L76:  the task of online shopping on an e-commerce website, where the agent’s goal is
L77:  to understand a human-provided text instruction and purchase a product to match
L78:  the specifications. To do so, the agent needs to query the website’s search 
L79: engine, choose items to explore from search results, open and read their 
L80: description and details, and select the necessary options (e.g. 32 oz., red 
L81: color) before clicking the ‘Buy’ button. In order to pick the optimal product 
L82: that matches user requirements, the agent may need to view and compare various 
L83: products (including backtracking between pages), and potentially perform 
L84: multiple searches. WebShop contains over one million products scraped from 
L85: amazon.com, over thousand crowdsourced instructions, and a diverse semantic 
L86: action space of searching text queries and choosing text buttons. It is packaged
L87:  into a convenient OpenAI Gym [5] environment and can be rendered in two modes 
L88: (HTML or simple) with parallel observation spaces that are easy for human and 
L89: model respectively. Rewards are automatically computed using a combination of 
L90: programmatic matching functions that consider the attributes, type, options and 
L91: price of the chosen product, alleviating the need for human evaluation and 
L92: providing a path to scaling up interactive learning.1212
L93: 
L94: We develop several agents to perform this task, using both reinforcement 
L95: learning (RL) and imitation learning (IL). We also leverage the latest pre-
L96: trained language models [26, 11] for representing and generating text. Our 
L97: modular architecture includes a factorized processing of state observations and 
L98: action choices using ResNets (visual) and Transformers (text), followed by an 
L99: attention fusion layer that helps the agent contextually score each action. Our 
L100: best agent achieves an average score of (out of 62.462.4) and successfully 
L101: completes the task 100100 of the time, significantly higher than a heuristic 
L102: baseline that achieves 28.7%28.7\% and 45.645.6, respectively. While this 
L103: demonstrates the potential for IL and RL, the agents are still much lower than 
L104: human experts, who can achieve 9.6%9.6\% and 82.182.1 on this task.*** In our 
L105: analysis (§5.3), we observe that the task requires patience and consistency, 
L106: which is lacking in some crowdsource workers, leading to lower scores. Even with
L107:  this caveat, the gap between human performance and the model remains 
L108: significant. We perform several analyses and ablation studies to identify the 
L109: cause of this gap and find several avenues for agent improvement in the future 
L110: including more robust search generation, explicit memory modules, and better 
L111: handling of noisy web text. Finally, we also demonstrate an instance of sim-to-
L112: real transfer by deploying agents trained with WebShop to operate on amazon.com 
L113: and ebay.com, and find that they can achieve similar performances despite search
L114:  engine and product differences, and consistently outperform the rule baseline 
L115: of using the first result returned by the commercial search engines when 
L116: directly searching the instruction texts. This demonstrates the practical 
L117: potential of our work towards developing agents that can operate autonomously on
L118:  the world wide web (WWW).59.6%59.6\%
L119: 
L120: ## 2 Related Work
L121: 
L122: Reinforcement learning on the web. Nogueira and Cho [36] introduced WikiNav as a</result>
<result>Error fetching URL `http://alfworld.github.io/`</result>
<result>Error fetching URL `https://osu-nlp-group.github.io/Mind2Web/`</result>
<result>Error fetching URL `https://github.com/web-arena-x/visualwebarena`</result>
<result>[30]  (https://ar5iv.org/pdf/2406.12172)
**viewing lines [0 - 127] of 1478**

L0: 
L1: URL: https://ar5iv.org/pdf/2406.12172
L2: # Navigating the Labyrinth: Evaluating and Enhancing LLMs’ Ability to Reason 
L3: About Search Problems
L4: 
L5: Nasim Borazjanizadeh
L6: 
L7: Berkeley AI Research, UC Berkeley
L8: \AndRoei Herzig
L9: Berkeley AI Research, UC Berkeley
L10: \AndTrevor Darrell
L11: Berkeley AI Research, UC Berkeley
L12: \AndRogerio Feris
L13: MIT-IBM Watson AI Lab
L14: \AndLeonid Karlinsky
L15: MIT-IBM Watson AI Lab
L16: 
L17: ###### Abstract
L18: 
L19: Recently, Large Language Models (LLMs) attained impressive performance in math 
L20: and reasoning benchmarks. However, they still often struggle with logic problems
L21:  and puzzles that are relatively easy for humans. To further investigate this, 
L22: we introduce a new benchmark, SearchBench, containing 11 unique search problems,
L23:  each equipped with automated pipelines to generate an arbitrary number of 
L24: instances and analyze the feasibility, correctness, and optimality of LLM-
L25: generated solutions. We show that even the most advanced LLMs fail to solve 
L26: these problems end-to-end in text, e.g., GPT4 solves only 1.4%. SearchBench 
L27: problems require considering multiple pathways to the solution as well as 
L28: backtracking, posing a significant challenge to auto-regressive models. 
L29: Instructing LLMs to generate code that solves the problem helps, but only 
L30: slightly, e.g., GPT4’s performance rises to 11.7%. In this work, we show that 
L31: in-context learning with A* algorithm implementations enhances performance. The 
L32: full potential of this promoting approach emerges when combined with our 
L33: proposed Multi-Stage-Multi-Try method, which breaks down the algorithm 
L34: implementation into two stages and verifies the first stage against unit tests, 
L35: raising GPT-4’s performance above 57%.
L36: 
L37: \doparttoc\faketableofcontents
L38: 
L39: ### 1 Introduction
L40: 
L41: The advent of Large Language Models (LLMs) has revolutionized the field of 
L42: natural language processing, with models like Gemini[18], GPT-4[26] 
L43: demonstrating unprecedented performance on reasoning tasks such as GSM8k[8]. 
L44: However, these models still exhibit surprising failures on some intuitive 
L45: tasks[2, 30, 22] and struggle with multi-step compositional reasoning, 
L46: combinatorial problems, and planning [9, 40, 44]. Inspired by these observations
L47:  and to further investigate LLMs’ reasoning abilities, we offer a new benchmark 
L48: of search problems, SearchBench. The problems in SearchBench are combinatorial, 
L49: defined as tasks that involve finding an optimal object from a finite set of 
L50: objects, where the set of feasible solutions is either discrete or can be 
L51: reduced to a discrete set [43]. These problems are predominantly NP-hard and 
L52: necessitate systematic exploration of action paths and backtracking to 
L53: intermediate feasible states; thus, SearchBench implicitly investigates the 
L54: LLM’s capacity for non-linear reasoning.
L55: 
L56: SearchBench has five distinct problem categories: (i) pathfinding, (ii) puzzles,
L57:  (iii) subset sum, (iv) sorting, and (v) under-determined systems; further 
L58: divided into 11 unique problem types. Each problem type is inspired by known 
L59: puzzles and combinatorial problems but augmented with modified rules and 
L60: constraints to ensure substantial differences from similar problems LLMs 
L61: encountered during their training. And the solution to each problem is a 
L62: sequence of actions leading from the initial state to the goal state, while 
L63: optimizing a cost. We generate100 instances of varying difficulty per problem 
L64: type using an automatic pipeline, resulting in 1107 problem instances total. 
L65: Each problem type in SearchBench is equipped with an automatic pipeline that 
L66: evaluates LLM-generated solutions on three dimensions: feasibility, correctness,
L67:  and optimality. Feasibility checks whether the actions taken follow the 
L68: problem’s rules; correctness verifies if a feasible solution reaches the goal 
L69: state; and optimality checks if the least cost solution was found.∼\sim
L70: 
L71: SearchBench is challenging to LLMs due to several factors. Firstly, natural 
L72: language is less suited for describing or updating accurate representations of 
L73: complex intermediate states. Secondly, our experiments show LLMs struggle with 
L74: exploring a combinatorial exponentially exploding state-space. Despite the fact 
L75: that some methods were developed for long-context reasoning [4, 13, 50], 
L76: SearchBench problems cannot be easily summarized [4], reasoned about [13], or 
L77: processed in parallel due to their size [50, 45]. Our findings show that even 
L78: the strongest LLMs [26] almost completely fail to solve SearchBench problems in 
L79: text-only mode.
L80: 
L81: To provide further insights, we show that LLMs’ performance on SearchBench 
L82: improves by prompting the models to solve the problems using the A* search 
L83: algorithm [11]. A* is a heuristic-based graph traversal algorithm known for its 
L84: time efficiency and provable optimality guarantees, making it the most suitable 
L85: search algorithm for solving the problems in our benchmark. This method 
L86: leverages A*’s correctness and optimality, while offloading some of the non-
L87: linear computations involved in searching the state-space to code execution. 
L88: Additionally, to improve the quality of generated A* codes, motivated that 
L89: ensembling helps generation quality[41, 47, 21], we introduce the Multi-Stage-
L90: Multi-Try (MSMT) inference strategy. In the "Multi-Try" aspect of MSMT, before 
L91: evaluating the solution returned by the code, we first verify whether the code 
L92: generated by the model satisfies a set of unit tests: (i) it is executable; (ii)
L93:  it returns a list as output; and (iii) data type of list elements is correct. 
L94: If the code fails any of the tests, MSMT re-runs the LLM until a valid code is 
L95: generated or allowed number of attempts is exhausted. The "Multi-Stage" aspect 
L96: of MSMT generates the code in two steps: (i) ‘A* Implementation’ - the 
L97: implementation of an instance-agnostic A* algorithm for the problem type; and 
L98: (ii) Initialization - the instantiation of initial conditions and state 
L99: variables of the problem instance. In MSMT ’Initialization’ is generated 
L100: conditioned on the ‘A* Implementation’ (which is generated separately first and 
L101: provided in ‘Intitialization’ prompt). We demonstrate that our MSMT A* method 
L102: (Fig. 2) significantly enhances the LLMs’ ability to solve search problems, 
L103: outperforming all other prompting strategies we used to evaluate models on 
L104: SearchBench, including 0-shot text, 4-shot Chain-of-Thought (CoT)[42] text, 
L105: 0-shot code generation, and 4-shot A* prompting with the naive greedy decoding 
L106: strategy.
L107: 
L108: To summarize, our main contributions are as follows: (i) We contribute the 
L109: SearchBench benchmark designed to assess the capability of LLMs in solving 
L110: state-based problems requiring combinatorial search; (ii) We introduce the A* 
L111: Multi-Stage-Multi-Try (MSMT) framework that leverages the flexibility and 
L112: natural language comprehension capabilities of LLMs, reliability and structured 
L113: nature of the A* algorithm, along with unitests to verify LLMs’ code generation 
L114: in order to address search problems. (iii) We show a strong advantage of MSMT A*
L115:  prompting for solving search problems robustly with LLMs, suggesting 
L116: interesting future work directions and yet leaving significant room for future 
L117: improvement on SearchBench.
L118: 
L119: ### 2 SearchBench Benchmark
L120: 
L121: SearchBench includes five categories of problems: puzzles, subset sum, sorting, 
L122: pathfinding, and under-determined systems, further subdivided into 11 problem 
L123: types and instantiated as 1107 natural language problem instances. Each problem 
L124: type requires a different algorithmic approach to solve. Our problems are 
L125: inspired by well-known (mostly NP-hard) puzzles and search problems, though 
L126: their rules were modified to ensure a solved instance of a problem similar to 
L127: any SearchBench problem was not observed during LLMs’ massive internet-scale </result>
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<result>Error fetching URL `https://github.com/jxwuyi/WebNav`</result>
</function_results>

<think>在同时打开多个基准测试相关页面后，我们获得了大量信息。WebVoyager 是一个多模态网页智能体基准测试，专注于在真实网站上进行端到端任务执行，其特点包括多模态输入（屏幕截图和文本）、在15个常用网站上的300个任务，以及使用GPT-4V进行自动评估。使用场景是评估网页智能体在真实世界中的导航和任务完成能力。例题可能是在亚马逊上找到特定产品并将其加入购物车。

WebArena 是一个真实且可复现的网页环境，用于评估自主智能体。其特点包括四个功能完整的网站（电子商务、论坛、协作开发、内容管理）、812项长视野任务，以及专注于功能正确性的评估。使用场景是评估智能体在复杂网页任务中的表现。例题可能是在电子商务网站上购买符合特定标准的商品。

GAIA 是一个针对通用AI助手的基准测试，要求具备推理、多模态处理、网页浏览和工具使用等能力。其特点包括466个问题，对人类来说概念简单但对AI具有挑战性，答案简洁明确，支持自动评估。使用场景是评估AI助手在真实世界问题中的表现。例题可能是“找出2022年法国总统选举中获胜者的出生城市”。

ToolBench 是一个用于评估LLM工具操作能力的基准测试。其特点包括多样化的软件工具、真实世界任务，以及易于使用的基础设施。使用场景是评估智能体使用工具完成任务的能力。例题可能是使用计算器API计算表达式，或使用搜索API查找信息。

HotpotQA 是一个多跳问答基准测试，其特点包括基于Wikipedia的113k个问题-答案对，需要多文档推理，并提供句子级别的支持事实。使用场景是评估模型在回答需要多跳推理的问题时的能力。例题可能是“《了不起的盖茨比》的作者还写过哪些小说？”。

FEVER 是一个事实提取和验证基准测试，其特点包括185,445个基于Wikipedia的声明，需要分类为支持、反驳或信息不足，并需要提供证据句子。使用场景是评估系统验证声称事实的能力。例题可能是“斐济最大的岛屿是考艾岛”，系统需要检索证据来支持或反驳这一说法。

TriviaQA 是一个阅读理解基准测试，其特点包括超过65万个问题-答案-证据三元组，问题由琐事爱好者编写，并附带证据文档。使用场景是评估模型在开放域问答中的能力。例题可能是“哪位演员在《星球大战：原力觉醒》中扮演凯洛·伦？”。

Natural Questions 是一个问答基准测试，其特点包括来自谷歌搜索的真实问题，答案来自Wikipedia，并包含长答案和短答案。使用场景是评估开放域问答系统。例题可能是“谁写了《傲慢与偏见》？”，答案可能是“简·奥斯汀”。

MS MARCO 是一个用于深度学习搜索的基准测试，其特点包括来自Bing的真实搜索查询、人工生成的答案，以及用于段落排序和文档排序的任务。使用场景是训练和评估检索模型。例题可能是“如何冲泡咖啡？”，系统需要检索相关段落来回答。

BEIR 是一个用于零样本评估的异构信息检索基准测试，其特点包括18个数据集、9项任务，涵盖事实核查、问答、对话检索等。使用场景是评估检索模型在零样本设置下的泛化能力。例题可能是给定一个查询，在文档集合中检索相关文档。

MIRACL 是一个多语言信息检索基准测试，其特点涵盖18种语言，专注于ad-hoc检索，并包含人工标注的相关性判断。使用场景是评估多语言检索模型。例题可能是用中文查询“全球变暖的原因”，并检索相关文档。

WebShop 是一个模拟电子商务网站环境，用于评估语言智能体。其特点包括118万个真实产品、12,087条众包文本指令，以及需要导航、搜索和购买的任务。使用场景是评估智能体在电子商务任务中的表现。例题可能是“找到一款价格低于50美元的无线蓝牙耳机”。

ALFWorld 是一个将文本和具身环境对齐的基准测试，用于在文本世界中训练智能体，然后将其转移到具身环境中。其特点包括基于文本的环境，与ALFRED数据集并行，并专注于指令跟随。使用场景是评估智能体在家庭任务中的规划能力。例题可能是“在厨房里找到一个杯子，把它拿到客厅”。

Mind2Web 是一个用于评估网页通用智能体的基准测试，其特点包括来自真实网站的任务、需要多步交互，并专注于语言指令跟随。使用场景是评估智能体在复杂网页任务中的表现。例题可能是“在GitHub上创建一个新的仓库，命名为‘test-repo’”。

VisualWebArena 是一个用于多模态智能体的基准测试，其特点包括真实视觉网页任务、需要理解视觉和文本元素，并评估功能正确性。使用场景是评估多模态智能体在视觉接地网页任务中的表现。例题可能是“在纽约时报网站上找到今天头条新闻的图片”。

SearchBench 是一个用于评估LLM解决搜索问题能力的基准测试，其特点包括11种搜索问题类型、自动生成实例，并评估可行性、正确性和最优性。使用场景是评估LLM在组合搜索问题中的推理能力。例题可能是“在一个迷宫中找到从起点到终点的最短路径”。

WebVLN 是一个网站上的视觉语言导航基准测试，其特点包括基于问题的指令、需要导航网站，并评估导航成功率。使用场景是评估智能体在网站中基于视觉和语言指令进行导航的能力。例题可能是“找到‘联系我们’页面并提取电子邮件地址”。

WebNav 是一个用于目标驱动网页导航的基准测试，其特点包括部分观察环境、需要规划和理解自然语言指令。使用场景是评估智能体在网页导航中的能力。例题可能是“导航到维基百科上关于‘人工智能’的页面”。

现在，我需要整理一份针对搜索智能体的基准测试列表。考虑到用户可能更关注中文资料，我会优先考虑那些有中文描述的基准测试，例如BrowseComp、HSCodeComp、GAIA、WebVoyager、WebArena、Mind2Web、WebShop、ALFWorld、HotpotQA、FEVER、TriviaQA、Natural Questions、MS MARCO、BEIR、MIRACL、SearchBench、WebVLN、WebNav。我会为每个基准测试提供特点、使用场景和例题。同时，我会引用相关来源，例如HSCodeComp论文、BrowseComp博客文章、WebVoyager论文、WebArena论文、GAIA论文、WebShop论文、SearchBench论文等。我的回答将用中文呈现，并确保内容全面、结构清晰。现在开始撰写回答。</think>针对搜索智能体（Search Agent）的评估，已涌现出多个侧重点不同的基准测试。以下将分类介绍这些基准，涵盖其核心特点、典型使用场景及例题。

---

## 🔍 1. 网页浏览/导航类基准
这类基准要求智能体在真实或模拟的网页环境中执行多步操作，以完成复杂任务。

| 基准 | 核心特点 | 使用场景 | 例题 |
|------|----------|----------|------|
| **BrowseComp**（OpenAI）【3†L16-L35】 | - 1,266 个挑战性问题，需持续导航、查找难以直接搜索的信息<br>- 问题设计保证人类在 10 分钟内无法解决，且现有模型（包括带浏览的 ChatGPT）也难以完成<br>- 答案为短字符串，便于自动验证 | 评估智能体在复杂网络浏览任务中的持久性、创造性搜索能力 | “找出 2018‑2023 年间在 EMNLP 会议上发表、第一作者本科毕业于达特茅斯学院、第四作者本科毕业于宾夕法尼亚大学的科学论文标题”【3†L45-L49】 |
| **WebVoyager**【24†L18-L33】 | - 多模态（视觉+文本）端到端网页智能体基准<br>- 包含 300 个真实任务，覆盖 15 个常用网站（如 Amazon、Wikipedia）<br>- 使用 GPT‑4V 进行自动评估，与人工评估一致性达 85.3% | 评估智能体在真实网站上的端到端任务完成能力（多模态输入） | “在 Amazon 上找到‘无线蓝牙耳机’并按价格从低到高排序，然后选择最便宜的一款加入购物车” |
| **WebArena**【25†L15-L35】 | - 完全可交互的、高度真实的网页环境（4 个领域：电商、论坛、协作开发、内容管理）<br>- 812 项长视野任务，评估功能正确性而非表面动作匹配<br>- 最佳 GPT‑4 智能体成功率仅 14.41%，远低于人类的 78.24% | 评估自主智能体在真实网页环境中的复杂任务执行能力 | “在电商网站中购买一双‘黑色运动鞋’，价格低于 $80，并将收货地址修改为纽约”【25†L86-L92】 |
| **Mind2Web**【19†L4-L14】 | - 首个面向通用网页智能体的数据集，任务来自真实网站<br>- 支持在线评估（Online‑Mind2Web），包含 300 个实时任务、136 个流行网站<br>- 强调多步指令跟随与复杂交互 | 评估智能体在任意网站上的通用任务完成能力 | “在 GitHub 上创建一个名为 ‘test‑repo’ 的公开仓库，并添加一个 README.md 文件” |
| **VisualWebArena**【20†L4-L10】 | - 多模态网页智能体基准，任务依赖视觉信息（截图）<br>- 包含丰富的视觉接地任务，如识别图片、按钮位置等<br>- 评估智能体在真实视觉网页环境中的表现 | 评估多模态智能体在视觉丰富的网页任务中的能力 | “在纽约时报网站找到今天头条新闻的配图，并描述图中主要物体” |
| **WebVLN**【22†L4-L11】 | - 网站上的视觉‑语言导航任务，智能体需根据问题指令在网站中导航<br>- 数据集 WebVLN‑v1 包含多种网站导航场景<br>- 评估导航成功率与任务完成度 | 评估智能体在网站中基于视觉与语言指令的导航能力 | “找到‘联系我们’页面，并提取其中的邮箱地址” |
| **WebNav**【23†L4-L11】 | - 目标驱动的网页导航基准，环境为部分观测<br>- 智能体需理解自然语言指令并在网页中规划动作序列<br>- 早期经典任务，常用于测试基础导航能力 | 评估智能体在部分观测网页环境中的规划与指令跟随能力 | “导航到 Wikipedia 上关于‘人工智能’的页面，并摘录第一段内容” |

---

## 🛠️ 2. 工具使用/通用助手类基准
这类基准测试智能体调用外部工具（如搜索、计算、API）来解决实际问题的能力。

| 基准 | 核心特点 | 使用场景 | 例题 |
|------|----------|----------|------|
| **GAIA**【26†L10-L25】 | - 466 个真实世界问题，需要推理、多模态处理、网页浏览、工具使用等综合能力<br>- 对人类简单（92% 准确率），但对最强 GPT‑4 仅 15%<br>- 答案唯一、事实性，支持自动评估 | 评估通用 AI 助手在需要多步工具使用的真实任务中的表现 | “2022 年法国总统选举的获胜者出生在哪个城市？”【26†L10-L25】 |
| **ToolBench**（OpenBMB）【7†L4-L10】 | - 包含多样软件工具的真实任务基准<br>- 提供易用的基础设施（虚拟 API 服务器等）<br>- 评估 LLM 调用工具完成复杂任务的能力 | 评估智能体在工具调用与组合上的熟练度 | “使用计算器 API 计算 (3.14 * 7.2)^2，然后使用搜索 API 查找‘圆周率’的最新精确值” |
| **HSCodeComp**【2†L16-L35】 | - 首个针对深度搜索智能体在分层规则应用上的专家级电商基准<br>- 任务：根据嘈杂的商品描述预测 10 位 HS 编码（关税规则）<br>- 632 个商品条目，标注由多位电商专家完成，最佳智能体准确率仅 46.8%（人类 95%） | 评估智能体在复杂规则（如关税分类）下的深层推理与搜索能力 | “给定商品描述‘男士纯棉针织T恤，领口为V领’，预测其 10 位 HS 编码”【2†L24-L32】 |

---

## ❓ 3. 问答/检索类基准
这类基准主要评估智能体在开放域或特定领域的信息检索与问答能力。

| 基准 | 核心特点 | 使用场景 | 例题 |
|------|----------|----------|------|
| **HotpotQA**【8†L4-L10】 | - 113k 个基于 Wikipedia 的多跳问答对<br>- 需要跨多个文档推理，并提供句子级支持事实<br>- 涵盖多样的问题类型（比较、列举、因果等） | 评估模型在需要多跳推理的开放域问答中的能力 | “《了不起的盖茨比》的作者还写过哪些小说？” |
| **FEVER**【27†L16-L28】 | - 185,445 个基于 Wikipedia 的声明，需分类为 Supported/Refuted/NotEnoughInfo<br>- 要求提供证据句子（可多句、多页面）<br>- 挑战性高（最佳系统仅 31.87% 准确率） | 评估系统在事实核查与证据检索上的能力 | “斐济最大的岛屿是考艾岛。”【27†L48-L50】 |
| **TriviaQA**【28†L5-L10】 | - 超过 650k 个问答‑证据三元组，问题由琐事爱好者编写<br>- 每个问题平均提供 6 篇证据文档，适合远程监督阅读<br>- 包含阅读理解和开放域 QA 两种任务设置 | 评估模型在开放域琐事问答中的检索与阅读理解能力 | “哪位演员在《星球大战：原力觉醒》中扮演凯洛·伦？” |
| **Natural Questions**【11†L4-L8】 | - 来自 Google 搜索的真实用户问题，答案来自 Wikipedia<br>- 包含长答案（段落）和短答案（实体/日期等）<br>- 训练集 307k，开发/测试集各 8k | 评估开放域问答系统对真实用户查询的响应能力 | “谁写了《傲慢与偏见》？”（答案：“简·奥斯汀”） |
| **MS MARCO**【12†L4-L10】 | - 基于 Bing 真实搜索查询的检索/问答基准<br>- 包含段落排序、文档排序、问答等任务<br>- 数据规模大（约 500k 查询，880 万段落） | 训练与评估检索模型在真实搜索场景下的表现 | “如何冲泡咖啡？”（系统需检索相关段落回答） |
| **BEIR**【13†L4-L10】 | - 异构零样本检索基准，包含 18 个数据集、9 种任务（事实核查、QA、对话检索等）<br>- 评估模型在未见任务上的泛化能力<br>- 已成为检索模型的标准评估套件 | 评估检索模型在零样本设置下的跨任务泛化能力 | 给定查询“全球变暖的主要原因”，在文档集合中检索相关文档 |
| **MIRACL**【14†L4-L10】 | - 多语言检索基准，覆盖 18 种语言（包括中文、阿拉伯语等）<br>- 专注于 ad‑hoc 检索，所有查询与文档均经过人工标注<br>- 提供多语言检索挑战（WSDM 2023 Cup） | 评估多语言检索模型在不同语言下的检索效果 | 中文查询：“全球变暖的原因”，检索相关中文文档 |

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## 🧩 4. 组合搜索/推理类基准
这类基准专门测试智能体在组合搜索问题上的逻辑推理与规划能力。

| 基准 | 核心特点 | 使用场景 | 例题 |
|------|----------|----------|------|
| **SearchBench**【30†L22-L36】 | - 11 种独特的搜索问题类型（路径查找、谜题、子集和、排序、欠定系统等）<br>- 自动生成任意数量实例，并评估解决方案的可行性、正确性、最优性<br>- GPT‑4 在纯文本模式下仅解决 1.4%，通过 A* 代码生成提升至 11.7% | 评估 LLM 在组合搜索问题上的推理与规划能力 | “在一个 5x5 迷宫中，从起点 (0,0) 到终点 (4,4) 的最短路径是什么？（障碍物位置已知）”【30†L56-L64】 |

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## 🛒 5. 特定领域/仿真环境基准
这类基准针对特定领域（如电商、家庭环境）设计，测试智能体在仿真环境中的交互能力。

| 基准 | 核心特点 | 使用场景 | 例题 |
|------|----------|----------|------|
| **WebShop**【29†L15-L30】 | - 模拟电商网站环境，包含 118 万真实商品、12,087 条众包指令<br>- 智能体需根据文本指令搜索、筛选、购买商品<br>- 奖励自动计算，最佳模型成功率 29%（人类 59%） | 评估语言智能体在电商任务中的导航、搜索与决策能力 | “找到一款价格低于 $50 的无线蓝牙耳机，评分至少 4 星，并加入购物车”【29†L20-L25】 |
| **ALFWorld**【18†L4-L10】 | - 将文本环境（TextWorld）与具身环境（ALFRED）对齐的基准<br>- 智能体先在文本中学习抽象策略，然后在视觉环境中执行<br>- 包含 134 个家庭任务（如“在厨房找到杯子并拿到客厅”） | 评估智能体在文本与具身环境之间的迁移学习与规划能力 | “在厨房找到‘杯子’，把它拿到客厅放在桌子上” |

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## 📊 总结
以上基准覆盖了搜索智能体的不同能力维度：从**网页浏览、工具使用、问答检索**，到**组合搜索推理**以及**特定领域仿真**。选择合适的基准取决于您要评估的具体能力（如多模态交互、工具调用、多跳推理等）。这些基准大多提供公开的数据集与评估代码，可直接用于实验。

> 注：本文基于 2025 年 11 月前的公开资料整理，部分基准可能已有更新版本，建议访问对应官网获取最新信息。<｜end▁of▁sentence｜>