ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents

• URL: http://arxiv.org/abs/2511.07685v1
• Date: Wed, 12 Nov 2025 01:11:26 GMT
• Title: ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents
• Authors: Manasi Sharma, Chen Bo Calvin Zhang, Chaithanya Bandi, Clinton Wang, Ankit Aich, Huy Nghiem, Tahseen Rabbani, Ye Htet, Brian Jang, Sumana Basu, Aishwarya Balwani, Denis Peskoff, Marcos Ayestaran, Sean M. Hendryx, Brad Kenstler, Bing Liu,
• Abstract summary: Deep Research (DR) is an emerging agent application that leverages large language models to address open-ended queries.<n>We introduce ResearchRubrics, a standardized benchmark for DR built with over 2,800+ hours of human labor.<n>We also propose a new complexity framework for categorizing DR tasks along three axes: conceptual breadth, logical nesting, and exploration.
• Score: 11.666923792025313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Research (DR) is an emerging agent application that leverages large language models (LLMs) to address open-ended queries. It requires the integration of several capabilities, including multi-step reasoning, cross-document synthesis, and the generation of evidence-backed, long-form answers. Evaluating DR remains challenging because responses are lengthy and diverse, admit many valid solutions, and often depend on dynamic information sources. We introduce ResearchRubrics, a standardized benchmark for DR built with over 2,800+ hours of human labor that pairs realistic, domain-diverse prompts with 2,500+ expert-written, fine-grained rubrics to assess factual grounding, reasoning soundness, and clarity. We also propose a new complexity framework for categorizing DR tasks along three axes: conceptual breadth, logical nesting, and exploration. In addition, we develop human and model-based evaluation protocols that measure rubric adherence for DR agents. We evaluate several state-of-the-art DR systems and find that even leading agents like Gemini's DR and OpenAI's DR achieve under 68% average compliance with our rubrics, primarily due to missed implicit context and inadequate reasoning about retrieved information. Our results highlight the need for robust, scalable assessment of deep research capabilities, to which end we release ResearchRubrics(including all prompts, rubrics, and evaluation code) to facilitate progress toward well-justified research assistants.

Related papers

• AgentIR: Reasoning-Aware Retrieval for Deep Research Agents [76.29382561831105]
  Deep Research agents generate explicit natural language reasoning before each search call.<n> Reasoning-Aware Retrieval embeds the agent's reasoning trace alongside its query.<n>DR- Synth generates Deep Research retriever training data from standard QA datasets.<n>AgentIR-4B achieves 68% accuracy with the open-weight agent Tongyi-DeepResearch.
  arXiv  Detail & Related papers  (2026-03-04T18:47:26Z)
• DR-Arena: an Automated Evaluation Framework for Deep Research Agents [35.99095633093855]
  Large Language Models (LLMs) increasingly operate as Deep Research (DR) Agents capable of autonomous investigation and information synthesis.<n>Current benchmarks predominantly rely on static datasets, which suffer from limited task generality, temporal misalignment, and data contamination.<n>We introduce DR-Arena, a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation.
  arXiv  Detail & Related papers  (2026-01-15T15:28:21Z)
• DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation [56.886936435727854]
  DeepResearchEval is an automated framework for deep research task construction and agentic evaluation.<n>For task construction, we propose a persona-driven pipeline generating realistic, complex research tasks anchored in diverse user profiles.<n>For evaluation, we propose an agentic pipeline with two components: an Adaptive Point-wise Quality Evaluation that dynamically derives task-specific evaluation dimensions, criteria, and weights conditioned on each generated task, and an Active Fact-Checking that autonomously extracts and verifies report statements via web search, even when citations are missing.
  arXiv  Detail & Related papers  (2026-01-14T18:38:31Z)
• DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Report [36.25273583677749]
  We introduce Deep Research Bench II, a new benchmark for evaluating deep-research systems.<n>For each task, a system must produce a long-form research report that is evaluated by a set of 9430 fine-grained binary rubrics.<n>We evaluate several state-of-the-art deep-research systems on Deep Research Bench II and find that even the strongest models satisfy fewer than 50% of the rubrics.
  arXiv  Detail & Related papers  (2026-01-13T13:18:39Z)
• Dr.Mi-Bench: A Modular-integrated Benchmark for Scientific Deep Research Agent [52.876617746453995]
  Dr.Mi-Bench is a Modular-integrated benchmark for scientific deep research (DR) agents.<n>Dr.Mi-Eval is a novel modular-integrated evaluation paradigm.
  arXiv  Detail & Related papers  (2025-11-30T17:16:47Z)
• Deep Research: A Systematic Survey [118.82795024422722]
  Deep Research (DR) aims to combine the reasoning capabilities of large language models with external tools, such as search engines.<n>This survey presents a comprehensive and systematic overview of deep research systems.
  arXiv  Detail & Related papers  (2025-11-24T15:28:28Z)
• AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite [75.58737079136942]
  We present AstaBench, a suite that provides the first holistic measure of agentic ability to perform scientific research.<n>Our suite comes with the first scientific research environment with production-grade search tools.<n>Our evaluation of 57 agents across 22 agent classes reveals several interesting findings.
  arXiv  Detail & Related papers  (2025-10-24T17:10:26Z)
• A Rigorous Benchmark with Multidimensional Evaluation for Deep Research Agents: From Answers to Reports [24.09178055088843]
  Deep Research Agents (DRAs) exhibit the capabilities for task decomposition, cross-source retrieval, multi-stage reasoning, and structured output.<n>This paper introduces a rigorous benchmark and a multidimensional evaluation framework tailored to DRAs and report-style responses.<n>The framework enables comprehensive evaluation of long-form reports generated by DRAs, incorporating integrated scoring metrics for semantic quality, topical focus, and retrieval trustworthiness.
  arXiv  Detail & Related papers  (2025-10-02T16:40:02Z)
• DRBench: A Realistic Benchmark for Enterprise Deep Research [81.49694432639406]
  DRBench is a benchmark for evaluating AI agents on complex, open-ended deep research tasks in enterprise settings.<n>We release 15 deep research tasks across 10 domains, such as Sales, Cybersecurity, and Compliance.
  arXiv  Detail & Related papers  (2025-09-30T18:47:20Z)
• WebWeaver: Structuring Web-Scale Evidence with Dynamic Outlines for Open-Ended Deep Research [73.58638285105971]
  This paper tackles textbfopen-ended deep research (OEDR), a complex challenge where AI agents must synthesize vast web-scale information into insightful reports.<n>We introduce textbfWebWeaver, a novel dual-agent framework that emulates the human research process.<n>Our framework establishes a new state-of-the-art across major OEDR benchmarks, including DeepResearch Bench, DeepConsult, and DeepResearchGym.
  arXiv  Detail & Related papers  (2025-09-16T17:57:21Z)
• Deep Research: A Survey of Autonomous Research Agents [33.96146020332329]
  The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks.<n>To overcome these limitations, the paradigm of deep research has been proposed, wherein agents actively engage in planning, retrieval, and synthesis to generate comprehensive and faithful analytical reports grounded in web-based evidence.<n>We provide a systematic overview of the deep research pipeline, which comprises four core stages: planning, question developing, web exploration, and report generation.
  arXiv  Detail & Related papers  (2025-08-18T09:26:14Z)
• Characterizing Deep Research: A Benchmark and Formal Definition [24.523394260858822]
  We propose a formal characterization of the deep research (DR) task and introduce a benchmark to evaluate the performance of DR systems.<n>We argue that the core defining feature of deep research is not the production of lengthy report-style outputs, but rather the high fan-out over concepts required during the search process.
  arXiv  Detail & Related papers  (2025-08-06T08:09:28Z)
• Benchmarking Deep Search over Heterogeneous Enterprise Data [73.55304268238474]
  We present a new benchmark for evaluating a form of retrieval-augmented generation (RAG)<n>RAG requires source-aware, multi-hop reasoning over diverse, sparsed, but related sources.<n>We build it using a synthetic data pipeline that simulates business across product planning, development, and support stages.
  arXiv  Detail & Related papers  (2025-06-29T08:34:59Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)
• DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents [30.768405850755602]
  DeepResearch Bench is a benchmark consisting of 100 PhD-level research tasks.<n> evaluating Deep Research Agents is inherently complex and labor-intensive.<n>We propose two novel methodologies that achieve strong alignment with human judgment.
  arXiv  Detail & Related papers  (2025-06-13T13:17:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.