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Cited by 0Information Retrieval and Search BehaviorTopic ModelingArtificial Intelligence Applications

DR-Arena: an Automated Evaluation Framework for Deep Research Agents

Open access
Gao, Yiwen, Zhao, Ruochen, Deng, Yang, Zhang, Wenxuan·2026·Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2026.acl-long.1249
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Abstract

As Large Language Models (LLMs) increasingly operate as Deep Research (DR) Agents capable of autonomous investigation and information synthesis, reliable evaluation of their task performance has become a critical bottleneck.Current benchmarks predominantly rely on static datasets, which suffer from several limitations: limited task generality, temporal misalignment, and data contamination.To address these, we introduce DR-Arena, a fully automated evaluation framework that pushes DR agents to their capability limits through dynamic investigation.DR-Arena constructs real-time Information Trees from fresh web trends to ensure the evaluation rubric is synchronized with the live world state, and employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage.DR-Arena further adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges.Experiments with six advanced DR agents demonstrate that DR-Arena achieves a Spearman correlation of 0.94 with the LM-SYS Search Arena leaderboard.This represents state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication.

AI snapshot

Written from the paper’s full text.

TL;DR

DR-Arena is a fully automated evaluation framework for Deep Research agents that builds real-time Information Trees from live web trends and uses an Examiner with an Adaptive Evolvement Loop to stress-test depth and width capabilities. It achieves a Spearman correlation of 0.94 with the LM-SYS Search Arena leaderboard without manual annotation.

Key findings

  • DR-Arena reaches Spearman 0.94 and Pearson 0.74 with LMSYS Search Arena, outperforming static benchmarks; it is the only compared benchmark requiring zero human intervention.
  • Ablations: full tree yields 89.0% question quality and 88.0% rubric accuracy, versus 2.0%/8.0% for No Logic Chain and 9.0%/4.0% for Flat Context; removing rubrics drops Spearman from 0.94 to 0.83; first-round-only evaluation drops Spearman to 0.77, while Round 2 reaches 0.94 and Pearson improves from 0.68 to 0.74.
  • Cross-examiner agreement: GPT-5.2-Chat 93.02% agreement / Cohen’s κ=0.901; Claude-Opus-4.6 88.70% / κ=0.839. Human audit: 90.6% question validity, 89.1% rubric factuality, verdict κ=0.91, transition logic accuracy 96.9%, stop-condition efficiency 92.2%.
  • Efficiency and errors: average investigation rounds correlate negatively with skill gap (r = -0.61, p = 0.045); failure rates range from 50.17% (GPT-5.1-Search) to 66.82% (Claude-Opus-4.1-Search), with failure types balanced between DEPTH 32.3%, WIDTH 30.0%, BOTH 19.5%, and NONE 18.1%.

Methods

  • Framework: a closed-loop Examiner constructs Dynamic Information Trees from Google Trends seeds and live web scraping, represents them as directed graphs, expands depth/width, generates de-contextualized tasks with rubrics, and runs an Adaptive Evolvement Loop with failure tags (DEPTH, WIDTH, BOTH, NONE) and a transition matrix; Elo ratings are updated via Bradley-Terry.
  • Evaluation: six DR agents selected from 13 on LMSYS Search Arena as of Dec 3, 2025 (GPT-5.1-Search, Gemini-2.5-Pro-Grounding, o3-Search, Grok-4-Search, Perplexity-Sonar-Pro-High, Claude-Opus-4.1-Search); Gemini-3-Pro is the fixed Examiner; a Swiss-style tournament uses 4 rounds and 30 pre-constructed Information Trees; baselines include six recent benchmarks; human study uses 2 annotators on 30 matches/64 turns; cross-examiner validation uses 50 sampled matches.
  • Analysis: Spearman/Pearson correlations against LMSYS Search Arena, ablations for tree-guided generation, rubric-based judgment, and the evolvement loop, plus human audit, failure distribution, and efficiency analyses.

Limitations

  • Stated: the main pipeline relies on a single Examiner, so judge-specific preferences cannot be fully ruled out; generated rubrics can conflict with the Examiner’s parametric knowledge, risking incorrect overrides or disregard of mistakes.
  • Stated: live-web and commercial search API dependence makes exact reproduction sensitive to indexing, ranking, geoblocking, and webpage availability; source prioritization may disadvantage agents using different but reliable sources; the evidence-based rubric may undervalue creative synthesis or lateral thinking.
  • Evident: human audit and cross-examiner validation use small samples (2 annotators, 30 matches/64 turns; 50 matches), and Appendix G.2 documents a judge hallucination where parametric knowledge caused an incorrect override.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.18653/v1/2026.acl-long.1249

Cites

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Statistics

Nothing to recompute.

Claims the paper makes

2 of 4 cited sources checked · 2 supported · 0 flagged

  • Premisecitation

    Good-performing LLMs tend to perform better in LLM-as-a-judge tasks.

    “As good-performing LLMs tend to perform better in LLM-as-a-judge tasks (Zheng et al., 2023)”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Zheng et al., 2023 source
  • Findingown data

    Closely matched pairs such as Gemini-2.5-Pro vs. o3-Search (∆Elo ≈ 13.5) extend to 2.8 rounds.

    “closely matched pairs such as Gemini-2.5-Provs. o3-Search (∆Elo ≈ 13.5) extend to 2.8 rounds.”
    Quote in the paperSubstantial claim
  • Findingown data

    The Spearman correlation drops from 0.94 to 0.83 when using an intuition-based judge instead of evidence-based adjudication.

    “the Spearman correlation drops from 0.94 to 0.83”
    Quote in the paperSubstantial claim
  • Findingown data

    BOTH failures are persistent (∼20-30%) across all models.

    “The persistent BOTH failures (∼20-30%) across all models”
    Quote in the paperSubstantial claim
  • Findingnone given

    The adaptive loop optimizes computational allocation, concentrating computational resources on distinguishing closely matched models while rapidly resolving mismatches.

    “the adaptive loop optimizes computational allocation, concentrating computational resources on distinguishing closely matched models while rapidly resolving mismatches.”
    Quote in the paperSubstantial claim
  • Classified as probably not a substantial claim. 25
  • May combine two claims. 43
  • The quote only partly states this claim. 3

  • Classified as probably not a substantial claim. May combine two claims.

    Premisecitation

    Depth and width constitute two fundamental capabilities of DR agents: reasoning and information gathering.

    “Collectively, depth and width constitute two fundamental capabilities of DR agents: reasoning and information gathering.”
    Quote in the paperSubstantial claim1 of 1 sources checked
    • cites Lan et al. (2025) source

  • May combine two claims.

    Findingown data

    GPT-5.1-Search has the highest stability with a minimum failure rate of 50.17%.

    “with GPT-5.1-Searchdemonstrating the highest stability with a minimum failure rate of 50.17%.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The alternative Examiners achieve high agreement with the original Examiner, with Percent Agreement rates of 93.02% and 88.70%, and Cohen's Kappa scores of 0.901 and 0.839, respectively.

    “the alternative Examiners achieve high agreement with the original Examiner, with Percent Agreement rates of 93.02% and 88.70%, and Cohen's Kappa scores of 0.901 and 0.839, respectively.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    DR-Arena achieves approximate alignment with human judgment, recovering the exact hierarchy for most models.

    “DR-Arena achieves approximate alignment with human judgment, recovering the exact hierarchy for most models.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    96.9% of the Evolvement Loop transitions correctly follow the diagnostic logic, and 92.2% of the matches are deemed to have stopped at an efficient round count.

    “96.9% of the Evolvement Loop transitions correctly follow the diagnostic logic, and 92.2% of the matches are deemed to have stopped at an efficient round count.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The two human annotators are experts.

  • May combine two claims.

    Findingown data

    90.6% of the generated questions are confirmed to strictly adhere to the Depth and Width principles and 89.1% of the rubrics are verified as factually correct based on the source URLs.

    “90.6% of the generated questions are confirmed to strictly adhere to the Depth and Width principles and 89.1% of the rubrics are verified as factually correct based on the source URLs.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The two human annotators are experts.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Findingnone given

    Among all benchmarks, DR-Arena is the only one that requires zero human intervention, automating the ground-truth generation via Dynamic Information Trees to assess factual correctness on the live web.

    “among all benchmarks, DR-Arena is the only one that requires zero human intervention, automating the ground-truth generation via Dynamic Information Trees to assess factual correctness on the live web.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    o3-Search has the lowest DEPTH failure rate (19%) and a WIDTH failure rate of 30%.

    “it has the lowest DEPTH failure rate (19%), with limitations primarily in coverage breadth (WIDTH: 30%).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingreasoning

    The matchup between GPT-5.1-Search and o3-Search exhibits the highest investigation duration (3.07 rounds) despite a moderate Elo gap (∆Elo ≈ 44).

    “The matchup between GPT-5.1-Search and o3-Search exhibits the highest investigation duration (3.07 rounds) despite a moderate Elo gap (∆Elo ≈ 44).”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The automated verdicts achieve substantial alignment with human experts' judgments, yielding a Cohen's Kappa agreement of 0.91.

    “the automated verdicts achieve substantial alignment with human experts' judgments, yielding a Cohen's Kappa agreement of 0.91.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The two human annotators are experts.

  • May combine two claims.

    Findingown data

    DR-Arena achieves the SOTA Spearman correlation of 0.94, and a Pearson correlation of 0.74, significantly outperforming static datasets like Deep Research Bench (FutureSearch) and LiveResearch-Bench, which show negative correlations.

    “DR-Arena achieves the SOTA Spearman correlation of 0.94, and a Pearson correlation of 0.74, significantly outperforming static datasets like Deep Research Bench (FutureSearch) and LiveResearch-Bench, which show negative correlations.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    The results derived solely from the First Round are insufficient, with Spearman correlation dropping from 0.94 to 0.77.

    “The results derived solely from the First Round are insufficient, where Spearman correlation drops from 0.94 to 0.77”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The distribution of identified failure types is for the losing agents.

    “The right panel shows the distribution of identified failure types for the losing agents.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    DR-Arena achieves a Pearson correlation of 0.74 and a Spearman correlation of 0.94 with human rankings across 6 models.

    “DR-Arena (Ours) | 6 | 0.74 | 0.94”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The 30 Dynamic Information Trees constructed from Google Trends are representative of deep research tasks.
    • Rests on an assumption: Human preference rankings provide a valid reference standard for evaluating deep research agents.

  • May combine two claims. The quote only partly states this claim.

    Findingown data

    In the G.2 case study, the Judge hallucinated that Ocarina of Time won the 2005 contest and incorrectly overrode the valid Ground Truth.

    “The Judge hallucinated that Ocarina of Time won the 2005 contest.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    In G.2, Agent A correctly identified Final Fantasy VII as the 2005 winner and Chrono Trigger as the opponent.

    “Agent A: Correctly identified Final Fantasy VII as the 2005 winner and Chrono Trigger as the opponent.”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data

    GPT-5.2-Chat agrees with the original Examiner at 93.02% with Cohen's kappa 0.901 on 50 sampled matches.

    “GPT-5.2-Chat | 93.02% | 0.901”
    Quote in the paperSubstantial claim

  • The quote only partly states this claim.

    Findingown data

    Claude-Opus-4.6 agrees with the original Examiner at 88.70% with Cohen's kappa 0.839 on 50 sampled matches.

    “Claude-Opus-4.6 | 88.70% | 0.839”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The distribution of verdicts spans 789 unique rounds.

    “The left panel displays the distribution of verdicts across 789 unique rounds.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Removing the rubrics or the evolvement loop significantly degrades alignment.

    “Removing the rubrics or the evolvement loop significantly degrades alignment.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The full tournament dataset covers 789 unique interaction rounds.

    “covering 789 unique interaction rounds.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingreasoning

    In G.3, the Examiner utilized its parametric knowledge to identify the error in the Ground Truth and correctly judged the agents based on actual historical events.

    “The Examiner utilized its parametric knowledge to identify the error in the Ground Truth and correctly judged the agents based on actual historical events.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The dataset covers a diverse range of high-level categories and specific sub-niches.

    “The dataset covers a diverse range of high-level categories and specific sub-niches.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The 30 Dynamic Information Trees constructed from Google Trends are representative of deep research tasks.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    The system successfully extracted verifiable structures from various authoritative sources, with Wikipedia serving as a major hub.

    “The system successfully extracted verifiable structures from various authoritative sources, with Wikipedia serving as a major hub.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Better (49.9%) is the most frequent outcome, significantly outnumbering Much Better (32.8%).

    “The data reveals that”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    In G.3, Agent A incorrectly identified the runner-up as Master Chief.

    “Agent A (Gemini-2.5-Pro):Incorrectly identified the runner-up as Master Chief.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    In G.3, Agent B correctly identified the runner-up as Cloud Strife and noted that Link was eliminated in the semi-finals.

    “Agent B (GPT-5.1-Search):Correctly identified the runner-up as Cloud Strife and noted that”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    At the moment a verdict was reached, the Information Trees' Search Tree Depth distribution peaks at Depth 2 and has a long tail extending up to Depth 8.

    “While the distribution peaks at Depth 2 (representing standard multi-hop queries), it exhibits a long tail extending up to Depth 8.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductnone given

    DR-Arena employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage.

    “employs an automated Examiner to generate structured tasks testing two orthogonal capabilities: Deep reasoning and Wide coverage.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationown data

    This divergence could be an example of the Factuality-Fluency Trade-off: the automated Examiner strictly penalizes hallucinated numbers that human annotators may overlook, being swayed by Claude's superior writing style.

    “this divergence could be an example of the Factuality-Fluency Trade-off : Our qualitative analysis reveals that the automated Examiner strictly penalizes hallucinated numbers that human annotators may overlook, being swayed by Claude's superior writing style.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    PPL-Sonar-Pro and Grok-4's pattern indicates effective retrieval but inconsistent logical reasoning.

    “indicating effective retrieval but inconsistent logical reasoning.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    The tournament begins with a randomized initialization, followed by 4 rounds of dynamic pairing, to mitigate bias.

    “To mitigate bias, the tournament begins with a randomized initialization, followed by 4 rounds of dynamic pairing.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    DR-Arena transforms evaluation into an active stress test through Adaptive Evolvement, efficiently isolating performance boundaries and disentangling failures in logical deduction (Depth) from deficits in information aggregation (Width).

    “Beyond timeliness, it transforms evaluation into an active stress test through Adaptive Evolvement, efficiently isolating performance boundaries and disentangling failures in logical deduction (Depth) from deficits in information aggregation (Width).”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationreasoning

    This adaptive mechanism ensures the system efficiently converges to a verdict by continuously pushing agents toward their specific breakdown points.

    “This adaptive mechanism ensures the system efficiently converges to a verdict by continuously pushing agents toward their specific breakdown points.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    A linear mapping is observed between Elo scores derived from Search Arena and those derived from DR-Arena, confirming that automated metrics effectively proxy human preference.

    “We observe a linear mapping, confirming that our automated metrics effectively proxy human preference.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    DR-Arena serves as a trustworthy and scalable proxy for human adjudication, offering the community a reliable standard to benchmark the next generation of DR agents.

    “DR-Arena serves as a trustworthy and scalable proxy for human adjudication, offering the community a reliable standard to benchmark the next generation of DR agents.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The Examiner employs a de-contextualization strategy: when generating queries, the Examiner is strictly prohibited from mentioning specific filenames or website titles.

    “the Examiner employs a de-contextualization strategy: When generating queries, the Examiner is strictly prohibited from mentioning specific filenames or website titles.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    Evidence-Based Adjudication is critical for reducing judge hallucination and ensuring rigorous alignment with human standards.

    “This demonstrates that Evidence-Based Adjudication is critical for reducing judge hallucination and ensuring rigorous alignment with human standards.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    DR-Arena departs from passive evaluator paradigms by introducing an active Examiner for long-horizon Deep Research, where an Adaptive Evolvement Loop dynamically modulates task depth and width during evaluation to expose capability boundaries that static datasets or passive evaluators fail to expose.

    “DR-Arena departs from this paradigm by introducing an active Examiner for long-horizon Deep Research, where an Adaptive Evolvement Loop dynamically modulates task depth and width during evaluation to expose capability boundaries that static datasets or passive evaluators fail to expose.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    DR-Arena fundamentally departs from the static benchmark paradigm by generating Dynamic Information Trees directly from the live web, synchronizing tasks and ground truth with the current world state and eliminating both temporal misalignment and parametric memorization.

    “DR-Arena fundamentally departs from this paradigm by generating Dynamic Information Trees directly from the live web, synchronizing tasks and ground truth with the current world state and eliminating both temporal misalignment and parametric memorization.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    DR-Arena can distinguish even highly correlated models, albeit at higher computational cost.

    “demonstrating that DR-Arena can distinguish even highly correlated models, albeit at higher computational cost.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    These profiles reveal capability trade-offs beyond aggregate metrics, enabling targeted model improvements.

    “These profiles reveal capability trade-offs beyond aggregate metrics, enabling targeted model improvements.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    The distinction between Depth failure, Width failure, or both/none disentangles reasoning deficits from retrieval gaps, providing the necessary signal for the Evolvement Loop to intelligently target the specific weakness in the subsequent rounds.

    “This distinction is critical as it disentangles reasoning deficits from retrieval gaps, providing the necessary signal for the Evolvement Loop to intelligently target the specific weakness in the subsequent rounds.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    These findings validate the automated Examiner as a trustworthy, scalable proxy for human adjudication.

    “These findings validate the automated Examiner as a trustworthy, scalable proxy for human adjudication.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The two human annotators are experts.

  • May combine two claims.

    Interpretationown data

    DR-Arena achieves state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication.

    “This represents state-of-the-art alignment with human preferences without any manual efforts, validating DR-Arena as a reliable alternative for costly human adjudication.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductnone given

    DR-Arena adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges.

    “DR-Arena further adopts Adaptive Evolvement Loop, a state-machine controller that dynamically escalates task complexity based on real-time performance, demanding deeper deduction or wider aggregation until a decisive capability boundary emerges.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    The Examiner simultaneously synthesizes a list of rubrics that is factually correct and traceable to source URLs.

    “the Examiner simultaneously synthesizes a list of rubrics that is factually correct and traceable to source URLs.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    The system dynamically adjusts task complexity (Depth D and Width W) based on adjudication verdict and diagnostic failure type of the losing agent.

    “The system dynamically adjusts task complexity (Depth D and Width W ) based on adjudication verdict and diagnostic failure type of the losing agent.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    DR-Arena's judgments are highly consistent across diverse frontier LLMs, suggesting that the evaluation signal is not primarily driven by the stylistic bias of a particular judge model.

    “These results indicate that DR-Arena's judgments are highly consistent across diverse frontier LLMs, suggesting that the evaluation signal is not primarily driven by the stylistic bias of a particular judge model.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Study design and conductreasoning

    They reduce the risk by biasing tree construction toward high-reliability seed domains.

    “We reduce this risk by biasing tree construction toward high-reliability seed domains”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Fully freezing the search environment would re-introduce contamination risks.

    “fully freezing the search environment would re-introduce contamination risks.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductnone given

    The evidence-based rubric correlates well with human preference on fact-heavy research tasks, but may undervalue creative synthesis or lateral thinking that deviates from the strict logical path of the generated tree.

    “although our evidence-based rubric correlates well with human preference on fact-heavy research tasks, it may undervalue creative synthesis or lateral thinking that deviates from the strict logical path of the generated tree.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    Prior to official annotation, annotators underwent a training session involving 5 trial cases to align their understanding with evaluation rubrics.

    “Prior to the official annotation, the annotators underwent a training session involving 5 trial cases to strictly align their understanding with our evaluation rubrics.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    DR-Arena operates over the live web and depends on commercial search APIs, so exact reproduction may be affected by temporal changes in indexing, ranking, geoblocking, and webpage availability.

    “because DR-Arena operates over the live web and depends on commercial search APIs, exact reproduction may be affected by temporal changes in indexing, ranking, geoblocking, and webpage availability.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductown data

    In Experiment I, annotators were presented with outputs from three configurations in a blinded, randomized order to prevent bias.

    “annotators were presented with outputs from three configurations (Full Tree, Flat Context, No Logic Chain) in a blinded, randomized order to prevent bias.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Two annotators, bachelor's students in Computer Science with a focus on NLP and native-level English proficiency, were recruited for the human study.

    “We recruited two annotators for this study.Both annotators are current bachelor's students in Computer Science with a focus on Natural Language Processing (NLP).They possess native-level proficiency in English”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    When generated ground-truth rubrics conflict with the Examiner's internal parametric knowledge, there is a risk that a judge could override the provided rubrics due to strong parametric priors.

    “when the generated ground-truth rubrics conflict with the Examiner's internal parametric knowledge, there arises a risk that a judge could override the provided rubrics due to strong parametric priors.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The tasks effectively test agents' context window management and retrieval recall.

    “effectively testing the agents' context window management and retrieval recall.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    In Experiment I, 50 cases were randomly sampled from the test set.

    “We randomly sampled 50 cases from the test set.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    DR-Arena relies on a single Examiner in the main evaluation pipeline, so judge-specific preferences cannot be fully ruled out.

    “DR-Arena still relies on a single Examiner in the main evaluation pipeline, so judge-specific preferences cannot be fully ruled out.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductown data

    In Experiment II, 30 full-match logs comprising 64 interaction turns were randomly sampled.

    “We randomly sampled 30 full-match logs, comprising a total of 64 interaction turns.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    The divergence between human and automated rankings stems from differing evaluation priorities: the Automated Examiner enforces a strict penalty on hallucinated details, whereas human crowds tend to prioritize response fluency and presentation.

    “The results suggest that this divergence stems from differing evaluation priorities: the Automated Examiner enforces a strict penalty on “hallucinated details,” whereas human crowds tend to prioritize response fluency and presentation.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Human preference rankings provide a valid reference standard for evaluating deep research agents.

  • May combine two claims.

    Interpretationown data

    This balance validates the effectiveness of the Depth & Width task generation strategy, confirming that the framework successfully exerts equal pressure on both reasoning and information aggregation.

    “This balance validates the effectiveness of our”
    Quote in the paperSubstantial claim
63 of 63

  • The paper relies on this without support.

    Gemini-3-Pro is currently the best model on LMSYS Text Arena.

    “we choose Gemini-3-Pro as the fixed Examiner, which is currently the best model on LMSYS Text Arena.”

  • The paper relies on this without support.

    The 30 pre-constructed Information Trees are derived from diverse real-world websites and are representative.

    “each pairing competes across the same set of 30 pre-constructed Information Trees derived from diverse real-world websites”

  • The paper relies on this without support.

    The two human annotators are experts.

    “two expert annotators audited a random sample of 30 full-match logs”

  • The paper relies on this without support.

    Existing static benchmarks suffer from temporal misalignment, penalizing agents that retrieve current information contradicting outdated ground truth.

    “Existing static benchmarks suffer from temporal misalignment, penalizing agents that retrieve current information contradicting outdated ground truth.”

  • The paper relies on this without support.

    The 30 Dynamic Information Trees constructed from Google Trends are representative of deep research tasks.

    “We utilized a total of 30 Dynamic Information Trees constructed from Google Trends.”

  • The paper relies on this without support.

    Human preference rankings provide a valid reference standard for evaluating deep research agents.

    “As noted in Section 4.2, a discrepancy exists between human rankings (preferring Claude-Opus) and DR-Arena's automated rankings (preferring Perplexity-Sonar).”
6 of 6

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Pressure test leaves depth unchanged

    Minor

    Round 1 calls for depth and width to increase by one, but the next round remains at Depth 2; only width increases.

1 of 1

Cites

The publisher hasn’t deposited this paper’s reference list.

Research works printed but not linked: 3

  1. Ralph Allan Bradley and Milton E Terry. 1952. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika, 39(3-4):324- 345. · DOI · Paper page
  2. Haiyuan Wan, Chen Yang, Junchi Yu, Meiqi Tu, Jiaxuan Lu, Di Yu, Jianbao Cao, Ben Gao, Jiaqing Xie, Aoran Wang, Wenlong Zhang, Philip Torr, and Dongzhan Zhou. 2026. Deep research arena: The first exam of llms' research abilities via seminar-grounded tasks. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 40, pages 33341-33349. · DOI
  3. Colin White, Samuel Dooley, Manley Roberts, Arka Pal, Benjamin Feuer, Siddhartha Jain, Ravid Shwartz-Ziv, Neel Jain, Khalid Saifullah, Sreemanti Dey, Shubh- Agrawal, Sandeep Singh Sandha, Siddartha V. Naidu, Chinmay Hegde, Yann LeCun, Tom Goldstein, Willie Neiswanger, and Micah Goldblum. 2025. Livebench: A challenging, contamination-limited LLM bench- mark. In The Thirteenth International Conference on Learning Representations.

Authors
Gao, Y., Zhao, R., Deng, Y., & Zhang, W.
Affiliations
  • National University of Singapore (SG)
  • Nanyang Technological University (SG)
  • Singapore Management University (SG)
  • Singapore University of Technology and Design (SG)
Venue
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2026
Pages
27130-27152
DOI
10.18653/v1/2026.acl-long.1249
OpenAlex
W7166828574
Semantic Scholar
917bf79e822419eacdbed9939467b4cc8e716f18
Cited by
0
References
0
FWCI
0.00
Type
conference paper
Open access
Licence
cc-by
Locations
  • Annual Meeting of the Association for Computational Linguistics (ACL) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Research Foundation
  • National Research Foundation Singapore
Topics
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
deep research agents, large language models, automated assessment, benchmark design, deep reasoning, wide coverage, human preference alignment

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-23.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-23.

Cited sources are checked by gpt-6-sol.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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