DeepResearch Arena Benchmark

• DeepResearch Arena is a large-scale evaluation benchmark that rigorously assesses research agents through authentic, seminar-derived research tasks.
• It employs a multi-agent hierarchical task generation system to extract and translate expert dialogue into traceable, high-fidelity research challenges.
• The benchmark spans over 10,000 tasks across 12 disciplines, revealing significant performance gaps and guiding future model improvements.

DeepResearch Arena is a large-scale evaluation benchmark and high-fidelity dataset focused on rigorously assessing the general research capabilities of LLMs and deep research agents. Unlike prior benchmarks, which often rely on static, crowdsourced, or search-derived tasks, DeepResearch Arena grounds its research challenges in academic seminar discourse, emulating real-world environments where substantive research questions emerge organically through expert interaction. This design ensures that the tasks measure not only span-the-corpus retrieval or expository competence, but also multi-stage research workflows—encompassing literature synthesis, methodological ideation, and empirical planning—closely mirroring the arc of authentic scholarly inquiry (Wan et al., 1 Sep 2025).

1. Motivation and Foundational Premises

DeepResearch Arena addresses the longstanding challenge of evaluating deep research agents beyond superficial question-answering or document retrieval. Prior work has identified critical limitations in benchmarks that (i) overfit static, factoid-style tasks, (ii) fail to capture the open-ended, interactive character of real research, and (iii) expose models to data leakage through canonical benchmarks and widely available corpora (Java et al., 6 Aug 2025, FutureSearch et al., 6 May 2025). By leveraging transcripts from more than 200 academic seminars, the Arena focuses on frontiers of disciplinary and interdisciplinary debate, thus curating tasks that genuinely reflect evolving research interests, scholarly knowledge gaps, and the emergence of novel methodologies (Wan et al., 1 Sep 2025).

The principal technical innovation underpinning DeepResearch Arena is the Multi-Agent Hierarchical Task Generation (MAHTG) system. This architecture structures the task creation process to (a) extract research-worthy inspirations from expert dialogue, (b) translate these inspirations into traceable, quality-controlled research tasks, and (c) filter noise and generic conversations to preserve high signal-to-noise ratios. Collectively, this ensures that the resulting task corpus provides both intellectual provenance (traceability of origin) and granularity of challenge, supporting robust downstream evaluation.

2. Dataset Construction via MAHTG

The MAHTG pipeline operates on large-scale seminar transcripts, processing unstructured speech to surface "research-worthy inspirations." The pipeline operates through the following hierarchical stages:

1. Segmentation: Seminar transcripts are segmented by turn or topic, isolating self-contained, potentially research-inspiring candidate excerpts.
2. Inspiration Extraction: Specialized agentic annotators apply selection heuristics—potentially learned or rule-based—to distinguish dialogue snippets that reflect either an unresolved question, methodological suggestion, or deep conceptual challenge.
3. Translation to Tasks: Extracted inspirations are transformed into explicit research tasks. This translation phase employs multi-agent collaboration to reformulate inspirations into well-posed research objectives (e.g., "Synthesize prior literature on X," "Propose empirical designs to test Y," "Compare the merits of approaches Z1 and Z2"), ensuring that each task is actionable, specific, and appropriate for agent-based research workflows.
4. Noise Filtering and Traceability Validation: A second agentic layer cross-validates each task against the original transcript, filtering conversational noise, discarding non-research or redundant prompts, and preserving task-to-source alignment for auditability.

The resulting benchmark comprises over 10,000 research tasks, derived from more than 200 seminars and spanning 12 academic disciplines—including, but not limited to, literature, history, and core sciences (Wan et al., 1 Sep 2025). This scale and disciplinary breadth are unattainable via manual crowdsourcing or hand-crafting, and the traceability of origin allows researchers to audit, revise, or further annotate tasks as needed.

3. Task Taxonomy and Disciplinary Breadth

The DeepResearch Arena's task inventory exhibits substantial diversity, supporting the evaluation of deep research agents against a taxonomically structured space of scientific, methodological, and interpretive challenges. Tasks range from classical literature reviews (e.g., "Survey conceptualizations of cognitive architectures arising in recent AI seminars"), to design challenges ("Propose evaluation metrics for robustness in social robotics"), to open questions of historical causality/counterfactuals, and domain-transfer problems. Because seminar-derived prompts often reference emergent topics, rare phenomena, or context-specific debates, they test for model capabilities in:

• Literature Synthesis: Integrating and summarizing distributed research across multiple sources under novel conceptual frameworks.
• Methodological Design: Articulating or evaluating scientific procedures (e.g., new experimental set-ups, measurement paradigms, or models).
• Empirical Verification: Proposing empirical approaches, data acquisition strategies, or validation criteria under real-world constraints.

Table 1 summarizes the scope of the dataset.

Dimension	Value	Description
Number of research tasks	>10,000	Curated from seminar discourse
Number of seminars	>200	Spanning disciplinary and interdisciplinary
Disciplines covered	12 (e.g., literature, science)	Humanities, sciences, social sciences, etc.
Task length	Variable	Typically multi-sentence, often multi-part

4. Evaluation Protocols and Benchmarking Methodology

DeepResearch Arena is designed to rigorously challenge state-of-the-art research agents and LLMs. The evaluation protocol emphasizes both breadth (coverage over varied domains) and depth (the ability to follow multi-stage workflows). Evaluators benchmark agents' outputs in relation to:

• Completeness of Literature Synthesis: Does the agent recover core trends, unresolved debates, and canonical references?
• Methodological Plausibility: Are proposed methodological innovations faithful to discipline standards and tailored to the research context?
• Empirical Falsifiability: Can agent outputs be mapped onto testable empirical procedures, not just rhetorical suggestion?

The traceability of tasks to their seminar origins supports both automated and human-in-the-loop evaluation. Performance is typically assessed by expert judges, who score agent responses along axes such as coverage, insight, rigor, and originality. Quantitative performance gaps have been observed among models, with current state-of-the-art agents (e.g., those based on Gemini, GPT-4o, or analogous architectures) achieving widely varying coverage and depth (Wan et al., 1 Sep 2025).

5. Empirical Findings and Performance Gaps

Evaluation using DeepResearch Arena reveals that even best-in-class deep research agents exhibit substantive performance shortfalls. Notable empirical observations include:

• Challenging Query Construction: The seminar-derived tasks often demand cross-domain synthesis or link topics not typically contiguous in curated datasets, leading to failure modes not observed in factoid or traditional QA settings.
• Model Gaps: Large gaps in both recall (coverage of relevant literature/methods) and precision (accuracy/factual grounding) are observable, with state-of-the-art models (e.g., Gemini 2.5 Pro, OpenAI o3, Claude 3.7 Sonnet) each exhibiting modality-dependent failures.
• Disciplinary Biases: Certain models excel in particular domains (scientific synthesis) but underperform in more open-ended or humanistic contexts, reinforcing the need for broad, multidisciplinary benchmarks (Wan et al., 1 Sep 2025).

Preliminary model rankings indicate no single dominant agent across all disciplines and task types, and failure cases point toward deficits in both deep literature search and coherent argument development.

6. Significance and Future Directions

The introduction of DeepResearch Arena marks a significant methodological advance in the systematic, large-scale evaluation of deep research agents. By grounding tasks in the actual discourse of active researchers, the Arena overcomes the twin limitations of static QA corpora and hypothetical challenge sets. This enables the faithful assessment of agentic research workflows at the frontiers of formal science and the humanities.

Future research directions suggested by empirical findings from DeepResearch Arena include:

• Enhanced Multi-Agent Orchestration: To better decompose and tackle multi-stage research problems, enabling dynamic agent collaboration during literature review, methodological proposal, and synthesis.
• Meta-Reasoning and Traceability: Improving agents’ ability to maintain traceable, auditable lines of reasoning, especially in complex, interdisciplinary contexts.
• Task Adaptation and Personalization: Tailoring agent workflows based on individual research contexts or disciplinary conventions, building on MAHTG-derived insights.
• Benchmark Extension: Including additional evaluation protocols (e.g., adversarial task construction or longitudinal tracking of agent improvement) to drive deeper diagnostic assessment.

The adoption of DeepResearch Arena is poised to raise the bar for agentic research evaluation and to inform the architecture, training, and deployment of next-generation research assistants in both academic and applied contexts (Wan et al., 1 Sep 2025).