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ARXIV:2604.11307 · AI BENCHMARKING · SUBMITTED 14 APR · 16:49 UTC · FRESHNESS STALE
ARXIV:2604.11307AI BENCHMARKINGSUBMITTED 14 APR · 16:49 UTCFRESHNESS STALELei Xiong · Huaying Yuan · Zheng Liu · Zhao Cao · Zhicheng Dou · arXiv
A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems.
Opportunity summary
Pain A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple…
Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, multi-modal, multi-document scientific reasoning remains underexplored and lacks systematic evaluation. Code availability is flagged in the production record; the public repository…
AI Benchmarking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems.
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10.48550/arXiv.2604.11307A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems.
Abstract
Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers, including their text, tables, and figures. As a result, multi-modal, multi-document scientific reasoning remains underexplored and lacks systematic evaluation. To address this gap, we introduce PaperScope, a multi-modal multi-document benchmark designed for agentic deep research. PaperScope presents three advantages: (1) Structured scientific grounding. It is built on a knowledge graph of over 2,000 AI papers spanning three years, providing a structured foundation for research-oriented queries. (2) Semantically dense evidence construction. It integrates semantically related key information nodes and employs optimized random-walk article selector to sample thematically coherent paper sets, thereby ensuring adequate semantic density and task complexity. (3) Multi-task evaluation of scientific reasoning. It contains over 2,000 QA pairs across reasoning, retrieval, summarization, and problem solving, enabling evaluation of multi-step scientific reasoning. Experimental results show that even advanced systems such as OpenAI Deep Research and Tongyi Deep Research achieve limited scores on PaperScope, highlighting the difficulty of long-context retrieval and deep multi-source reasoning. PaperScope thus provides a rigorous benchmark alongside a scalable pipeline for constructing large-scale multi-modal, multi-source deep research datasets.
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unverified0 refs; 3 sources; 50% coverage.
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Derived fallback: Estimated from adjacent evidence; not verified from source.
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Dimensions overall score 7.0
PROBLEM
A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from...
METHOD
Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, multi-modal, multi-document scientific reasoning remains underexplored and lacks systematic evaluation. Code availability is flagged in the production record; the public repository link still...
WHY NOW
AI Benchmarking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers, including their text, tables, and figures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers, including their text, tables, and figures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, multi-modal, multi-document scientific reasoning remains underexplored and lacks systematic evaluation. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Benchmarking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A multi-modal, multi-document benchmark for evaluating AI agents in deep scientific research, revealing limitations in current advanced systems.
Segment
AI Benchmarking
Adoption evidence
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Commercial read
7.0/10 public viability
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proof status
unverified
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stale
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passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
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Gaps
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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People
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ARTIFACTS
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DEFENSIBILITY
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