PaperlayerView exact revision on arXiv

Source-linked AI summary

AI4Research: A Survey of Artificial Intelligence for Scientific Research

Qiguang Chen, Mingda Yang, Libo Qin, Jinhao Liu, Zheng Yan, Jiannan Guan, Dengyun Peng, Yiyan Ji, Hanjing Li, Mengkang Hu, Yimeng Zhang, Yihao Liang, Yuhang Zhou, Jiaqi Wang, Zhi Chen, Wanxiang Che

arXiv:2507.01903v2cs.CLcs.AI

TL;DR

AI4Research lacks a comprehensive survey despite rapid advances in AI for scientific research, limiting systematic understanding of the field. This paper provides a unified survey with a five-task taxonomy, future directions, and applications and resources, while identifying challenges involving creativity and cross-domain knowledge transfer.

  • Problem

    Comprehensive surveys systematically analyzing AI-driven research factors and developments remain lacking, despite substantial advances in AI for scientific research.

  • Method

    The paper presents a comprehensive AI4Research survey organized around a five-area taxonomy and compiles applications, tools, datasets, and other research resources.

  • Results

    The survey identifies future research directions spanning interdisciplinary models, ethics and bias, explainability, adaptive experiments, and societal considerations.

  • Takeaways & Limitations

    AI4Research provides a unified framework and resource collection for examining AI applications across scientific comprehension, surveys, discovery, writing, and reviewing.

  • Takeaways & Limitations

    AI assistance may boost short-term creativity during supported tasks while hampering independent creative performance when users work unassisted.

Abstract

from arXiv · show

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct research processes across a wide range of scientific disciplines. Despite these significant strides, a comprehensive survey on AI for Research (AI4Research) remains absent, which hampers our understanding and impedes further development in this field. To address this gap, we present a comprehensive survey and offer a unified perspective on AI4Research. Specifically, the main contributions of our work are as follows: (1) Systematic taxonomy: We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research. (2) New frontiers: Then, we identify key research gaps and highlight promising future directions, focusing on the rigor and scalability of automated experiments, as well as the societal impact. (3) Abundant applications and resources: Finally, we compile a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools. We hope our work will provide the research community with quick access to these resources and stimulate innovative breakthroughs in AI4Research.

1. Introduction

Recent AI advances motivate AI4Research, but comprehensive surveys of AI-driven research remain lacking. This paper addresses the gap with a five-area taxonomy, future research directions, and multidisciplinary applications and resources.

  • AI advances in reasoning and experimental coding have stimulated efforts to develop systems for innovative scientific research.
  • The survey responds to a lack of comprehensive analysis of AI-driven research developments that impedes continued progress.
  • AI4Research organizes research support into scientific comprehension, academic surveys, scientific discovery, academic writing, and academic reviewing.The taxonomy covers AI tools that enhance or automatically execute stages of the research process.
  • The survey identifies future directions including interdisciplinary models, ethical and bias mitigation, explainability, and adaptive systems for dynamic experiments.
  • The paper compiles multidisciplinary applications and resources spanning frameworks, datasets, collaborative platforms, and academic tools.These resources cover natural sciences, applied science, and social sciences.

2. The Definition of AI4Research

AI4Research applies AI to improve, accelerate, and partially automate research through five core tasks, modeled as a composed research workflow. Its modules cover comprehension, surveys, discovery, writing, and peer reviewing, with formal objectives for knowledge, survey quality, and research efficiency.

  • AI4Research applies artificial intelligence to improve, accelerate, and partially automate research across disciplines.
  • Its five core tasks are Scientific Comprehension, Academic Survey, Scientific Discovery, Academic Writing, and Peer Reviewing.
  • The taxonomy organizes research tools and literature across scientific comprehension, academic surveys, discovery, writing, and reviewing, including semi- and fully automatic subtasks.
  • The framework represents the overall research process as a functional composition in which one task’s output becomes the next task’s input.
  • Scientific Comprehension: Scientific Comprehension extracts and interprets texts, figures, and metadata using textual and table-and-chart comprehension functions to produce knowledge.
  • Academic Survey: Academic Surveys retrieve relevant literature and generate thematic clusters and summaries, optimizing relevance, coverage, and clarity against domain requirements.

3. AI for Scientific Comprehension

AI for Scientific Comprehension covers methods that extract, interpret, synthesize, and critically evaluate scientific information across textual, tabular, and chart content. The survey distinguishes semi-automatic systems guided by human questions from fully automatic systems that independently process scientific knowledge.

  • Scope: Scientific comprehension extracts, understands, and synthesizes literature to accelerate knowledge acquisition and automatic research processing.The survey divides it into textual comprehension and table-and-chart comprehension.
  • Textual Scientific Comprehension: Textual scientific comprehension involves identifying concepts, interpreting terminology, and critically evaluating scientific texts.The survey further distinguishes semi-automatic and fully automatic levels of automation.
  • Semi-Automatic Scientific Comprehension: Semi-automatic systems answer manually created questions about long-context scientific content, using human-guided, tool-augmented, or self-guided approaches.Human-guided systems use iterative dialogue, tool-augmented systems invoke external resources, and self-guided systems answer single-turn publication questions.
  • Full-Automatic Scientific Comprehension: Fully automatic comprehension enables AI to read and understand scientific knowledge without human questions or intervention.Its scope can include autonomous question formulation, answering, and some scientific discovery or idea mining.
  • Automatic Comprehension Methods: Automatic methods deepen comprehension through article summarization, self-questioning, self-reflection, and Socratic problem decomposition.These methods generate summaries or questions and refine outputs through reflection, self-critique, or external retrieval.
  • Table and Chart Comprehension: Multimodal comprehension extends beyond text to tables and charts for scientific question answering, summarization, and data interpretation.Research also develops datasets and instruction-tuned models for table and chart understanding.

4. AI for Academic Survey

AI for Academic Survey applies AI to retrieve relevant literature and generate structured survey reports. The workflow progresses from retrieval through roadmap mapping and section-level generation to document-level survey construction.

  • Overview: AI for Academic Survey systematically reviews and summarizes literature to keep researchers current and identify relevant studies.The survey presents it as a pre-writing research process supporting academic writing and AI4Research.
  • Overview Report Generation: Overview Report Generation follows a sequence of research roadmap mapping, section-level related-work generation, and document-level survey generation.Figure 4 presents retrieval and report generation as the two primary stages.
  • Related Work Retrieval: Related Work Retrieval identifies foundational and novel papers aligned with evolving research objectives through semantic, graph-guided, or LLM-augmented paradigms.These paradigms respectively use semantic similarity, scholarly entity graphs, or language-model agents and pipelines.
  • Research Roadmap Mapping: Research Roadmap Mapping cleans, integrates, and depicts topic development from broad literature to expose trends, gaps, and future directions.Hierarchical organization can improve survey coherence, while interactive and graph-based structures refine knowledge organization.
  • Section-level Related Work Generation: Section-level related-work generation targets the structure of scientific papers using extractive or generative approaches.Extractive methods select and order salient sentences, whereas generative methods structure citations and produce connecting text with varying human guidance.
  • Document-level Survey Generation: Document-level survey generation automates systematic literature reviews through staged generation, plan-based search, and reference-aware structuring.The survey points to SurveyBench comparisons using reference, outline, and content quality metrics.

5. AI for Scientific Discovery

AI for Scientific Discovery uses existing knowledge and feedback to generate and evaluate research ideas, analyze theories, and conduct experiments. The survey organizes ideation around internal knowledge, external knowledge or environments, and collaborative discussion.

  • Overview: AI for Scientific Discovery generates hypotheses, theories, and ideas while automating idea generation, assessment, theoretical analysis, and experimental design.Its stated goal is to expedite research and guide new directions for complex scientific challenges.
  • Idea Mining: Idea mining is presented as crucial for innovative research, with LLMs showing creativity and potential for automated scientific discovery.The survey groups current approaches by their information sources and collaboration patterns.
  • Internal Knowledge: Internal-knowledge ideation uses pretrained parameters and prompts to extract candidate concepts without external data.Some methods adjust decoding temperature or apply constraints to explore distinct idea spaces.
  • External Knowledge: External-knowledge ideation incorporates publication metadata, citation networks, or knowledge graphs to align hypotheses with current domain developments.These methods organize scholarly information to support knowledge extraction and idea mining.
  • External Environment Feedback: External-environment feedback treats ideation as an interactive loop in which AI proposes experiments, receives outcomes, and refines subsequent ideas.This contrasts with static document mining by incorporating experimental or simulated results.
  • Team Discussion: Team-discussion approaches use AI-AI or human-AI collaboration to critique, recombine, and refine ideas into richer portfolios.Human researchers may curate intermediate artifacts, while agents exchange feedback across ideation, experiment design, and interpretation.
  • Team Discussion: LLM assistance can improve short-term creativity during supported tasks but may hinder independent creativity when users later work unassisted.This finding raises concerns about longer-term effects on human creativity and cognitive abilities.

5.2. Novelty & Significance Assessment

Novelty and significance assessment evaluates the originality and impact of ideas or scholarly papers. The survey describes traditional predictive methods, LLM-based reasoning approaches, and human-AI collaboration, while noting risks from purely LLM-augmented assessment.

  • Scope: Novelty and significance assessment evaluates the originality and impact of ideas and scholarly papers.The survey organizes approaches into traditional methods, LLM-based methods, and human-AI collaboration methods.
  • Traditional Methods: Traditional approaches train models to classify or regressively assess novelty and significance.SAPPhIRE, for example, uses a causality ontology to quantify novelty in design problems through textual similarity.
  • LLM-Based Methods: LLM-based approaches can decompose complex reasoning into interpretable viewpoint nodes to support more robust assessment.GraphEval is described as a lightweight graph-based framework for reasoning evaluation.
  • Human-AI Collaboration: Purely LLM-augmented novelty assessments may overestimate creativity and produce homogenized evaluations.The survey therefore includes human-AI collaboration as a distinct assessment approach.

5.3. Theory Analysis

Theory analysis evaluates whether scientific hypotheses align with established principles through claim formalization, evidence collection, verification, and theorem proving. These components support structured and logically grounded assessment of scientific claims.

  • Overview: Theory analysis evaluates whether hypotheses align with established scientific principles.The section frames theory analysis as an AI-supported assessment of hypothesis validity.
  • Claim Formalization: Scientific claim formalization converts natural-language assertions into structured representations for systematic verification.Approaches range from template-based pipelines to PCFG-based frameworks and LLM-refined templates.
  • Evidence Collection: Scientific evidence collection identifies, retrieves, and curates data sources that support or challenge research claims.Recent work addresses source quality, retrieval configuration, missing information, and retrieval errors.
  • Verification Analysis: Scientific verification analysis assesses claims for logical coherence, factual consistency, and robustness against existing evidence.The literature also emphasizes domain expertise and stepwise pipelines to improve reliability and interpretability.
  • Theorem Proving: Theorem proving develops algorithms and generative models that autonomously generate and verify formal mathematical proofs.Retrieval-augmented methods can prioritize trivial intermediate conjectures, creating a performance challenge.

5.4. Scientific Experiment Conduction

Scientific experiment conduction uses AI to design, execute, manage, simulate, and analyze experiments, with the broader goal of reducing human involvement. The section distinguishes collaborative and fully autonomous approaches across planning, prediction, laboratory management, conduction, and analysis.

  • Overview: AI experiment conduction aims to automate scientific studies from hypothesis formulation through data interpretation, accelerating research and improving reproducibility.The survey notes that current AI scientists still lack validation capabilities needed for rigorous experimentation and high-quality manuscripts.
  • Experimental Design: Experimental design provides the foundation for efficient AI-assisted experiment conduction.Systematic design is described as central to automating and enhancing experimental processes.
  • Experimental Design: Semi-automatic design creates experimental plans through human-AI collaboration, including quantum protocols and polymer-sequence optimization.Reported examples combine transformer models or deep learning with optimization and, in one case, molecular-dynamics validation.
  • Experimental Design: Full-automatic design uses agent-centric methods to schedule experiments and refine protocols as new data arrive.Platforms such as The AI Scientist and Agent Laboratory support continuous protocol refinement.
  • Pre-experiment Prediction: Pre-experiment prediction separates evaluative prediction of outcomes from exploratory forecasting of compounds, pathways, and combinatorial schemes.Evaluative methods estimate quantitative effects or feasibility, while exploratory methods support discovery-oriented generation and prediction.
  • Experiment Management: Self-driving laboratories integrate machine learning and robotics for hypothesis generation, high-throughput experimentation, and iterative procedure refinement.Open-loop management retains some human-AI collaboration, whereas close-loop management is fully autonomous and can generate hypotheses, design experiments, and validate results.
  • Experiment Conduction: Experiment conduction reduces human involvement through automated ML pipelines and real-world experimental simulation or execution.Automated ML conduction spans preprocessing through hyperparameter optimization, while simulation and conduction use strategies including self-improvement.
  • Experimental Analysis: Experimental analysis tests hypotheses, evaluates models, validates theoretical assumptions, and explores datasets through metrics, consistency checks, and visualization.The surveyed subprocesses include automated evaluation metrics, theoretical consistency analysis, and exploratory analysis.

5.5. Full-Automatic Discovery

Full-automatic discovery closes the scientific loop by connecting hypothesis generation, experimental design, autonomous execution, result analysis, and iterative feedback. Multi-agent systems and rigor-oriented modules are presented as routes toward more reliable, innovative, and faster discovery.

  • Full-Automatic Discovery: Full-automatic discovery connects hypothesis generation, experimental design, autonomous execution, result analysis, and iterative feedback.It is defined as an end-to-end AI capability that closes the scientific process loop.
  • Full-Automatic Discovery: Laboratory automation and closed-loop assistants use multi-agent systems to improve the reliability, innovation, and iteration speed of automated discovery.The section identifies these developments as drivers of progress toward more capable discovery systems.
  • Rigor and Reliability: Rigor-oriented discovery systems separate intra-agent reliability, inter-agent systematic control, and experimental knowledge for interpretability.These modules address insufficient rigor and overstated claims in automated discovery.
  • Rigor and Reliability: Data-driven extensions aim to increase exploration diversity while feeding experimental results back into ideation for iterative hypothesis refinement.The described systems combine discovery, feedback, and broader exploration mechanisms.

6. AI for Academic Writing

AI for academic writing spans semi-automatic assistance and full-automatic manuscript generation. Semi-automatic systems support preparation, drafting, editing, figures, formulas, and citations with human oversight, while full-automatic systems aim to produce submission-ready papers without human intervention.

  • Taxonomy: AI for academic writing assists researchers with drafting, editing, formatting, or generating scientific manuscripts from scratch.The survey divides the field into semi-automatic and full-automatic academic writing.
  • Semi-Automatic Writing: Semi-automatic writing requires human input and oversight while improving manuscript quality and efficiency through suggestions, corrections, and formatting assistance.Its preparation phase includes title generation, structural guidance, and content-coherence support.
  • Manuscript Preparation: Preparation tools generate and rank title candidates and evaluate section structure for logical flow, completeness, cohesion, repetition, and ordering errors.These tools support authors before the manuscript is fully drafted.
  • Manuscript Writing: Supplementary writing tasks include figure and chart generation, formula transcription into editable LaTeX, and citation recommendation and integration.Figure work may use text-to-image or programmable representations, while formula tools iteratively compare drafts with source images.
  • Manuscript Completion: Post-draft tools correct grammar and revise expression and logic to improve language, cohesion, structure, clarity, and fluency.Self-guided revision systems analyze drafts and suggest sentence-level or history-based edits.
  • Full-Automatic Writing: Full-automatic academic writing generates complete scientific manuscripts without human intervention, covering drafting, formatting, and submission-ready production.Recent systems primarily use modular multi-agent designs with self-feedback for iterative refinement.

7. AI for Academic Peer Reviewing

AI for academic peer reviewing is organized into pre-review, in-review, and post-review stages, covering manuscript triage, reviewer assignment, review generation, influence analysis, and dissemination. The survey presents these stages as a pipeline for improving review efficiency, feedback, and scholarly impact.

  • Process Pipeline: Peer reviewing is structured into Pre-Review, In-Review, and Post-Review stages.Pre-Review includes desk review and reviewer matching; In-Review includes peer review and meta-review; Post-Review includes influence analysis and promotion enhancement.
  • Pre-Review: AI-assisted desk review uses keyword extraction, topic matching, and preliminary scoring to route manuscripts and shorten editorial turnaround.Examples include Elsevier, IEEE, Springer, Nature, and AnnotateGPT systems.
  • Pre-Review: Reviewer matching assigns manuscripts to experts using affinity, topic, quality, fairness, and workload considerations.Earlier approaches formulate matching as optimization, while later systems embed papers and reviewer profiles in shared topic spaces.
  • In-Review: In-review systems support numerical scoring, written feedback, unified review generation, and meta-review synthesis.Research spans score prediction, comment generation, integrated score-comment outputs, and aggregation of reviewers’ opinions.
  • In-Review: Multi-agent and iterative frameworks aim to improve review reliability, while comparisons show off-the-shelf LLMs emphasize technical validity more than novelty.Specialized agents, reinforcement-learning review rounds, and multi-objective optimization are used to improve feedback quality and focus.
  • Post-Review: Post-review AI predicts scholarly influence and generates outreach materials such as posters, lay summaries, and videos.Influence analysis commonly predicts citation trajectories from paper characteristics, while promotion enhancement broadens dissemination.

8. Application of AI for Research

AI4Research applications span natural sciences, applied science and engineering, and social sciences. The surveyed systems support simulation, discovery, diagnosis, robotics, automated experimentation, and research assistance, with reported capabilities ranging from physical-law discovery to surgeon-level procedural accuracy.

  • Application Scope: AI4Research applications are grouped into natural sciences, applied science and engineering, and social sciences.The natural-science category includes physics, biology and medicine, and chemistry and materials science; applied areas include robotics and software engineering.
  • Natural Sciences: In physics, AI supports law discovery, physical-world simulation, and neural-operator learning to improve simulation and computation.Physics-informed, Hamiltonian, and Lagrangian neural networks incorporate physical structure and conservation principles.
  • Natural Sciences: In biology and medicine, AI analyzes molecular and clinical information to support protein discovery, cell modeling, drug discovery, diagnosis, and precision medicine.These applications range from molecular structure prediction to clinical decision support and experimental workflow optimization.
  • Natural Sciences: Autonomous optical coherence tomography achieves surgeon-level accuracy in vascular anastomosis procedures.The example combines AI-guided perception and physical intervention in a delicate medical procedure.
  • Natural Sciences: Chemistry and materials systems integrate machine learning, robotics, and instrumentation into closed-loop design, synthesis, and characterization.Automatic discovery platforms combine robotic operations, online characterization, and real-time decisions to execute experiments from reagent dispensing through result analysis.
  • Applied Science and Engineering: Robotics and control research applies deep learning, reinforcement learning, and LLMs to robot perception, decision-making, and control.Autonomous design systems combine robotics, machine learning, and domain expertise to plan, execute, and optimize experiments.

9. Resources

The survey compiles resources for AI4Research across scientific comprehension, academic surveys, discovery, and writing. These resources include benchmarks, datasets, tools, and evaluation frameworks covering static, multimodal, interactive, and autonomous research tasks.

  • Resource Overview: The paper provides an expanded resource suite of tools, benchmarks, and datasets spanning all research stages.The collection is intended to support evaluation and development across AI4Research workflows.
  • Scientific Comprehension: Scientific-comprehension resources evaluate question answering, reasoning, paper understanding, chart and table comprehension, and semantic fidelity.Benchmarks include ScienceQA, SciBench, AutoPaperBench, SciCUEval, ChartQA, TableBench, and interactive frameworks such as SCITOOL-BENCH.
  • Academic Surveys: Academic-survey resources support scholarly retrieval and section-level related-work generation using paired literature sections and full texts.Examples include AcademicBrowse, Cochrane, MSLR 2022, MS2, OARelatedWork, and OAG-Bench.
  • Scientific Discovery: Scientific-discovery resources cover idea mining, novelty assessment, theory analysis, experiment design, experiment conduction, and full automatic discovery.They include structured hypothesis-generation datasets, verification benchmarks, research-agent evaluations, and autonomous-discovery suites.
  • Scientific Discovery: Experiment-conduction benchmarks assess AI agents on realistic research tasks such as optimization, tuning, replication, and scientific experimentation.Representative resources include MLAgentBench, Exp-Bench, MLE-Bench, ScienceBoard, ScienceArena, AutoReproduce, and SciReplicate-Bench.
  • Academic Writing: Academic-writing resources provide curated datasets and tools for multiple aspects of the writing process.The survey summarizes representative systems and their contributions in Table 9.

10. Frontiers & Future Direction

Future AI4Research systems should integrate knowledge across disciplines, support collaborative and real-time experimentation, and improve multimodal understanding, while addressing fairness, privacy, explainability, and reliability challenges.

  • Interdisciplinary AI Models: Foundation models should support interdisciplinary research across biology, physics, social sciences, and other domains.Heterogeneous modalities and persistent negative transfer complicate unified preprocessing, feature fusion, and reliable knowledge transfer.
  • Ethics and Safety in AI4Research: AI4Research must mitigate fairness, bias, safety, and plagiarism risks while balancing predictive performance against equitable and original scientific outputs.The survey highlights application-specific fairness tuning, anti-collusion measures, and concerns about intelligent plagiarism in LLM-generated literature.
  • AI for Collaborative Research: Collaborative AI systems can synchronize cross-domain information and support hypothesis generation, experimental planning, and preliminary analysis for human–AI research teams.Federated learning offers a privacy-preserving approach when sensitive institutional data cannot be fully shared, but interaction complexity and privacy–accessibility tensions remain.
  • Explainability and Transparency of AI4Research: Explainable AI4Research requires standardized explanation frameworks because powerful black-box models may sacrifice interpretability, complicating scientific adoption and discovery assessment.Variation in explanation techniques and metrics can produce conflicting results and undermine user confidence.
  • Dynamic Experimental Systems: Agentic real-time systems could iteratively survey literature, generate hypotheses, design experiments, and refine workflows from experimental feedback.Routine deployment still requires reliable heterogeneous-device integration, robust low-latency control, and coordination with robotic laboratory platforms.
  • Multimodal Integration: Multimodal AI4Research should ingest manuscripts, figures, tables, code, and signals with modality-specific preprocessing and expert refinement.Cross-modal annotation scarcity and heterogeneous noise remain major barriers to training, evaluation, and uncertainty quantification.

11. Related work

Prior surveys often emphasize scientific discovery and academic writing, whereas this paper broadens coverage to the full research lifecycle and organizes AI-enabled research systematically.

  • Scope of Prior Work: Existing surveys largely focus on scientific discovery and academic writing, often within AI4Science or limited research stages.They commonly overlook scientific comprehension, academic surveys, peer review, and AI applications across those stages.
  • This Survey: This paper introduces AI4Research as a broader framework and provides resources and insights covering key factors and recent developments in AI-enabled research.Its stated goal is to streamline community access to essential resources and support further innovation.

12. Conclusion

The paper responds to the lack of comprehensive AI4Research surveys with a unified framework, systematic task taxonomy, future research directions, and open-source resources.

  • Motivation: The survey addresses a missing comprehensive synthesis of AI’s growing role in scientific research.The authors connect this gap to the rapid development of systems for reasoning and experimental coding.
  • Contributions: Its contributions are a systematic AI4Research taxonomy, identified research gaps and future directions, and a compilation of open-source resources.The resources are intended to support community understanding and future advances.
Loading 2507.01903v2…