AI4Research: A Survey of Artificial Intelligence for Scientific Research (2507.01903v1)

Abstract: Recent advancements in AI, particularly in 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.

• The paper presents a unified five-module taxonomy that formalizes AI’s role across scientific comprehension, discovery, writing, and peer review.
• It compares semi-automatic and fully-automatic systems, highlighting multi-agent and hierarchical methods to enhance research efficiency.
• Empirical benchmarks demonstrate that the AI4Research framework can accelerate discovery, improve reproducibility, and foster innovation across disciplines.

AI4Research: A Comprehensive Survey of Artificial Intelligence for Scientific Research

The paper "AI4Research: A Survey of Artificial Intelligence for Scientific Research" (2507.01903) presents a systematic and detailed taxonomy of the application of AI, particularly LLMs, across the entire scientific research lifecycle. The survey addresses a critical gap in the literature by providing a unified framework that encompasses not only scientific discovery and academic writing, but also scientific comprehension, academic survey, and peer review. This breadth distinguishes the work from prior surveys, which have typically focused on narrower aspects of AI in science.

Taxonomy and Formalization

The authors introduce a five-part taxonomy for AI4Research, formalizing each stage as a compositional function within the research process:

1. AI for Scientific Comprehension (AI4SC): Extraction, interpretation, and synthesis of information from scientific literature, including both textual and multimodal (tables, charts) content.
2. AI for Academic Survey (AI4AS): Systematic retrieval, synthesis, and structuring of literature to generate comprehensive domain overviews.
3. AI for Scientific Discovery (AI4SD): Automated hypothesis generation, novelty assessment, theoretical analysis, and experimental design/conduction.
4. AI for Academic Writing (AI4AW): Assistance and automation in drafting, editing, and formatting manuscripts.
5. AI for Academic Peer Reviewing (AI4PR): Automation and augmentation of the peer review process, including pre-review, in-review, and post-review stages.

Each module is formalized as a function $A_i$ acting on task-specific inputs, with the overall AI4Research system $\mathcal{A}$ defined as a composition of these modules. The objective is to maximize research efficiency, performance, and innovation.

Survey of Methods and Benchmarks

Scientific Comprehension

The survey distinguishes between semi-automatic and fully-automatic comprehension systems. Semi-automatic systems leverage human-AI interaction, retrieval-augmented generation, and tool integration (e.g., fact-checking, reasoning augmentation). Fully-automatic systems employ summarization, self-questioning, and self-reflection to autonomously process and understand scientific literature. The integration of multimodal comprehension—tables, charts, and figures—is highlighted as essential for holistic understanding.

Academic Survey

AI-driven academic surveys are divided into related work retrieval (semantic, graph-based, and LLM-augmented) and overview report generation (roadmap mapping, section-level, and document-level synthesis). The paper provides strong empirical results, with models like SurveyForge and AutoSurvey achieving high content and outline quality on benchmarks such as SurveyBench. The use of multi-agent and hierarchical frameworks is shown to improve coherence and coverage.

Scientific Discovery

The survey provides a granular breakdown of AI-driven scientific discovery, including idea mining (from internal knowledge, external knowledge, and environment feedback), novelty and significance assessment, theory analysis, and experiment conduction. The authors present comprehensive benchmark results (e.g., Liveideabench, ScienceAgentBench) that reveal the relative strengths of leading LLMs and agentic systems in generating feasible, original, and flexible research ideas. The integration of multi-agent collaboration and human-AI interaction is shown to enhance both the diversity and quality of generated hypotheses.

Academic Writing

AI4AW is categorized into semi-automatic and full-automatic writing. Semi-automatic systems assist with title generation, logical structuring, figure/chart creation, formula transcription, citation management, grammar correction, and logical revision. Full-automatic systems, such as AI Scientist and Zochi, employ multi-agent, modular pipelines with self-feedback for end-to-end manuscript generation. Despite progress, the survey notes that fully eliminating human intervention—especially for citation accuracy—remains an open challenge.

Peer Review

AI4PR encompasses pre-review (desk review, reviewer matching), in-review (score prediction, comment generation, unified review), and post-review (influence analysis, promotion). The survey presents detailed comparisons of LLM and expert review performance, noting that while LLMs can approach human-level review quality, they tend to underemphasize novelty and over-prioritize technical validity. Multi-agent and iterative refinement strategies are shown to improve review depth and alignment with human standards.

Multidisciplinary Applications

The survey provides an extensive mapping of AI4Research applications across natural sciences (physics, biology, chemistry), applied sciences and engineering (robotics, software engineering), and social sciences (sociology, psychology). In each domain, the authors detail the integration of AI for simulation, law discovery, experiment automation, and large-scale data analysis. The discussion includes both the technical advances and the domain-specific challenges, such as data scarcity, interpretability, and regulatory constraints.

Resources and Benchmarks

A significant contribution of the paper is the compilation of open-source tools, datasets, and benchmarks for each stage of the research lifecycle. These resources support reproducibility, systematic evaluation, and community-driven development. The survey also highlights the emergence of dynamic, interactive, and multimodal benchmarks that better reflect real-world research workflows.

Future Directions and Open Challenges

The authors identify several frontiers for AI4Research:

• Interdisciplinary AI Models: Development of foundation and graph-based models capable of integrating heterogeneous, cross-domain knowledge.
• Ethics, Fairness, and Safety: Mitigation of bias, prevention of plagiarism, and establishment of ethical frameworks for AI-generated research.
• Collaborative Research: Multi-agent and federated learning systems for distributed, privacy-preserving, and efficient collaboration.
• Explainability and Transparency: Advancement of both white-box and black-box interpretability methods, with attention to the trade-off between transparency and performance.
• Dynamic and Real-Time Experimentation: Real-time, agentic optimization of experimental protocols in self-driving laboratories.
• Multimodal and Multilingual Integration: Robust handling of diverse data modalities and support for low-resource languages to democratize research access.

Implications and Outlook

The survey's unified framework and comprehensive taxonomy provide a foundation for both theoretical and practical advances in AI-driven research. By formalizing the research lifecycle as a composition of AI modules, the paper enables modular development, systematic benchmarking, and targeted improvement of individual components. The strong empirical results across multiple benchmarks demonstrate the feasibility of automating substantial portions of the research process, though the need for human oversight—particularly in creative and evaluative tasks—remains.

Practically, the integration of AI4Research systems promises to accelerate discovery, improve reproducibility, and lower barriers to entry for new researchers. The proliferation of open-source tools and benchmarks will facilitate rapid iteration and community validation. Theoretically, the formalization of research as a compositional AI process opens avenues for meta-research, optimization, and the paper of emergent properties in collaborative, multi-agent systems.

Looking forward, the convergence of LLMs, agentic architectures, and domain-specific tools is likely to yield increasingly autonomous research workflows. However, the field must address challenges related to rigor, transparency, ethical use, and the preservation of human creativity. The survey provides a roadmap for these developments and establishes a reference point for future work in AI4Research.