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 introduces a unified taxonomy categorizing AI tools for tasks from literature comprehension to peer review.
• It benchmarks state-of-the-art methods, highlighting advances in LLM-driven comprehension, idea mining, and autonomous discovery.
• Key implications include reduced research cycle times and a push for hybrid, human-in-the-loop approaches to overcome LLM limitations.

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 survey of the application of artificial intelligence, particularly LLMs, across the entire scientific research lifecycle. The authors introduce a unified taxonomy, analyze state-of-the-art methods, benchmark results, and discuss open challenges and future directions. This work distinguishes itself by extending beyond the narrower focus of AI4Science, encompassing the broader research workflow including comprehension, survey, discovery, writing, and peer review.

Taxonomy and Formalization

The authors propose a five-part taxonomy for AI4Research, each corresponding to a core research task:

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): Automated retrieval, synthesis, and structuring of literature to generate comprehensive surveys and related work sections.
3. AI for Scientific Discovery (AI4SD): Hypothesis generation, novelty assessment, theory analysis, experimental design, and full-automatic discovery.
4. AI for Academic Writing (AI4AW): Assistance and automation in drafting, editing, and formatting scientific 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 mapping research inputs to outputs, with explicit objectives (e.g., maximizing coherence, coverage, novelty, or review quality). The composition of these modules models the end-to-end AI4Research pipeline.

Survey of Methods and Benchmarks

Scientific Comprehension

• Textual Comprehension: Advances include human-guided, tool-augmented, and self-guided systems. Notable are retrieval-augmented generation, fact-checking, and reasoning augmentation. Fully automatic comprehension leverages summarization and self-questioning pipelines.
• Table and Chart Understanding: Instruction-tuned multimodal models (e.g., Table-LLaVA, ChartQA) and reasoning paradigms (Chain-of-Table, Tree-of-Table) have improved performance on complex scientific data.

Academic Survey

• Related Work Retrieval: Semantic, graph-based, and LLM-augmented retrieval methods are surveyed. Multi-agent and curiosity-driven retrieval strategies are highlighted for their ability to emulate human research heuristics.
• Survey Generation: Both extractive and generative approaches are discussed, with recent benchmarks (e.g., SurveyBench) enabling quantitative comparison. Iterative, agent-based, and plan-based generation pipelines are shown to approach human-level survey quality.

Scientific Discovery

• Idea Mining: LLMs demonstrate strong creativity, with methods leveraging internal knowledge, external data, and environment feedback. Multi-agent and human-AI collaborative ideation systems are shown to enhance novelty and feasibility.
• Novelty and Significance Assessment: LLM-augmented and human-in-the-loop methods are compared, with evidence that pure LLM-based assessment may overestimate creativity, necessitating hybrid approaches.
• Theory Analysis and Experiment Conduction: Automated claim formalization, evidence retrieval, and theorem proving are surveyed. Full-automatic experiment design and conduction, including self-driving laboratories and multi-agent orchestration, are rapidly advancing.
• Full-Automatic Discovery: End-to-end systems (e.g., Zochi, AI Scientist) are benchmarked on ScienceAgentBench and similar suites, demonstrating the feasibility of closed-loop, autonomous research.

Academic Writing

• Semi-Automatic Writing: AI tools assist in title generation, logical structuring, figure/chart creation, formula transcription, and citation management. Human-in-the-loop revision frameworks are shown to improve writing quality.
• Full-Automatic Writing: Multi-agent, feedback-driven systems can generate entire manuscripts, though human oversight remains necessary for citation accuracy and nuanced content.

Peer Review

• Pre-Review: AI-driven desk review and reviewer matching systems are widely adopted by publishers, improving efficiency and fairness.
• In-Review: LLMs can generate plausible review comments and scores, with multi-agent and iterative refinement frameworks enhancing alignment with human reviewers. However, LLMs tend to underemphasize novelty relative to technical validity.
• Post-Review: AI is used for citation impact prediction and the generation of promotional materials (posters, lay summaries, videos), broadening the reach of scientific work.

Numerical Results and Comparative Analyses

The paper provides extensive benchmarking across tasks:

• Survey Generation: SurveyForge (DeepSeek-v3) achieves the highest reference and content quality on SurveyBench, approaching human-written survey standards.
• Idea Mining: On Liveideabench, models like DeepSeek-R1 and Gemini-2.0-Flash-Exp lead in fluency, feasibility, and originality, but no model dominates across all metrics.
• Full-Automatic Discovery: On ScienceAgentBench, o1-preview and Claude-3.5-Sonnet achieve the highest success rates and verification scores, but cost and knowledge integration remain limiting factors.
• Peer Review: LLMs (GPT-4o, DeepSeek-R1) approach human-level focus and text similarity metrics, but still lag in nuanced aspects of review quality.

Applications and Resources

The survey catalogs applications across natural sciences (physics, biology, chemistry), applied sciences (robotics, software engineering), and social sciences (sociology, psychology). It provides curated lists of tools, datasets, and benchmarks for each research stage, facilitating practical adoption and further research.

Implications and Future Directions

Practical Implications

• Workflow Automation: AI4Research systems are increasingly capable of automating literature review, hypothesis generation, experiment design, manuscript drafting, and peer review, reducing time-to-publication and enabling higher research throughput.
• Interdisciplinary Integration: The modular taxonomy supports integration of domain-specific AI tools, enabling cross-disciplinary workflows and collaborative research.
• Resource Accessibility: The compilation of open-source tools and datasets lowers the barrier for adoption and benchmarking, accelerating community progress.

Theoretical Implications

• Unified Modeling: The formalization of research tasks as composable AI modules provides a foundation for principled system design and evaluation.
• Limits of LLMs: While LLMs excel in many tasks, their limitations in novelty assessment, domain adaptation, and explainability highlight the need for hybrid and human-in-the-loop approaches.

Open Challenges and Future Research

The authors identify several frontiers (see Figure~\ref{fig:future-work}):

• Interdisciplinary AI Models: Developing foundation and graph-based models capable of robust cross-domain reasoning.
• Ethics, Fairness, and Safety: Addressing bias, fairness, and plagiarism in AI-generated research outputs.
• Collaborative and Federated Research: Enabling privacy-preserving, distributed modeling and adaptive collaboration in heterogeneous teams.
• Explainability and Transparency: Improving interpretability of AI-driven research outputs, especially in high-stakes domains.
• Dynamic, Real-Time Experimentation: Integrating agentic AI with real-time feedback in laboratory automation.
• Multimodal and Multilingual Integration: Handling diverse data modalities and supporting low-resource languages to democratize research.
• Standardization: Establishing unified frameworks and metrics for evaluation and comparison across research tasks.

Conclusion

This survey provides a comprehensive, formal, and practical overview of AI4Research, establishing a foundation for both immediate application and future research. The modular taxonomy, benchmarking, and resource compilation will inform the design and deployment of next-generation AI-driven research systems. The identified challenges and future directions underscore the need for continued innovation in model development, system integration, and ethical governance as AI becomes increasingly central to scientific discovery and dissemination.