From Hypothesis to Publication: A Comprehensive Survey of AI -Driven Research Support Systems (2025)

Abstract: Research is a fundamental process driving the advancement of human civilization, yet it demands substantial time and effort from researchers. In recent years, the rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. To monitor relevant advancements, this paper presents a systematic review of the progress in this domain. Specifically, we organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. Hypothesis formulation involves knowledge synthesis and hypothesis generation. Hypothesis validation includes the verification of scientific claims, theorem proving, and experiment validation. Manuscript publication encompasses manuscript writing and the peer review process. Furthermore, we identify and discuss the current challenges faced in these areas, as well as potential future directions for research. Finally, we also offer a comprehensive overview of existing benchmarks and tools across various domains that support the integration of AI into the research process. We hope this paper serves as an introduction for beginners and fosters future research.

## introduction research is creative and systematic work aimed at expanding knowledge and driving civilization's development @xcite . researchers typically identify a topic, review relevant literature, synthesize existing knowledge, and formulate hypothesis, which are validated through theoretical and experimental methods. findings are then documented in manuscripts that undergo peer review before publication @xcite . however, this process is resource-intensive, requiring specialized expertise and posing entry barriers for researchers @xcite . in recent years, artificial intelligence (ai) technologies, represented by large language models (llms), have experienced rapid development @xcite @xcite @xcite . these models exhibit exceptional capabilities in text understanding, reasoning, and generation @xcite . in this context, ai is increasingly involving the entire research pipeline @xcite , sparking extensive discussion about its implications for research @xcite @xcite . moreover, following the release of chatgpt, approximately 20% of academic papers and peer-reviewed texts in certain fields have been modified by llms @xcite . a study also reveals that 81% of researchers integrate llms into their workflows @xcite . as the application of ai in research attracts increasing attention, a significant body of related studies has begun to emerge. to systematically synthesize existing research, we present comprehensive survey that emulates human researchers by using the research process as an organizing framework. specifically, as depicted in figure 1 , the research process is divided into three key stages: (1) hypothesis formulation, involving knowledge synthesis and hypothesis generation; (2) hypothesis validation, encompassing scientific claim verification, theorem proving, and experiment validation; (3) manuscript publication, which focuses on academic publications and is further divided into manuscript writing and peer review. comparing with existing surveys although @xcite reviews the application of ai in research, it predominantly focuses on llms, while neglecting the knowledge synthesis figure 1 : overview of ai for research. the framework consists of three stages: hypothesis formulation, hypothesis validation, and manuscript publication. in the hypothesis formulation stage, knowledge integration leads to the proposal of an initial hypothesis after a topic is identified. the hypothesis validation stage involves verifying the hypothesis from three perspectives to ensure its correctness and validity. finally, the manuscript publication stage focuses on drafting and publishing the validated hypothesis. contributions our contributions can be summarized as follows: (1) we align the relevant fields with the research process of human researchers, systematically integrating and extending these aspects while primarily focusing on the research process itself. (2) we introduce a meticulous taxonomy (shown in figure 2 ). (3) we provide a summary of tools that can assist in the research process. (4) we discuss new frontiers, outline their challenges, and shed light on future research. ## hypothesis formulation this stage centers on the process of hypothesis formulation. as illustrated in figure 3 , it commences with developing a comprehensive understanding of the domain, followed by identifying a specific aspect and generating pertinent hypothesis. this section is further structured into two key components: knowledge synthesis and hypothesis generation. ## hypothesis validation in scientific research, any proposed hypothesis must undergo rigorous validation to establish its validity. in some studies, this process is also referred to as 'falsification' @xcite . as illustrated in figure 4 , this section explores the application of ai in verifying scientific hypothesis through three methodological components: scientific claim verification, theorem proving, and experiment validation. ## manuscript publication upon validating a hypothesis as feasible, researchers generally progress to the publication stage. as depicted in figure 5 , this section categorizes manuscript publication into two primary components: manuscript writing and peer review. ## benchmarks given that ai for research spans multiple disciplines, the tasks addressed within each domain vary significantly. to facilitate cross-domain exploration, we provide a summary of benchmarks associated with various areas, including research paper recommendation, systematic literature review, hypothesis generation, scientific claim verification, theorem proving, experiment verification, manuscript writing, and peer review. an overview of these benchmarks is presented in ## tools to accelerate the research workflow, we have curated a collection of tools designed to support various stages of the research process, with their applicability specified for each stage. to ensure practical relevance, our selection criteria emphasize tools that are publicly accessible or demonstrate significant influence on github. a comprehensive overview of these tools is presented in ## challenges we identify several intriguing and promising avenues for future research. ## ethical considerations ai has demonstrated significant potential in enhancing productivity by mitigating human limitations, thereby motivating increased investigation into its capacity to accelerate the research process @xcite . nevertheless, its integration into scientific workflows introduces a range of ethical concerns @xcite , including algorithmic biases, data privacy issues, risks of plagiarism, and the broader implications of ai-generated content for research communities. in this work, we examine these ethical challenges across the key stages of the research lifecycle: hypothesis formulation, validation, and publication. during the hypothesis formulation stage, research paper recommendation systems and literature reviews are commonly employed; however, they often suffer from limitations that can lead to the formation of information bubbles and restrict exposure to diverse viewpoints. furthermore, these systems tend to reinforce recognition disparities between prominent and lesser-known researchers and may inadvertently contribute to the dissemination of misinformation @xcite . to address these biases, recommendation algorithms can be enhanced by emphasizing contentbased rather than author-based recommendations and by incorporating robust evaluation mechanisms to strengthen the credibility of suggested materials. in contrast, ai-driven hypothesis generation presents more pronounced ethical challenges. first, the attribution of intellectual property rights and authorship for ai-generated hypotheses remains ambiguous @xcite . additionally, the widespread generation of low-quality content poses a risk of diluting the integrity of the academic landscape @xcite , while the potential misuse of such technologies for illicit purposes cannot be overlooked @xcite . addressing these concerns necessitates the development of robust accountability frameworks, the assignment of clear responsibility for ai-generated outputs to researchers, and the establishment of appropriate legal and regulatory mechanisms. during the hypothesis validation phase, automated systems for scientific fact-checking remain underdeveloped. this limitation may be exploited by malicious actors to create advanced misinformation generators capable of circumventing existing fact-checking tools @xcite . likewise, in the context of experimental validation, there is a risk of unethical or legally questionable experiments being designed @xcite . these concerns underscore the need for continued research into model safety. during the manuscript publication stage, several challenges remain. text generated by ai models may carry a risk of plagiarism @xcite , while ai-assisted peer reviews often offer vague feedback and exhibit inherent biases @xcite @xcite . to address these issues, the development of robust detection methods is essential. however, current detection tools are still in the early stages of maturity @xcite . ## conclusion this paper provides a systematic survey of existing research on ai for research, offering a comprehensive review of the advancements in the field. within each category, we offer detailed descriptions of the associated subfields. in addition, we examine current challenges, ethical considerations, and potential directions for future research. to support researchers in exploring ai-driven research applications and enhancing workflow efficiency, we also summarize existing benchmarks and tools, accompanied by a comparative analysis of representative methods and their capabilities. furthermore, in the course of investigating various subfields within ai for research, we observed that this domain remains in its infancy. research in numerous directions remains at an experimental stage, and substantial progress is necessary before these approaches can be effectively applied in practical scenarios. we hope that this survey serves as an introduction to the field for researchers and contributes to its continued advancement.