Venue: NAACL 2025

# ResearchAgent: Iterative Research Idea Generation

over Scientific Literature with Large Language Models

 Jinheon Baek\({}^{1}\) Sujay Kumar Jauhar\({}^{2}\) Silviu Cucerzan\({}^{2}\) Sung Ju Hwang\({}^{1,3}\)

KAIST\({}^{1}\) Microsoft Research\({}^{2}\) DeepAuto.ai\({}^{3}\)

{jinheon.baek, sungju.hwang}@kaist.ac.kr {sjauhar, silviu}@microsoft.com

###### Abstract

The pace of scientific research, vital for improving human life, is complex, slow, and needs specialized expertise. Meanwhile, novel, impactful research often stems from both a deep understanding of prior work, and a cross-pollination of ideas across domains and fields. To enhance the productivity of researchers, we propose ResearchAgent, which leverages the encyclopedic knowledge and linguistic reasoning capabilities of Large Language Models (LLMs) to assist them in their work. This system automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them based on the feedback from collaborative LLM-powered reviewing agents. Specifically, starting with a core scientific paper, ResearchAgent is augmented not only with relevant publications by connecting information over an academic graph but also entities retrieved from a knowledge store derived from shared underlying concepts mined across numerous papers. Then, mimicking a scientific approach to improving ideas with peer discussions, we leverage multiple LLM-based ReviewingAgents that provide reviews and feedback via iterative revision processes. These reviewing agents are instantiated with human preference-aligned LLMs whose criteria for evaluation are elicited from actual human judgments via LLM prompting. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines, showing its effectiveness in generating novel, clear, and valid ideas based on both human and model-based evaluation results. Our initial foray into AI-mediated scientific research has important implications for the development of future systems aimed at supporting researchers in their ideation and operationalization of novel work1.

Footnote 1: Code: [https://github.com/JinheonBaek/ResearchAgent](https://github.com/JinheonBaek/ResearchAgent).

_Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)_, pages 6709–6738

April 29 - May 4, 2025 ©2025 Association for Computational Linguistics

## 1 Introduction

Scientific research plays a crucial role in driving innovation, advancing knowledge, solving problems, expanding our understanding of the world, and ultimately improving the lives of people in tangible ways. This process usually consists of two key components: the formulation of new research ideas and the validation of these ideas through well-crafted experiments, which are typically conducted by human researchers Hope et al. (2023); Wang et al. (2023); Huang et al. (2023). However, this is a slow, effort-intensive process, which requires reading and synthesizing overwhelming amounts of knowledge over the vast corpus of rapidly growing scientific literature to formulate research ideas, as well as design and perform experimental validations of those ideas. For example, the number of academic papers published per year is more than 7 million Fire and Guestrin (2019). Similarly, the process of testing a new pharmaceutical drug requires deep expertise, and is massively expensive and labor-intensive, often taking several years Vamathevan et al. (2019).

In the meantime, recent Large Language Models (LLMs) Touvron et al. (2023); OpenAI (2023); Anil et al. (2023) have shown impressive capabili

Figure 1: (A) The scientific knowledge used for research idea generation consists of a paper, its relationships over an academic graph, and entities within a knowledge store extracted from numerous papers. (B) Given them, the proposed research idea generation process involves problem identification, method development, and experiment design. Those are also iteratively refined by reviews and feedback from reviewing agents, aligned with criteria induced from human judgements.



ties in processing and generating text with remarkable accuracy, even outperforming human experts across diverse specialized domains including math, physics, history, law, medicine, and ethics. They are able to process and analyze large volumes of data at speeds and scales far exceeding human capabilities, have internalized large swaths of human knowledge from being trained on virtually the entire web, and can identify patterns, trends, and correlations that may not be immediately apparent to human researchers (such as the usage of quantum mechanics in medical imaging or applying psychological insights in AI). This renders them ideally poised to become foundational tools to accelerate the two phases of the scientific research process: ideation of novel research opportunities, and scientific validation of those research hypotheses.

A few recent papers in the domain of LLM-augmented scientific discovery have focused on the second phase. Specifically, they attempt Huang et al. (2023); Al4Science and Quantum (2023); Bran et al. (2023) to mainly accelerate the experimental validation process, by writing code for machine-learning models, facilitating the exploration of chemical spaces, or advancing the simulation of molecular dynamics. Thus, in this paper, we leverage LLMs in the first phase of scientific research - specifically idea generation, whose key focus is conceptualizing novel research questions, methodologies, and experiments. To the best of our knowledge, our work is the first to leverage and evaluate the capabilities of LLMs to act as mediators in scientific idea generation in an open-ended setting.

Given our goal to build an LLM-powered ResearchAgent, we draw inspiration from how human researchers position themselves to come up with novel research ideas. We draw distinctions between three key components of their workflow: a broad and deep understanding of related scientific literature, an encyclopedic view of concepts and how they relate to one another both within and across domains, and a community of colleagues on which to rely for feedback and constructive criticism.

We model each of these three aspects in our ResearchAgent. Specifically, in order to imbibe related work, the system begins with a core scientific paper and then explores a range of related papers through references and citation relationships. Further, to develop an encyclopedic view of related concepts, we build and then augment ResearchAgent with an entity-centric knowledge store derived from co-occurrences of key concepts in the scientific literature. This repository is aimed at capturing novel underlying relationships within and across domains, thereby increasing the chances of a cross-pollination of ideas Wahle et al. (2023). Finally, to simulate robust feedback mechanisms, we instantiate a number of LLM-powered ReviewingAgents that help the ResearchAgent to iterate on research idea generation with constructive critiques. Crucially, these ReviewingAgents are prompted with evaluation criteria that are induced from real researchers' judgements, thus aligning them with actual scientific preferential standards. An illustration of our system is provided in Figure 1.

We validate the effectiveness of ResearchAgent for research idea generation based on scientific literature across multiple disciplines. Then, on a battery of tests conducted with both human- and model-based evaluations, we demonstrate that ResearchAgent outperforms strong LLM-powered baselines by large margins, generating more clear, relevant, and significant ideas that are especially novel. Furthermore, analyses show the efficacy of our comprehensive approach to modeling ResearchAgent: the entity-centric knowledge store and the iterative idea refinement steps help the system generate meaningfully better ideas compared with an instantiation that is purely based on prior related work.

These findings highlight the immense potential of AI-mediated research assistants like ResearchAgent to enhance the ideation process in scientific research. In practice, it can support researchers by identifying knowledge gaps, proposing novel problem statements, and suggesting potential methodologies early in the research process. Also, it can assist in designing experiments and streamline the writing and refinement of research papers by generating drafts and offering feedback on how to effectively frame contributions and cite relevant work.

## 2 Related Work

Large Language ModelsLLMs have shown impressive performances across various tasks [9, 5], including scientific fields such as mathematics, physics, medicine, and computer science [8, 7, 6, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 222, 213, 214, 215, 216, 217, 218, 219, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 252, 259, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 282, 284, 286, 287, 288, 289, 291, 288, 289, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 323, 333, 341, 342, 343, 344, 345, 346, 347, 348, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 42, 433, 44, 45, 46, 47, 48, 49, 500, 409, 411, 432, 44, 46, 48, 49, 51, 52, 53, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 111, 109, 112, 109, 120, 121, 123, 124, 125, 126, 127, 128, 129, 131, 140, 143, 144, 145, 146, 147, 148, 149, 150, 151, 161, 172, 173, 174, 175, 176, 177, 178, 179, 180, 190, 191, 193, 194, 195, 196, 197, 198, 199, 200, 203, 204, 205, 206, 207, 208, 209, 210, 211, 223, 213, 214, 215, 216, 217, 218, 229, 230, 226, 227, 228, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 251, 253, 254, 255, 256, 257, 258, 259, 261, 259, 270, 262, 263, 264, 265, 266, 266, 267, 268, 269, 271, 288, 289, 290, 291, 2 

experimental validation of already identified research ideas, but not for identifying new problems.

Hypothesis GenerationThe principle of hypothesis generation is based on literature-based discovery (Swanson, 1986), which aims to discover relationships between concepts (Henry and McInnes, 2017). For instance, these concepts could be a specific disease and a compound not yet considered as a treatment for it. Early works on automatic hypothesis generation first build a corpus of discrete concepts, and then identify their relationships with machine learning approaches, e.g., using similarities between word (concept) vectors (Tshitoyan et al., 2019) or applying link prediction methods over a graph (where concepts are nodes) (Sybrandt et al., 2020; Nadkarni et al., 2021). Recent approaches are further powered by LLMs (Wang et al., 2023b; Qi et al., 2023; Yang et al., 2023), leveraging their prior knowledge about scientific disciplines. Yet, all these approaches perform idea generation in a localized manner and are designed to identify potential relationships between two variables or generate sentence-level connections, which may be sub-optimal to capture the complexity and multifaceted nature of real-world problems (e.g., urban planning involves numerous interacting variables). Meanwhile, we do not artificially restrict the target research idea to be a predictive single concept or simple binary link, instead allowing the model to generate ideas in a more open-ended fashion.

We note that there has been a recent surge of interest in exploring scientific idea generation: from Li et al. (2024) that focus on evaluating whether LLMs can generate research ideas that are better than human ideas, to Lu et al. (2024) that aim to automatically generate full research papers (including idea development, code writing, and experiment execution), to Li et al. (2024) that enhance the idea generation process by organizing a sequential chain of literature, all of which acknowledge and build upon insights from a prior version of our paper.

Knowledge-Augmented LLMsThe approach to augment LLMs with external knowledge makes them more accurate and relevant to target contexts. Much prior work aims at improving the factuality of LLM responses to queries by retrieving the relevant documents and injecting them into the LLM input (Lazaridou et al., 2022; Ram et al., 2023; Shi et al., 2023). In addition, given that entities or facts are atomic units for representing knowledge, recent studies augment LLMs with them (Baek et al., 2023; Wu et al., 2023). In contrast to these efforts, which use knowledge units piecemeal, we instead jointly leverage accumulated knowledge over massive troves of scientific papers. Also, Baek et al. (2024) proposes to use entities for query suggestion, which - while similar - has the entirely different objective of narrowing the focus of LLMs to entities already present in their context. Instead, our approach retrieves and integrates entities outside the given context, enabling LLMs to explore other concepts for research idea generation.

Iterative Refinements with LLMsSimilar to humans, LLMs do not always generate optimal outputs on their first attempt. To tackle this, drawing inspiration from humans who can iteratively refine their thoughts based on critiques from themselves and their peers, many recent studies have investigated the potential of LLMs to correct and refine their outputs, demonstrating that they indeed possess those capabilities (Welleck et al., 2023; Madaan et al., 2023; Shridhar et al., 2023; Ganguli et al., 2023; Wang et al., 2023b; Qi et al., 2023; Yang et al., 2023). Based on their findings, we extend this paradigm (and further test their capability) to our novel scenario of research idea generation.

## 3 Method

We present ResearchAgent, a system that automatically proposes research ideas with LLMs.

### LLM-Powered Research Idea Generation

We begin by formally introducing the new problem of research idea generation, followed by an explanation of how LLMs are utilized to tackle it.

Research Idea GenerationThe goal of the research idea generation task is to formulate new and valid research ideas, to enhance the overall efficiency of the first phase of scientific discovery. While we acknowledge that the real process by which humans conduct research is varied and complex to an extent well beyond the scope of this scientific study, we attempt to model simulacra in three systematic steps that would likely be maximally beneficial to a researcher seeking assistance from an AI system. These are namely, identifying novel research ideas, proposing methods to validate these ideas, and designing experiments to measure the success of these methods in relation to the ideas.

To accomplish the aforementioned steps, we utilize the existing literature (such as academic pub 

lications) as a primary source, which provides insights about existing knowledge along with gaps and unanswered questions2. Formally, let \({\cal L}\) be the literature, and \(o\) be the ideas that consist of the problem \(p\), method \(m\), and experiment design \(d\), as follows: \(o=[\mathbf{p},\mathbf{m},\mathbf{d}]\) where each item consists of a sequence of tokens. Then, the idea generation model \(f\) can be represented as follows: \(o=f({\cal L})\), which is further decomposed into three submodular steps: \(\mathbf{p}=f({\cal L})\) for identifying problems, \(\mathbf{m}=f(\mathbf{p},{\cal L})\) for developing methods, and \(\mathbf{d}=f(\mathbf{p},\mathbf{m},{\cal L})\) for designing experiments. We operationalize \(f\) with LLMs, leveraging their capability to understand and generate academic text.

Footnote 2: We focus on the existing literature-based idea generation by following the paradigm that a _new idea_ is more often than not just a new combination of old elements Young (2003).

Large Language ModelsBefore describing the LLM in the context of our problem setup, let us first provide its general definition, which takes an input sequence of tokens \(x\) and generates an output sequence of tokens \(y\), as follows: \(\mathbf{y}=\mathsf{LLM}_{\theta}({\cal T}(\mathbf{x}))\). Here, the model parameters \(\theta\) are typically fixed after training, due to the high costs of further fine-tuning. In addition, the prompt template \({\cal T}\) serves as a structured format that outlines the context (including the task descriptions and instructions) to direct the model in generating the desired outputs.

### Knowledge-Augmented LLMs

for Research Idea Generation

We now turn to our primary focus of automatically generating research ideas with LLMs. Recall that we aim to produce a complete idea consisting of the problem, method, and experiment design (\(o=[\mathbf{p},\mathbf{m},\mathbf{d}]\)), while using the existing literature \({\cal L}\) as a primary source of information. We operationalize this with LLMs by instantiating the aforementioned research idea generation function \(f\) with \(\mathsf{LLM}\) coupled with the task-specific template. Formally, \(\mathbf{p}=\mathsf{LLM}({\cal T}_{p}({\cal L}))\) indicates the problem identification step, followed by \(\mathbf{m}=\mathsf{LLM}({\cal T}_{m}(\mathbf{p},{\cal L}))\) for method development and \(\mathbf{d}=\mathsf{LLM}({\cal T}_{e}(\mathbf{p},\mathbf{m},{\cal L}))\) for experiment design, which constitutes the full idea: \(o=[\mathbf{p},\mathbf{m},\mathbf{d}]\).

Following this general formulation, the important question to answer is how the body of scientific literature is leveraged for actually generating research ideas with LLMs. Here, we outline three key desiderata that contribute to the success of human researchers ideating novel research ideas: a broad and deep understanding of related work, an encyclopedic perspective on the interconnectedness of concepts within and across scientific domains, and a community of peers who help iteratively improve ideas through constructive critiques. We describe our operationalization of these three desiderata using the prior literature and LLMs in what follows.

Citation Graph-based Literature SurveyDue to the constraints on their input lengths and their reasoning abilities, particularly over very long contexts Liu et al. (2023), it is not possible to incorporate all the existing publications from the literature \({\cal L}\) into the LLM input. Instead, we need to find a meaningful subset relevant to the problem at hand. To achieve this, we mirror the process followed by human researchers, who expand their knowledge of a paper by perusing other papers that either cite or are cited by it. Concretely, for the \(\mathsf{LLM}\), we initiate its literature review process by providing a core paper \(l_{0}\) from \({\cal L}\) and then selectively incorporating subsequent papers \(\left\{l_{1},...,l_{n}\right\}\) that are directly connected based on a citation graph. This procedure makes the \(\mathsf{LLM}\) input for idea generation more manageable and coherent. In addition, we operationalize the selection process of the core paper and its relevant citations with two design choices: 1) the core paper is selected based on its citation count (e.g., exceeding 100 over 3 months) typically indicating high impact; 2) its relevant papers (which may be potentially numerous) are further narrow-downed based on their similarities of abstracts with the core paper, ensuring a more focused and relevant set of related work.

However, despite the simplicity and intuitiveness of this idea generation approach, there exists one major limitation. This approach relies exclusively on a set of given papers (the core paper and its citations); however, since scientific knowledge is not confined to specific studies but rather accumulates across a wide range of publications (across various fields), we should ideally harness this extensive, interconnected, and relevant scientific knowledge in our method for research idea generation.

Entity-Centric Knowledge AugmentationIn order to model an encyclopedic view of interconnected concepts, we must effectively design a framework to extract, store and effectively leverage the vast amount of knowledge in scientific literature \({\cal L}\). In this work, we view entities as the atomic units of knowledge, which allows for ease of representation and accumulation over papers in a unified 

manner across different disciplines. For example, we can easily extract the term "database" whenever it appears in any paper, using existing off-the-shelf entity linking methods and then aggregate their linked occurrences into a knowledge store. Then, if the term "database" is prevalent within the realm of medical science but less so in hematology (which is a subdomain of medical science), the constructed knowledge store can capture the affinity between those two domains based on overlapping entities. This representational paradigm can then be used to suggest the term "database" when formulating the ideas about hematology. In other words, this approach enables providing novel and interdisciplinary insights by leveraging the interconnectedness of entities across various fields.

Formally, we design the knowledge store as a two-dimensional matrix \({\cal K}\in{\cal R}^{m\times m}\) where \(m\) is the total number of unique entities identified and \({\cal K}\) is implemented in a sparse format. This knowledge store is constructed by extracting entities over all the available scientific articles in literature \({\cal L}\)3, which not only counts the co-occurrences between entity pairs within individual papers but also quantifies the count for each entity. Our approach is versatile, thus, we can use any entity linker; in this paper we use one developed by Wu et al. (2020). This off-the-shelf system proves capable of extracting key scientific entities (which is demonstrated in Table 16) despite its lack of customized training for the scientific domain. Specifically, this linker tags and canonicalizes entities in a paper \(l\) from \({\cal L}\), formalized as follows: \({\cal E}_{l}=\mathtt{EL}(l)\) where \({\cal E}_{l}\) denotes a multiset of entities (allowing for repetitions) appearing in \(l\)4. Upon extracting entities \({\cal E}\), to store them into the knowledge store \({\cal K}\), we consider all possible pairs of \({\cal E}\) represented as follows: \(\{e_{i},e_{j}\}_{(i,j)\in{\cal C}(|{\cal E}|,2)}\) where \(e\in{\cal E}\).

Footnote 3: As extracting entities on all articles is computationally infeasible, we target papers appearing after May 01, 2023.

Given this knowledge store \({\cal K}\), our next goal is to enhance the previous vanilla research idea generation process implemented based on a group of interconnected papers, denoted as follows: \(\boldsymbol{o}=\mathtt{LLM}({\cal T}(\{l_{0},l_{1},...,l_{n}\}))\). We do this by augmenting the LLM with the relevant entities from \({\cal K}\), which expand the context that LLMs consume with additional knowledge. Formally, let us define entities extracted from the group of interconnected papers, as follows: \({\cal E}_{\{l_{0},...,l_{n}\}}=\bigcup_{i=0}^{n}\mathtt{EL}(l_{i})\). Then, the probabilistic form of retrieving the top-\(k\) relevant external entities can be represented as follows:

\[\small\texttt{Ret}(\{l_{0},...,l_{n}\};{\cal K})=\operatorname*{arg\,max}_{I \subset[m],|I|=k}\prod P(e_{i}|{\cal E}_{\{l_{0},...,l_{n}\}}), \tag{1}\]

where \([m]=\{1,...,m\}\) and \(e_{i}\notin{\cal E}_{\{l_{0},..,l_{n}\}}\). Also, for simplicity, by applying Bayes' rule and assuming that entities are independent, the retrieval operation (Equation 1) can be approximated as follows:

\[\operatorname*{arg\,max}_{I\subset[m],|I|=k}\prod(\prod_{e_{j}\in{\cal E}_{\{ l_{0},...,l_{n}\}}}P(e_{j}|e_{i}))\times P(e_{i}), \tag{2}\]

where \(P(e_{j}|e_{i})\) and \(P(e_{i})\) can be derived from values in the two-dimensional matrix \({\cal K}\), suitably normalized. We note that the formulation in Equation 2 is only one instance of operationalizing retrieval; this could be replaced with other retrieval strategies - for example, embedding-based retrieval (discussions and results are provided in Appendix B.3). Hereafter, the instantiation of research proposal generation augmented with relevant entitypcentric knowledge is formalized as follows: \(\boldsymbol{o}=\mathtt{LLM}({\cal T}(\{l_{0},...,l_{n}\},\mathtt{Ret}(\{l_{0} ,...,l_{n}\};{\cal K})))\)5. We call this knowledge-augmented LLM-powered idea generation approach _ResearchAgent_, and provide the templates to instantiate it in Tables 6, 7, and 8.

Footnote 5: There may be additional knowledge sources (beyond the existing literature and entities) for research idea generation, and we leave exploring them as future work.

Iterative Research Idea RefinementsWe note that attempting to write a full research idea in one go may not be an effective strategy. Humans write drafts that are continually improved based on multiple rounds of reviews and feedback. Therefore, we lastly model a community of peers for iterative idea improvement by introducing a set of LLM-powered reviewing agents (called _ReviewingAgents_), which provide the ResearchAgent with reviews and feedback according to various criteria for improvement.

Specifically, similar to our approach to instantiate ResearchAgent with an LLM (LLM) and template (\({\cal T}\)), ReviewingAgents are instantiated similarly but with different templates (See Tables 9, 10, and 11). Then, with ReviewingAgents, each of the generated research ideas (problem, method, and experiment design) is separately evaluated according to its own specific five criteria6, which are provided in labels of Figure 2 and detailed in Table 12. Based on the reviews and feedback from ReviewingAgents, the ResearchAgent iteratively updates and refines its generation of research ideas.

 

Despite the proficiency of LLMs in the evaluation of machine-generated texts (Zheng et al., 2023; Fu et al., 2023), their judgments on research ideas may not be aligned with the judgments of real human researchers. However, there are no ground truth reference judgments available, and collecting them to align LLMs is expensive and often infeasible. Ideally, the judgments made by LLMs should be similar to the ones made by humans, and we aim to ensure this by automatically generating human preference-aligned evaluation criteria (used for automatic evaluations) with a few human annotations. Specifically, to obtain these humanized evaluation criteria, we first collect 10 pairs of research ideas and their associated scores for every evaluation criterion on a 5-point Likert scale, annotated by human researchers having at least 3 papers. After that, we prompt the LLM with these human-annotated pairs to induce detailed descriptions for evaluation criteria (Lin et al., 2024) (See Tables 13, 14, and 15). These criteria reflect the underlying human preferences7 and are used as evaluation criteria by the ReviewingAgents.

Footnote 7: We additionally ask five human annotators, who evaluate research ideas, to judge the quality of the induced criteria; two of them agree strongly, while the other three agree moderately.

## 4 Experimental Setup

### Data

The main source to generate research ideas is the scientific literature \(\mathcal{L}\), which we obtain from the Semantic Scholar Academic Graph API8. From this, we select papers appearing after May 01, 2023, because LLMs that we use in our experiments are trained on data from the open web available before this point. This follows the procedure of existing literature-based hypothesis generation work (Qi et al., 2023). Then, we select high-impact papers (that have more than 20 citations) as core papers, mirroring human researchers' tendency to leverage influential work, to ensure the high quality of generated ideas. The resulting data is still very large; thus, we further sample a subset of 300 papers as core papers to obtain a reasonably sized benchmark dataset. The average number of reference papers for each core paper is 87; the abstract of each paper has 2.17 entities on average. The distribution of disciplines for all papers is provided in Figure 7.

Footnote 8: [https://www.semanticscholar.org/product/api](https://www.semanticscholar.org/product/api)

### Baselines and Our Model

As we target the novel task of research idea generation involving the generation of problems, methods, and experimental designs, there are no baselines for direct comparison. Thus, we mainly compare our full ResearchAgent model against its ablated variants, outlined as follows:

1. **Naive ResearchAgent** - which uses only a core paper to generate research ideas.
2. **ResearchAgent w/o Entity Retrieval** - which uses the core paper and its relevant references without considering entities.
3. **ResearchAgent** - which is our full model that uses the relevant references and entities along with the core paper, to augment LLMs.

In addition to this set of core baselines, we also compare our approach against existing hypothesis generation work from prior literature in Table 3.

### Evaluation Setup

Given our formulation of idea generation (Sec 3.1), there are no ground-truth answers to measure the quality of the generated ideas. Yet, exhaustively listing pairs of core papers and reference research ideas is suboptimal, since there may exist a large number of valid research ideas for each core paper, and this process requires much time, effort and expertise on the part of human researchers. Thus, we use a combination of model-based automatic evaluation and manual human evaluation to validate different models on our experimental benchmark.

Model-based EvaluationFollowing the recent trends in using LLMs to judge the quality of output texts (especially in the setting of reference-free evaluations) (Zheng et al., 2023; Fu et al., 2023; Liu et al., 2023b), we use GPT-4 to judge the quality of research ideas. Note that each of the problem, method, and experiment design is evaluated with five different criteria (See labels of Figure 2 for criteria and see Table 12 for their detailed descriptions). We ask the LLM-based evaluation model to either rate the generated idea on a 5-point Likert scale for each criterion or perform pairwise comparisons between two ideas from different models. We provide the prompts for evaluations in Appendix A.

Human EvaluationSimilar to model-based evaluations, we perform human evaluations that involve assigning a score for each criterion and conducting pairwise comparisons between two ideas. As the 

generated ideas are knowledge-intensive, we carefully select annotators who are well-versed in the field and provide them with ideas that are highly relevant to their field of expertise9. Specifically, we choose ten expert researchers who have authored at least three papers and ask them to judge only the ideas that are generated based on their own papers.

Footnote 9: We also experiment with human evaluation using non-domain-experts, but this proves to be suboptimal; therefore, we focus on experts for reliable judgments of generated ideas.

### Implementation Details

We mainly use the GPT-4 [10] release from Nov 06, 2023, as the basis for all models, which is, notably, reported to be trained with data up to Apr 2023 (meanwhile, the papers used for idea generation appear after May 2023). To extract entities and build the entity-centric knowledge store, we use the off-the-shelf BLINK entity linker [57], with papers from May 01, 2023, to Dec 31, 2023 (available from Semantic Scholar API) along with their references, which number 50,091 in total. We provide detailed prompts used to elicit responses for research idea generation in Appendix A.3.

## 5 Experimental Results and Analyses

Main ResultsOur main results on scoring with human and model-based evaluations are provided in Figure 2. These demonstrate that our full ResearchAgent outperforms all baselines by large margins on every metric across problems, methods, and experiment designs (constituting the complete research ideas). Particularly, the full ResearchAgent augmented with relevant entities exhibits strong gains on metrics related to creativity (such as Originality for problems and Innovativeness for methods) since entities may offer novel concepts and views that may not be observable in the group of citation-based papers alone. In addition, the results of pairwise comparisons between models with both human and model-based evaluations - shown in Figure 3 - demonstrate that the full ResearchAgent shows the highest win ratio over its baselines.

\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Categories** & **Metrics** & **Problem** & **Method** & **Experiment** \\ \hline \multirow{2}{*}{**Human and Human**} & Scoring & 0.83 & 0.76 & 0.67 \\  & Pairwise & 0.62 & 0.62 & 0.41 \\ \hline \multirow{2}{*}{**Human and Model**} & Scoring & 0.64 & 0.58 & 0.49 \\  & Pairwise & 0.71 & 0.62 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of agreements between two human annotation results and between human and model evaluation results.

Figure 3: Results of pairwise comparisons between ideas from two of any different approaches, where we report the win ratio.

Figure 2: Main results on our research idea generation task with human- (top) and model-based (bottom) evaluations, where we report the score of each idea (problem, method, or experiment design) based on its own five criteria and their average score.

 

Analysis on Inter-Annotator AgreementsTo validate the quality and reliability of human annotations, we measure the inter-annotator agreements, where 20% of the generated ideas are evaluated by two human judges, and report results in Table 1. Specifically, for the scoring, we first rank scores from each annotator and measure Spearman's correlation coefficient Pirie (2006) between the ranked scores of two annotators. For the pairwise comparison between two judges, we measure Cohen's kappa coefficient Cohen (1960). Table 1 shows that the inter-annotator agreement is high, confirming the reliability of our assessments about the quality of generated research ideas. Also, while agreement scores for experimental designs are slightly lower than other aspects, this does not necessarily indicate a shortcoming in the quality of experimental designs produced by ResearchAgent, as demonstrated in Figures 2 and 3. Instead, we view this as the inherent subjectivity and variability in how such designs are perceived and evaluated by different annotators (i.e., the nature of the variability itself makes achieving high agreement challenging).

Analysis on Human-Model AgreementsSimilar to what we did for the aforementioned intern-annotator agreements, we measure agreements between human-based and model-based evaluations, to ensure the reliability of model-based evaluations. As shown in Table 1, we further confirm that agreements between humans and models are high, indicating that model-based evaluations are a reasonable proxy to judge research idea generation.

Analysis of Refinement StepsTo see the effectiveness of iterative refinements of research ideas with ReviewingAgents, in Figure 4, we report the averaged scores on the generated ideas as a function of refinement steps. We first observe initial improvements in the quality of generated ideas with increased refinement steps. Yet, the performance becomes saturated after three iterations, which may indicate diminishing returns for subsequent iteration steps, which aligns with the pattern observed in agent-based refinement work Du et al. (2023).

Ablation on Knowledge SourcesRecall that the full ResearchAgent is augmented with two different knowledge sources, namely relevant references and entities. To see their individual contribution, we perform an ablation study by either excluding one of the knowledge sources or replacing it with random elements. As shown in Table 2, each knowledge source contributes to performance improvement, and the relevant references are especially helpful. We also note that providing random elements is more helpful than providing no elements at all; we hypothesize that this may be due to the LLM's capability to filter out noise while still gaining incidental value from random inputs.

Analysis on Human Alignment for EvaluationRecall that to align judgments from model-based evaluations with actual human preferences, we generated the evaluation criteria based on human evaluation results and used them as the criteria for model-based evaluations. Figure 5 demonstrates the efficacy of this strategy, presenting the score distribution of human evaluation compared with the distributions of model-based evaluations with and without human alignment. We find that the score distribution of model-based evaluations without alignment is skewed and different from the score distribution of human judgments. Meanwhile, after aligning the model-based evaluations with human-induced score criteria, the calibrated distribution more closely resembles th

\begin{table}
\begin{tabular}{l c c c} \hline \multicolumn{1}{c}{**Methods**} & \multicolumn{1}{c}{**Problem**} & \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Experiment**} \\ \hline \hline ResarchAgent & **4.52** & **4.28** & **4.18** \\ - w/o Entities & 4.35 & 4.13 & 4.02 \\ - w/ Random Entities & 4.41 & 4.19 & 4.13 \\ - w/o References & 4.26 & 4.08 & 3.97 \\ - w/ Random References & 4.35 & 4.16 & 4.02 \\ - w/o Entieties \& References & 4.20 & 4.03 & 3.92 \\ \end{tabular}
\end{table}
Table 2: Results of ablation study on references and entities.

Figure 4: Results with varying the number of refinement steps.

Figure 5: Distributions of model-based evaluation results with and without the human-induced score criteria alignment (middle and right), as well as human evaluation results (left).

Figure 6: Results with bucketing papers based on citations.

 

Correlation on Citation CountsWe further investigate whether a high-impact paper (when used as a core paper) leads to high-quality research ideas. To measure this, we categorize papers by their citation count (as a proxy for impact), and visualize the average score of each bucket (with model-based evaluations) in Figure 6. We find that ideas from high-impact papers tend to be of higher quality, likely due to their ability to identify research gaps, propose feasible methods, and connect with other works. Additionally, based on the paper distribution (See Figure 7) and for the ease of manual quality check, evaluation criteria for model-based evaluations are induced mainly with computer science papers. To see whether those criteria are applicable to diverse fields, we also compare a correlation between scores of computer science papers and all papers in Figure 6. From this, we observe that the scores increase when the citation increases for both domains, which may support the generalizability of human-preference-induced evaluation criteria.

Comparisons to Hypothesis GenerationRecall that existing methods for hypothesis generation focus on predicting links between variables or generating hypotheses based on these links, which differs from our experimental setup of generating open-ended research ideas (problems, methods, and experiments). Nevertheless, to understand how the quality of the generated research ideas from prior works Wang et al. (2023b); Yang et al. (2023) differs from our ResearchAgent, we perform comparisons. As shown in Table 3, we observe that ResearchAgent is capable of generating superior research hypotheses, due to the utilization of broad and deep knowledge across domains as well as the iterative review and refinement procedures.

Analysis using Different LLMsTo assess how ResearchAgent's performance changes with different LLMs, we conduct an auxiliary analysis with Llama3, Mixtral, Qwen1.5, and GPT-3.5 Bai et al. (2023); Jiang et al. (2024), as shown in Table 4. These results show a significant performance drop with less capable models. Moreover, the performance differences between the Naive ResearchAgent without knowledge augmentation and the full ResearchAgent become marginal (for Mixtral and GPT-3.5), which indicates that they might struggle with capturing complex concepts between scientific papers. This can likely be attributed to the emergent abilities of LLMs for complex reasoning (but not in smaller LMs) Wei et al. (2022), although other subtle issues may also be contributing factors.

Qualitative AnalysisWe provide qualitative results on generated research ideas in Table 16. One representative example in the last row highlights the advantage of entity-centric knowledge augmentation, where two entities (such as Drosophila Genetic Reference Panel and CRISPR retrieved from the entity-centric knowledge store enable the generation of a novel research idea: bridging genetic variability and CRISPR applications. This exemplifies how external entity-based knowledge uncovers non-trivial relations between scientific concepts.

## 6 Conclusion

In this work, we introduced ResearchAgent, a system designed to assist researchers by generating research ideas, which encompass problem identification, method development, and experiment design. Inspired by the human process of ideation, our approach conducts broad and deep literature reviews, integrates knowledge across domains to foster idea cross-pollination, and employs a community of reviewing agents to iteratively refine the generated ideas. Our evaluations, both human and model-based, demonstrated that ResearchAgent produces ideas that are more creative, valid, and clear compared to baselines. While this initial foray shows promising results, multiple challenges remain to operationalize ResearchAgent in real-world research settings. Practical considerations include scaling the knowledge store to encompass diverse research domains, and keeping it current with the latest publications, through which the system can become adaptable even to emerging fields.

\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**LLMs** & **Models** & **Problem** & **Method** & **Experiment** \\ \hline \multirow{2}{*}{**GPT-4.0**} & Naive ResearchAgent & 4.20 & 4.03 & 3.92 \\  & ResearchAgent (Ours) & 4.52 & 4.28 & 4.18 \\ \hline \multirow{2}{*}{**GPP-3.5**} & Naive ResearchAgent & 3.56 & 3.36 & 3.63 \\  & ResearchAgent (Ours) & 3.58 & 3.58 & 3.60 \\ \hline \multirow{2}{*}{**Llamas (SB)**} & Naive ResearchAgent & 3.76 & 3.69 & 3.54 \\  & ResearchAgent (Ours) & 4.18 & 4.03 & 3.95 \\ \hline \multirow{2}{*}{**Mixtral (RQ/R)**} & Naive ResearchAgent & 3.31 & 3.27 & 3.20 \\  & ResearchAgent (Ours) & 3.28 & 3.35 & 3.31 \\ \hline \multirow{2}{*}{**Qwen1.5 (32B)**} & Naive ResearchAgent & 3.64 & 3.74 & 3.66 \\  & ResearchAgent (Ours) & 4.02 & 3.97 & 3.94 \\ \hline \end{tabular}
\end{table}
Table 4: Results with different, open and proprietary LLMs.

\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Methods** & **Clarity** & **Relevance** & **Origality** & **Feashingly Significance** \\ \hline SciGM & 4.01 & 4.37 & 4.56 & 3.98 & 4.15 \\ Hyprehensive Producer & 3.97 & 4.14 & 4.07 & 4.01 & 4.11 \\ ResearchAgent & **4.11** & **4.88** & **4.77** & **4.05** & **4.81** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of ResearchAgent with hypothesis generation methods Wang et al. (2023b); Yang et al. (2023) 



### Limitations

ResearchAgent has some limitations that we hope to address in future work.

First, recall that we built the entity-centric knowledge store to propose beneficial entities during idea generation; however this store is constructed by extracting entities from the titles and abstracts of a limited number of publications (due to the costs of processing them) thereby precluding a large number of other entities and their interconnectedness.

In addition, the number of entities that we obtain from the BLINK entity linker Wu et al. (2020) amounts to 3 per paper on average, indicating limited coverage (it is an open-domain linker after all), although it does exhibit generally strong understanding of scientific contexts, as demonstrated by the improvement achieved by the inclusion of its predictions (See Figures 2 and 3, and Table 16).

Furthermore, since our ResearchAgent is powered by LLMs, similar to any other approaches based on LLMs, it may hallucinate the generated research ideas. While our proposed ResearchAgent can partially mitigate this problem by augmenting LLMs with additional elements, such as references to the target paper and greater entity-centric knowledge, which help ground the generation process in more accurate and relevant information, validating these generated research ideas with experiments is essential to truly accelerate scientific research.

Moreover, while our iterative refinement process with ReviewingAgents demonstrates promising results, it has inherent limitations in scope. Although we employed diverse perspectives by utilizing 15 ReviewingAgents to evaluate three different ideas (problem, method, and experimental design) with five specific criteria for each, this approach may not fully capture the broad range of potential perspectives and criteria necessary for comprehensive evaluation across all different research domains. As acknowledged in the paper, our selection of criteria was informed by their presumed importance, but conducting an exhaustive exploration of all possible criteria over diverse domains is beyond the scope of this work (given the complexity of categorizing and balancing all relevant factors and perspectives). However, we believe the potential of our modular approach allows for customizing and aligning updated or even new criteria to any specific target domain with novel applications, and we leave further expanding them as future work.

Lastly, our ResearchAgent may be less suited for generating ideas in certain domains, such as theoretical sciences, where mathematical reasoning and proof generation play a central role. However, its flexibility allows for customization through specific instructions, enabling the integration of reasoning-based models and techniques to cater to the needs of theoretical research. For instance, in theoretical mathematics, we can instruct (reasoning-based) LLMs to focus on generating proofs or methods and omit experimental design steps that are less relevant. This as an exciting area for future work, where specialized techniques tailored to each domain could be included to broaden its applicability.

## Ethics Statement

We are aware that the ResearchAgent may have the potential to be misused for nefarious purposes, such as generating research ideas about new explosives, malicious software, and invasive surveillance tools. Notably, this vulnerability is not unique to our approach but a common challenge faced by existing LLMs that possess significant creative and reasoning capabilities, occasionally generating content that may be deemed undesirable. Consequently, it underscores the necessity to enhance the robustness and safety of LLMs more broadly.

Also, we recognize the risks of unintentional plagiarism associated with using ResearchAgent, where the system might generate ideas that closely mirror existing research due to the regurgitation of training data. While mitigation strategies, such as integrating access to a comprehensive knowledge base to inform users of prior work, can be employed, we understand that building and maintaining such a resource is inherently complex and may not fully eliminate the risk. To further reduce the possibility of plagiarism, recording and tracking all generated ideas could help identify similarities and guide the model to avoid repetition, though this approach would necessitate explicit user consent.

## Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00256259), the grant of the Korea Machine Learning Ledger Orchestration for Drug Discovery Project (K-MELLODDY) funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (grant number: RS-2024-12345678), the Artificial intelligence industrial convergence

cluster development project funded by the Ministry of Science and ICT (MSIT, Korea) & Gwangju Metropolitan City, and the Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (RS-2019-III190075, Artificial Intelligence Graduate School Program (KAIST)).

## References

* M. A. Al4Science and M. Azure Quantum (2023)The impact of large language models on scientific discovery: a preliminary study using gpt-4.. arXiv preprint arXiv:2311.07361. Cited by: SS1.
* R. Anil, S. Borgeaud, Y. Wu, J. Alalyrac, J. Yu, R. Soircut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Milican, D. Silver, S. Petrov, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. P. Lillicrap, A. Lazaridou, O. Firat, J. Molloy, M. Isard, P. Ronald Barham, T. Hennigan, B. Lee, F. Viola, M. Reynolds, Y. Xu, R. Doherty, E. Collins, C. Meyer, E. Rutherford, E. Moreira, K. Ayoub, M. Goel, G. Tucker, E. Piqueras, M. Krikun, I. Barr, N. Savinov, I. Danihelka, B. Roelofs, A. White, A. Andreassen, T. von Glehn, L. Yagati, M. Kazemi, L. Gonzalez, M. Khalman, J. Sygnowski, and et al. (2023)Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805. Cited by: SS1.
* J. Baek, A. F. Ajji, and A. Saffari (2023)Knowledge-augmented language model prompting for zero-shot knowledge graph question answering. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), Toronto, Canada, pp. 78-106. External Links: Link Cited by: SS1.
* J. Baiek, X. Chandrasekaran, S. Cuccerzan, A. Herring, and S. Kumar Jauhar (2024)Knowledge-augmented large language models for personalized contextual query suggestion. WWW. Cited by: SS1.
* J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang, B. Hui, L. Ji, M. Li, J. Lin, R. Lin, D. Liu, Q. Liu, C. Lu, K. Lu, J. Ma, R. Men, X. Ren, X. Ren, C. Tan, S. Tan, J. Tu, P. Wang, S. Wang, W. Wang, S. Wu, B. Wu, B. Xu, J. Xu, A. Yang, H. Yang, J. Yang, J. Yang, S. Yang, Y. Yao, W. Yu, Y. Bowen, H. Yuan, Z. Yuan, J. Zhang, X. Zhou, J. Zhou, X. Zhou, and T. Zhu (2023)Qwen technical report. arXiv preprint arXiv:2309.16609. Cited by: SS1.
* A. M. Bran, S. Cox, O. Schluter, C. Baldasarai, A. D. White, and P. Schwaller (2023)Chemcrow: augmenting large-language models with chemistry tools. Cited by: SS1.
* 46. Cited by: SS1.
* Y. Du, S. Li, A. Torralba, J. B. Tenenbaum, and I. Mordatch (2023)Improving factuality and reasoning in language models through multiagent debate. arXiv preprint arXiv:2305.14325. Cited by: SS1.
* M. Fire and C. Guestrin (2019)Over-optimization of academic publishing metrics: observing goodhart's law in action. GigaScience8. Cited by: SS1.
* J. Fu, See-Kiong Ng, Zhengbao Jiang, and Pengfei Liu (2023)GPTScore: evaluate as you desire. arXiv preprint arXiv:2302.04166. Cited by: SS1.
* Deep Ganguli, A. Askell, N. Schaiefer, T. I. Liao, K. Lukositute, A. Chen, A. Goldie, A. Mirhoseini, C. Olsson, D. Hernandez, D. Drein, D. Li, E. TranJohnson, E. Perez, J. Kerri, J. Mueller, J. Landau, K. Ndousse, K. Nguyen, L. Lovtti, M. Selifito, N. Elhage, N. Mercado, N. DasSarma, O. Rausch, R. Lasenby, R. Larson, S. Ringer, S. Kundu, S. Kandavath, S. Johnston, S. Kravec, S. El Showk, T. Lanham, T. Telleen-Lawton, T. Henighan, T. Hume, V. Bai, Z. Hatfield-Dodds, B. Mann, D. Amodei, N. Joseph, S. McCandlish, T. Brown, C. Olah, J. Clark, S. R. Bowman, and J. Kaplan (2023)The capacity for moral self-correction in large language models. arXiv preprint arXiv:2302.07459. Cited by: SS1.
* S. Henry and B. T. McInnes (2017)Literature based discovery: models, methods, and trends. Journal of biomedical informatics74, pp. 20-32. Cited by: SS1.
* T. Hope, D. Downey, D. S. Weld, O. Etzioni, and E. Horvitz (2023)A computational inflection for scientific discovery. Commun. ACM66 (8), pp. 62-73. Cited by: SS1.
* Qian Huang, Jian Vora, Percy Liang, and J. Leskovec (2023)Benchmarking large language models as AI research agents. arXiv preprint arXiv:2310.03302. Cited by: SS1.
* A. Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, L. Lencaud, L. Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Theophile Gervet, Thibaut Lavril, Thomas Wang, Timothee Lacroix, and William El Sayed (2024)Mickraft of experts. arXiv preprint arXiv:2401.04088.

 

* Lazaridou et al. (2022) Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, and Nikolai Grigorev. 2022. Internet-augmented language models through few-shot prompting for open-domain question answering. _arXiv preprint arXiv:2203.05115_.
* Li et al. (2024) Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Deil Zhao, Yu Rong, Tian Feng, and Lidong Bing. 2024. Chain of ideas: Revolutionizing research via novel idea development with llvm agents.
* Lin et al. (2024) Ying-Chun Lin, Jennifer Neville, Jack W Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, and Jaime Tevean. 2024. Interpretable user satisfaction estimation for conversational systems with large language models. _arXiv preprint arXiv:2403.12388_.
* Liu et al. (2023a) Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paramjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023a. Lost in the middle: How language models use long contexts. _Transactions of the Association for Computational Linguistics_, 12:157-173.
* Liu et al. (2023b) Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023b. G-eval: NLG evaluation using gpt-4 with better human alignment. In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023_, pages 2511-2522. Association for Computational Linguistics.
* Liu et al. (2024) Yiren Liu, Si Chen, Haocong Cheng, Mengxia Yu, Xiao Ran, Andrew Mo, Yiliu Tang, and Yun Huang. 2024. How AI processing delays foster creativity: Exploring research question co-creation with an llm-based agent. In _Proceedings of the CHI Conference on Human Factors in Computing Systems, CHI 2024, Honolulu, HI, USA, May 11-16, 2024_, pages 17:1-17:25. ACM.
* Lu et al. (2024) Chris Lu, Cong Lu, Robert Tyarko Lange, Jakob Foerster, Jeff Clune, and David Ha. 2024. The ai scientist: Towards fully automated open-ended scientific discovery.
* 16, 2023_.
* Nadkarni et al. (2021) R.K. Nadkarni, David Wadden, Iz Beltagy, Noah A. Smith, Hannaneh Hajishirzi, and Tom Hope. 2021. Scientific language models for biomedical knowledge base completion: An empirical study. _ArXiv_, abs/2106.09700.
* OpenAI (2023) OpenAI. 2023. GPT-4 technical report. _arXiv preprint arXiv:2303.08774_.
* Pirie (2006) W. Pirie. 2006. _Spearman Rank Correlation Coefficient_, volume 8.
* Portenoy et al. (2021) Jason Portenoy, Marissa Radensky, Jevin West, Eric Horvitz, Daniel S. Weld, and Tom Hope. 2021. Bridger: Toward bursting scientific filter bubbles and boosting innovation via novel author discovery. _arXiv preprint arXiv:2108.05669_.
* Qi et al. (2023) Biqing Qi, Kaiyan Zhang, Haoxiang Li, Kai Tian, Sihang Zeng, Zhang-Ren Chen, and Bowen Zhou. 2023. Large language models are zero shot hypothesis proposers. _arXiv preprint arXiv:2311.05965_.
* Ram et al. (2023) Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023. In-context retrieval-augmented language models. _Transactions of the Association for Computational Linguistics_, 11:1316-1331.

* Shi et al. (2023) Weijia Shi, Sewon Min, Michihiro Yasunaga, Mingoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023. Replug: Retrieval-augmented black-box language models. _arXiv preprint arXiv:2301.12652_.
* Shridhar et al. (2023) Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ram Pasunuru, Mrinmaya Sachan, Jason Weston, and Asli Celikyilmaz. 2023. The ART of LLM refinement: Ask, refine, and trust. _arXiv preprint arXiv:2311.07961_.
* Swanson (1986) Don R. Swanson. 1986. Undiscovered public knowledge. _The Library Quarterly_, 56:103-118.
* Sybrandt et al. (2020) Justin Sybrandt, Ilya Tyagin, M. Shuttman, and Ilya Safro. 2020. Agatha: Automatic graph mining and transformer based hypothesis generation approach. _Proceedings of the 29th ACM International Conference on Information & Knowledge Management_.
* Touvron et al. (2020) Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahati, Yasmine Babaei, Nikolay Bashlykov, Soupya Batra, Prajjwal Bhargava, Shruit Bhosalie, Dan Bikeel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esibbu, Jude Fernandes, Jeremy Fu, Wenynju Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan 

Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Klounann, Artem Korenecy, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molbyog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Pavin Xu, Zheng Yan, Hiyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023. LLama 2: Open foundation and fine-tuned chat models. _arXiv preprint arXiv:2307.09288_.

* Vamathevan et al. (2019) Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Paratu Shah, Michaelia Spitzer, and Shanrong Zhao. 2019. Applications of machine learning in drug discovery and development. _Nature reviews. Drug discovery_, 18(6):463--477.
* Wahle et al. (2023) Jan Philip Wahle, Terry Ruas, Mohamed Abdalla, Bela Gipp, and Saif M. Mohammad. 2023. We are who we cite: Bridges of influence between natural language processing and other academic fields. In _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023, Singapore, December 6-10, 2023_, pages 12896-12913. Association for Computational Linguistics.
* Wang et al. (2023a) Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora S. Marks, Bharath Ramsumdar, Le Song, Jimeng Sun, Jian Tang, Petat Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, and Marinka Zitnik. 2023a. Scientific discovery in the age of artificial intelligence. _Nat._, 620(7972):47-60.
* Wang et al. (2023b) Qingyun Wang, Doug Downey, Heng Ji, and Tom Hope. 2023b. Learning to generate novel scientific directions with contextualized literature-based discovery. _arXiv preprint arXiv:2305.14259_.
* Wei et al. (2022) Jason Wei, Yi Tay, Rishi Bommassani, Colin Rafel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed Huai hsin Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022. Emergent abilities of large language models. _arXiv preprint arXiv:2206.07682_.
* Welleck et al. (2023) Sean Welleck, Ximing Lu, Peter West, Faeze Brahman, Tianxiao Shen, Daniel Khashabi, and Yejin Choi. 2023. Generating sequences by learning to self-correct. In _The Eleventh International Conference on Learning Representations, ICLR 2023, Kiegali, Rwanda, May 1-5, 2023_. OpenReview.net.
* Wu et al. (2020) Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2020. Scalable zero-shot entity linking with dense entity retrieval. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 6397-6407, Online. Association for Computational Linguistics.
* Wu et al. (2023) Yike Wu, Nan Hu, Sheng Bi, Guilin Qi, J. Ren, Anhuan Xie, and Wei Song. 2023. Retrieve-rewrite-answer: A kg-to-text enhanced llms framework for knowledge graph question answering. _arXiv preprint arXiv:2309.11206_.
* Yang et al. (2023) Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. 2023. Large language models for automated open-domain scientific hypotheses discovery. _arXiv preprint arXiv:2309.02726_.
* Young (2003) J. Young. 2003. _A Technique for Producing Ideas_. McGraw Hill LLC.
* Zheng et al. (2023) Linamin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Haotong Zhang, Joseph Gonzalez, and Ion Stoica. 2023. Judging llm-as-a-jaduge with mt-bench and chatbot arena. _arXiv preprint arXiv:2306.05685_.

 



## Appendix A Additional Experimental Details

In this section, we provide additional details on experiments, including datasets, human evaluation setups, prompts (used for research idea generation and validation), and human-induced criteria.

### Data Statistics

We visualize a distribution of core paper categories used for idea generation in Figure 7, where the categories are obtained from Semantic Scholar API10. From this, we find that the top 3 categories are computer science, medicine, and engineering.

Footnote 10: [https://www.semanticscholar.org/product/api](https://www.semanticscholar.org/product/api)

### Details on Human Evaluation

To conduct evaluations with human judges, we recruited 10 researchers from the United States and South Korea, majoring in computer science, medicine, and biology, each with a minimum of 3 published papers. For annotation, they were provided with a 6-page guideline document, which includes the task instruction and annotation examples. After reading this document, the annotators access the Label Studio platform, on which they first read the title and abstract of the target paper, and then review and evaluate the generated research ideas from different models. During the evaluation process, they are allowed to use any external tools, such as web searches. We note that they were compensated at a rate of \(\$22.20\) per hour. Also, on average, for one hour, they evaluated 3 sets of research ideas (that are generated from their own papers), with each set comprising three sub-ideas (the problem, method, and experiment design) from three different approaches (i.e., a total of 9 ideas for one hour). We perform three rounds of human evaluations with refinements in between, and, due to the cost associated with human annotations, we are able to fully evaluate a total of 150 ideas.

### Prompts for Idea Generation

We provide the prompts used to elicit the idea generations from our full ResearchAgent, specifically for instantiating problem identification, method development, and experiment design in Table 6, Table 7, and Table 8, respectively.

### Prompts for Idea Validation

We provide the prompts used to elicit the idea validation from our ReviewingAgents as well as the model-based evaluations, specifically for instantiating problem validation, method validation, and experiment design validation in Table 9, Table 10, and Table 11, respectively. In addition, we provide the criteria used, which are induced by human judgments in the next subsection (Appendix A.5).

### Criteria Induced by Human Judgements

Recall that, to align model-based evaluations with human preferences, we induce the criteria (used for automatic evaluations) with actual human judgments. We note that this is done by prompting GPT-4 with 10 pairs of generated ideas and (randomly selected) human judgments. We provide the resulting criteria for validations of problems, methods, and experiment designs in Table 13, Table 14, and Table 15, respectively.





### Results on Generated Ideas by Domain

To see the quality of the generated research ideas across different domains, we breakdown the performance of ResearchAgent according to the categories of core papers in Figure 7, and present the results in Figure 9. From this, we observe that the generated research ideas on the high-resource domains (such as Computer Science, Medicine, and Engineering where there is a greater volume of existing literature as shown in Figure 7) are superior to those generated from the low-resource domain papers (such as Physics, Chemistry, and Mathematics). This disparity might be attributed to the fact that the underlying LLMs used to generate research ideas are likely trained on data predominantly sourced from high-resource domains, which leads to enhancing their ability to comprehend scientific concepts and produce relevant research ideas.

### Analysis with Different Entity Retrieval

To see the effectiveness of different entity retrieval strategies, we perform additional experiments, replacing the co-occurrence-based entity retrieval in Equation 2 to the contextual embedding-based retrieval. Notably, this contextual embedding-based retrieval approach uses the entities that have the highest similarity to the entities appearing in the current literature (i.e., core paper and its references) used for idea generation, where the similarities are calculated based on embedding-level similarities between entities over the latent space represented by the entity linker Wu et al. (2020). Therefore, unlike the previous co-occurrence-based entity retrieval that may retrieve entities that have opposite concepts to the main idea of the current core paper (since we often mention limitations of previous work along with the proposed ideas), this embedding-based approach may provide the ResearchAgent with mostly the entities having similar concepts to the core paper. Nevertheless, as shown in Table 5, the results with the strategy of entity co-occurrence-based retrieval are comparable to the results with the new embedding-based contextual retrieval. These results might confirm that there is not much difference in the quality of entity retrieval among those two strategies, i.e., most entities retrieved from the co-occurrence-based retrieval are contextually relevant for generating research ideas.

\begin{table}
\begin{tabular}{l c c c} \hline \hline
* w/ Co-occurrence-based Retrieval & 4.52 & 4.28 & **4.18** \\
- w/ Embedding-based Retrieval & 4.49 & **4.34** & 4.16 \\
- w/o Entity Retrieval & 4.35 & 4.13 & 4.02 \\ \hline \end{tabular}
\end{table}
Table 5: Results with different entity retrieval strategies.

Figure 9: Breakdown results of the research ideas generated from our full ResearchAgent across different domains.



* [15] T. K. Lee, M. C. Park, and J. S. Kim, "A new approach to the recognition of a large number of people in an image using deep neural networks," in _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pp. 204-206, 2013.
* [16] A. L. Balasubramanian, R. D. Manning, and G. P. Salakhutdinov, "An introduction to artificial intelligence: The use of computer vision," _IEEE Transactions on Neural Networks_**98**, no. 7, pp. 1035-1040, 2014.

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* [15] M. C. B. K., and R. J. S. G. Leal, "The role of the model for predicting human activity in a social network," _Journal of Human Behavior_**20**(4):387-496, 2000.

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* [15] M. C. B. K., and R. J. S. G. Leal, "The role of the model for predicting the performance of a human-level system in a real world," _Journal of Machine Learning Research_**20**(4):367-398, 2000.

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* [15] A. K. B. C., and J. M. R. L. Sears, "Theoretical Population of the New York: The Case of Population Biology," in _Proceedings of the National Academy of Sciences_, vol. 207(4):639-648, 2000.

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\begin{table}
\begin{tabular}{l l} \hline \hline
* The research problem has been used as the cornerstone of the method development, formulated based on an in-depth review of existing studies and a potential exploration of relevant entities.
- The existing studies refer to the target paper that has been pivotal in identifying the problem and method, as well as the related papers that have been additionally referenced in the discovery phase of the problem and method. \\ \multicolumn{2}{c}{The research problem and existing studies (target paper \& related papers) are as follows:} \\ Research problem: \{researchProblem\} & Rationale: \{researchProblemRationale\} \\ Target paper title: \{paper[’title']\} & Target paper abstract: \{paper[’abstract’]\} \\ Related paper titles: \{relatedPaper[’titles’]\} & Related paper abstracts: \{relatedPaper[’abstracts’]\} \\ \multirow{2}{*}{**User Message**} & Now, proceed with your \{metric\} evaluation approach that should be systematic: \\ \multicolumn{2}{c}{
- Start by thoroughly reading the proposed method and its rationale, keeping in mind the context provided by the research problem, and existing studies mentioned above.
- Next, generate a review and feedback that should be constructive, helpful, and concise, focusing on the \{metric\} of the method.
- Finally, provide a score on a 5-point Likert scale, with 1 being the lowest, please ensuring a discerning and critical evaluation to avoid a tendency towards uniformly high ratings (4-5) unless fully justified: \\ \multicolumn{2}{c}{\(\{criteria\}\)} \\ I am going to provide the proposed method with its rationale, as follows: \\ Scientific method: \{scientificMethod\} & Rationale: \{scientificMethodRationale\} \\ \multicolumn{2}{c}{After your evaluation of the above content, please provide your review, feedback, and rating, in the format of} \\ Review: \\ Feedback: \\ Rating (1-5): \\ \hline \end{tabular}
\end{table}
Table 10: The prompt used in the full instantiation of ReviewingAgent for method validation.




\begin{table}
\begin{tabular}{l l} \hline
**Types** & **Texts** \\ \hline
* The research problem has been formulated based on an in-depth review of existing studies and a potential exploration of relevant entities.
- The scientific method has been proposed to tackle the research problem, which has been informed by insights gained from existing studies and relevant entities.
- The existing studies refer to the target paper that has been pivotal in identifying the problem, method, and experiment, as well as the related papers that have been additionally referenced in their discovery phases. \\ The research problem, scientific method, and existing studies (target paper \& related papers) are as follows: \\ Research problem: \(\{\)researchProblem\(\}\) \\ Rationale: \(\{\)researchProblemRationale\(\}\) \\ Scientific method: \(\{\)scientificMethod\(\}\) \\ Rationale: \(\{\)scientificMethodRationale\(\}\) \\ Target paper title: \(\{\)paper[’title’]\(\}\) \\ User Message** & Target paper abstract: \(\{\)paper[’abstract’]\(\}\) \\ Related paper titles: \(\{\)relatedPaper[‘titles’]\(\}\) \\ Related paper abstracts: \(\{\)relatedPaper[‘abstracts’]\(\}\) \\ Now, proceed with your \(\{\)metric\(\}\) evaluation approach that should be systematic:
- Start by thoroughly reading the experiment design and its rationale, keeping in mind the context provided by the research problem, scientific method, and existing studies mentioned above.
- Next, generate a review and feedback that should be constructive, helpful, and concise, focusing on the \(\{\)metric\(\}\) of the experiment.
- Finally, provide a score on a 5-point Likert scale, with 1 being the lowest, please ensuring a discerning and critical evaluation to avoid a tendency towards uniformly high ratings (4-5) unless fully justified: \(\{\)criteria\(\}\) I am going to provide the designed experiment with its rationale, as follows: Experiment design: \(\{\)experimentDesign\(\}\) Rationale: \(\{\)experimentDesignRationale\(\}\) After your evaluation of the above content, please provide your review, feedback, and rating, in the format of Review: Feedback: Rating (1-5): \\ \hline \end{tabular}
\end{table}
Table 11: The prompt used in the full instantiation of ReviewingAgent for experiment design validation.




\begin{table}
\begin{tabular}{c c l p{284.5pt}} \hline
**Types** & **Criteria** & **Texts** \\ \hline  & **Clarity** & It assesses whether the problem is defined in a clear, precise, and understandable manner. \\  & **Relevance** & It measures whether the problem is pertinent and applicable to the current field or context of study. \\  & **Originality** & It evaluates whether the problem presents a novel challenge or unique perspective that has not been extensively explored before. \\  & **Feasibility** & It examines whether the problem can realistically be investigated or solved with the available resources and within reasonable constraints. \\  & **Significance** & It assesses the importance and potential impact of solving the problem, including its contribution to the field or its broader implications. \\ \hline  & **Clarity** & It assesses whether the method is described in a clear, precise, and understandable manner that allows for replication and comprehension of the approach. \\  & **Validity** & It measures the accuracy, relevance, and soundness of the method in addressing the research problem, ensuring that it is appropriate and directly relevant to the objectives of the study. \\ Method & Rigorousness & It examines the thoroughness, precision, and consistency of the method, ensuring that the approach is systematic, well-structured, and adherers to high standards of research quality. \\  & **Innovativeness** & It evaluates whether the method introduces new techniques, approaches, or perspectives to the research field that differ from standard research practices and advance them in the field. \\  & **Generalizability** & It assesses the extent to which the method can be applied to or is relevant for other contexts, populations, or settings beyond the scope of the study. \\ \hline  & **Clarity** & It determines whether the experiment design is described in a clear, precise, and understandable manner, enabling others to grasp the setup, procedure, and expected outcomes. \\  & **Validity** & It measures the appropriateness and soundness of the experimental design in accurately addressing the research questions or effectively validating the proposed methods, ensuring that the design effectively tests what it is intended to examine. \\ Experiment & **Robustness** & It evaluates the durability of the experimental design across a wide range of conditions and variables, ensuring that the outcomes are not reliant on a few specific cases and remain consistent across a broad spectrum of scenarios. \\  & **Feasibility** & It evaluates whether the experiment design can realistically be implemented with the available resources, time, and technological or methodological constraints, ensuring that the experiments is practical and achievable. \\  & **Reproducibility** & It examines whether the information provided is sufficient and detailed enough for other researchers to reproduce the experiment using the same methodology and conditions, ensuring the reliability of the findings. \\ \hline \end{tabular}
\end{table}
Table 12: The criteria used for evaluating research ideas: problems, methods, and experiment designs.





\begin{table}
\begin{tabular}{p{42.7pt} p{310.8pt} p{56.9pt}} \hline
**Types** & **Criteria** & **Texts** \\ \hline  & & 1. The problem is presented in a highly ambiguous manner, lacking clear definition and leaving significant room for interpretation or confusion. \\  & & 2. The problem is somewhat defined but suffers from vague terms and insufficient detail, making it challenging to grasp the full scope or objective. \\
**Clarity** & 3. The problem is stated in a straightforward manner, but lacks the depth or specificity needed to fully convey the nuances and boundaries of the research scope. \\  & 4. The problem is clearly articulated with precise terminology and sufficient detail, providing a solid understanding of the scope and objectives with minimal ambiguity. \\  & 5. The problem is exceptionally clear, concise, and specific, with every term and aspect well-defined, leaving no room for misinterpretation and fully encapsulating the research scope and aims. \\  & 1. The problem shows almost no relevance to the current field, failing to connect with the established context or build upon existing work. \\  & 2. The problem has minimal relevance, with only superficial connections to the field and a lack of meaningful integration with prior studies. \\
**Relevance** & 3. The problem is somewhat relevant, making a moderate attempt to align with the field but lacking significant innovation or depth. \\  & 4. The problem is relevant and well-connected to the field, demonstrating a good understanding of existing work and offering promising contributions. \\  & 5. The problem is highly relevant, deeply integrated with the current context, and represents a significant advancement in the field. \\  & 1. The problem exhibits no discernible originality, closely mirroring existing studies without introducing any novel perspectives or challenges. \\  & 2. The problem shows minimal originality, with slight variations from known studies, lacking significant new insights or innovative approaches. \\
**Problem** & **Originality** & 3. The problem demonstrates moderate originality, offering some new insights or angles, but these are not sufficiently groundbreaking or distinct from existing work. \\  & 4. The problem is notably original, presenting a unique challenge or perspective that is well-differentiated from existing studies, contributing valuable new understanding to the field. \\  & 5. The problem is highly original, introducing a pioneering challenge or perspective that has not been explored before, setting a new direction for future research. \\  & 1. The problem is fundamentally infeasible due to insurmountable resource constraints, lack of foundational research, or critical methodological flaws. \\  & 2. The problem faces significant feasibility challenges related to resource availability, existing knowledge gaps, or technical limitations, making progress unlikely. \\
**Feasibility** & 3. The problem is feasible to some extent but faces notable obstacles in resources, existing research support, or technical implementation, which could hinder significant advancements. \\  & 4. The problem is mostly feasible with manageable challenges in resources, supported by adequate existing research, and has a clear, achievable methodology, though minor issues may persist. \\  & 5. The problem is highly feasible with minimal barriers, well-supported by existing research, ample resources, and a robust, clear methodology, promoting significant advancements. \\  & 1. The problem shows minimal to no significance, lacking relevance or potential impact in advancing the field or contributing to practical applications. \\  & 2. The problem has limited significance, with a narrow scope of impact and minor contributions to the field, offering little to no practical implications. \\
**Significance** & 3. The problem demonstrates average significance, with some contributions to the field and potential practical implications, but lacks innovation or broader impact. \\  & 4. The problem is significant, offering notable contributions to the field and valuable practical implications, with evidence of potential for broader impact and advancement. \\  & 5. The problem presents exceptional significance, with groundbreaking contributions to the field, broad and transformative potential impacts, and substantial practical applications across diverse domains. \\ \hline \end{tabular}
\end{table}
Table XIII: The criteria induced from human judgments for validating the identified problems, which are used to align model-based evaluations with actual human preferences.





\begin{table}
\begin{tabular}{p{42.7pt} p{310.8pt} p{569.8pt}} \hline
**Types** & **Criteria** & **Texts** \\ \hline  & 1. The method is explained in an extremely vague or ambiguous manner, making it impossible to understand or replicate the approach without additional information or clarification. 2. The method is described with some detail, but significant gaps in explanation or logic leave the reader with considerable confusion and uncertainty about how to apply or replicate the approach. 3. The method is described with sufficient detail to understand the basic approach, but lacks the precision or specificity needed to fully replicate or grasp the nuances of the methodology without further guidance. 4. The method is clearly and precisely described, with most details provided to allow for replication and comprehension, though minor areas may benefit from further clarification or elaboration. 5. The method is articulated in an exceptionally clear, precise, and detailed manner, enabling straightforward application and thorough understanding of the approach with no ambiguities. 1. The method shows a fundamental misunderstanding of the research problem and lacks any credible alignment with established scientific principles or relevant studies. 2. The method partially addresses the research problem but exhibits significant flaws in its scientific underpinning, making its validity questionable despite some alignment with existing literature. 3. The method adequately addressed the research problem but with some limitations in its scientific validity, showing a mix of strengths and weaknesses in its alignment with related studies. 4. The method effectively addresses the research problem, demonstrating a strong scientific basis and sound alignment with existing literature, albeit with minor areas for improvement. 5. The method exemplifies an exceptional understanding of the research problem, grounded in a robust scientific foundation, and shows exemplary integration and advancement of existing studies’ findings. 1. The method demonstrates a fundamental lack of systematic approach, with significant inconsistencies and inaccuracies in addressing the research problem, showing a disregard for established research standards. 2. The method shows a minimal level of systematic effort but is marred by notable inaccuracies, lack of precision, and inconsistencies that undermine the rigorousness of the method in tackling the research problem. 3. The method exhibits an average level of systematic structure and adherence to research standards but lacks the thoroughness, precision, and consistency required for a rigorous scientific inquiry. 4. The method is well-structured and systematic, with a good level of precision and consistency, indicating a strong adherence to research standards, through it falls short of exemplifying the highest level of rigorousness. 5. The method exemplifies exceptional rigorousness, with outstanding thoroughness, precision, and consistency in its systematic approach, setting a benchmark for high standards in scientific research quality. 1. The method introduces no novel elements, fully relying on existing techniques without any attempt to modify or adapt them for the specific research problem, showing a lack of innovativeness. 2. The method shows minimal innovation, with only slight modifications to existing techniques that do not substantially change or improve the approach to the research problem. 3. The method demonstrates moderate innovativeness, incorporating known techniques with some new elements or combinations that offer a somewhat fresh approach to the research problem but fall short of a significant breakthrough. 4. The method is highly innovative, introducing new techniques or novel combinations of existing methods that significantly differ from standard practices, offering a new perspective or solution to the research problem. 5. The method represents a groundbreaking innovation, fundamentally transforming the approach to the research problem with novel techniques or methodologies that redefine the field's standard practices. 1. The method shows no adaptability, failing to extend its applicability beyond its original context or dataset, showing a complete lack of generalizability. 2. The method demonstrates minimal adaptability, with limited evidence of potential applicability to contexts slightly different from the original. 3. The method exhibits some level of adaptability, suggesting it could be applicable to related contexts or datasets with modifications. 4. The method is adaptable and shows evidence of applicability to a variety of contexts or datasets beyond the original. 5. The method is highly adaptable, demonstrating clear evidence of broad applicability across diverse contexts, populations, and settings. \\ \hline \end{tabular}
\end{table}
Table 14: The criteria induced from human judgments for validating the developed methods, which used to align model-based evaluations with actual human preferences.





\begin{table}
\begin{tabular}{p{42.7pt} p{316.8pt} p{59.8pt}} \hline \hline
**Types** & **Criteria** & **Texts** \\ \hline  & & 1. The experiment design is extremely unclear, with critical details missing or ambiguous, making it nearly impossible for others to understand the setup, procedure, or expected outcomes. \\  & & 2. The experiment design lacks significant clarity, with many important aspects poorly explained or omitted, challenging others to grasp the essential elements of the setup, procedure, or expected outcomes. \\  & & 3. The experiment design is moderately clear, but some aspects are not detailed enough, leaving room for interpretation or confusion about the setup, procedure, or expected outcomes. \\  & & 4. The experiment design is mostly clear, with most aspects well-described, allowing others to understand the setup, procedure, and expected outcomes with minimal ambiguity. \\  & & 5. The experiment design is exceptionally clear, precise, and detailed, enabling easy understanding of the setup, procedure, and expected outcomes, with ambiguity or need for further clarification. \\  & & 1. The experiment design demonstrates a fundamental misunderstanding of the research problem, lacks alignment with scientific methods, and shows no evidence of validity in addressing the research questions or testing the proposed methods. \\  & & 2. The experiment design has significant flaws in its approach to the research problem and scientific method, with minimal or questionable evidence of validity, making it largely ineffective in addressing the research questions or testing the proposed methods. \\  & & 3. The experiment design is generally aligned with the research problem and scientific method but has some limitations in its validity, offering moderate evidence that it can somewhat effectively address the research questions or test the proposed methods. \\  & & 4. The experiment design is well-aligned with the research problem and scientific method, providing strong evidence of validity and effectively addressing the research questions and testing the proposed methods, despite minor limitations. \\  & & 5. The experiment design excellently aligns with the research problem and scientific method, demonstrating robust evidence of validity and outstandingly addressing the research questions and testing the proposed methods without significant limitations. \\  & & 1. The experiment design demonstrates a fundamental lack of understanding of the scientific method, with no evidence of durability at adaptability across varying conditions, leading to highly unreliable and non-replicable results. \\  & & 2. The experiment design shows minimal consideration for robustness, with significant overshirts in addressing variability and ensuring consistency accross different scenarios, resulting in largely unreliable outcomes. \\
**Experiment Robustness** & 3. The experiment design adequately addresses some aspects of robustness but lacks comprehensive measures to ensure durability and consistency across a wide range of conditions, leading to moderate reliability. \\  & & 4. The experiment design incorporates a solid understanding of robustness, with clear efforts to ensure the experiment's durability and consistency across diverse conditions, though minor improvements are still possible for optimal reliability. \\  & & 5. The experiment design exemplifies an exceptional commitment to robustness, with meticulous attention to durability and adaptability across all possible conditions, ensuring highly reliable and universally applicable results. \\  & & 1. The experiment design is fundamentally unfeasible, with insurmountable resource, time, or technological constraints that make implementation virtually impossible within the proposed framework. \\  & & 2. The experiment design faces significant feasibility challenges, including major resource, time, or technological limitations, that heavily compromise its practical execution and likelihood of success. \\
**Feasibility** & 3. The experiment design is somewhat feasible, with moderate constraints on resources, time, or technology that could be addressed with adjustments, though these may not guarantee access. \\  & & 4. The experiment design is largely feasible, with minor resource, time, or technological limitations that can be effectively managed or mitigated, ensuring a high probability of successful implementation. \\  & & 5. The experiment design is highly feasible, with no significant constraints on resources, time, or technology, indicating that it can be implemented smoothly and successfully within the proposed framework. \\  & & 1. The experimental design lacks critical details, making it virtually impossible for other researchers to replicate the study under the same conditions or methodologies. \\  & & 2. The experiment provides some essential information but omits significant details needed for replication, leading to considerable ambiguity in methodology or conditions. \\
**Reproducibility** & 3. The experiment design includes sufficient details for replication, but lacks clarity or completeness in certain areas, posing challenges for seamless reproducibility. \\  & & 4. The experiment is well-documented with clear, detailed instructions and methodologies that allow for consistent replication, albeit with minor areas for improvement. \\  & & 5. The experiment design is exemplary in its clarity, detail, and comprehensiveness, ensuring that other researchers can precisely and effortlessly replicate the study under identical conditions and methodologies. \\ \hline \hline \end{tabular}
\end{table}
Table 15: The criteria induced from human judgments for validating the experiment designs, which are used to align model-based evaluations with actual human preferences.





\begin{table}
\begin{tabular}{p{14.2pt} p{369.8pt} p{57.0pt}}
**Index** & **Types** \\  & **Title: Knowledge-Augmented Language Model Promoting for Zero-Shot Knowledge Graph Question Answering \\  & **Abstract: Large Language Models (LLM's) are capable of performing zero-shot closed-book question answering tasks, based on their internal knowledge stored in parameters during pre-training. However, such internalized knowledge might be insufficient and incorrect, which could lead LMMs to generate factually wrong answers. Furthermore, fine-tuning LLM's to update their knowledge is expensive. To this end, we propose to augment the knowledge directly in the input of LLM. Specifically, we first retrieve the relevant facts to the target equation from the knowledge graph used on semantic similarities between the question and its associated facts. After that, we prepend the retrieved facts to the input question in the form of the prompt, which is then forwarded to LLMs to generate the answer. Our framework, Knowledge-Augmented language model PromorphOR (GAPDNP), requires no model training, thus completely zero-shot. We validate the performance of our RAPING approach on the knowledge graph equation answering task, that aims to answer the user’s question based on facts over a knowledge graph, an which ours outperforms relevant zero-shot baselines by up to 4 as in average, across multiple LIMS of various sizes.** \\  & **Entities: [“Natural language”, “Learning management system”], ‘Question answering /. Wikipedia’, ‘Artificial intelligence’, ’Named entity recognition’, ‘Quality assurance’, ‘Long short-term memory’, ‘English language’, ‘Google Books’, ‘Natural-language programming’, ‘L LMS color space’, ‘GifHub’, ‘Logic learning machine’, ‘Machine translation’, ‘DBpedia’, ‘Integrated library system’, ‘Spanish language’, ‘Neural machine translation’, ‘AIXW’, ‘Artificial general intelligence’, ‘Natural-language understanding’, ‘Nonlinear programming’, ‘Coole’, ‘LoRa’, ‘Adhesive’, ‘Common Crawc’, ‘BOUGLE (metric)’, ‘Transformer’, ‘Tomas Milolov’’)** \\ \hline
**Problem** & Developing a Multimodal Knowledge-Aware Prompting System for Multilingual Zero-Shot Question Answering across Structured and Unstructured Data Sources \\ \hline \end{tabular}
\end{table}
Table 16: The examples of research idea generation results from the proposed full ResearchAgent.



- IMKO is an advanced evolution of SMLR; incorporating state-of-the-art language and vision models to handle data verbalization with context-aware distribution methods.
- Techniques like attention mechanisms are employed to weigh the relevance of different data pieces when integrated, revealing how textual and visual data augment each other in a synthesized representation.
2. Linguistically Inclusive Retrieval Engine (IIRE): - LIRE extends EEMKA with an emphasis on semantic understanding, using transformer-based models trained on diverse datasets, including alignment and cultural nuances across languages. - Specific algorithms to handle linguistic phenomena such as code-switching and transliteration are included, enhancing the application to a broader set of languages and dialects. 3. Prompt Learning and Optimization Nexus (PLON): - Building on AMPL, PLON focuses on creating a library of optimized prompts categorized by linguistic features and data modalities, using Bayesian optimization algorithms. - I includes domain adaptation techniques and a wider array of meta-learning strategies with case studies for high and low-resource languages, and outlines theoretical frameworks for their implementation. 4. Cross-Modal Integrative Learning System (C-MILS): - C-MILS advances SCAS by detailing the use of multi-head attention across modalities for effective and scalable reasoning, with exemplifications on bow each modality can enhance comprehension synergistically. - The component now incorporates a layer of abstraction to distill knowledge into a modality-agnostic format, adding reasoning and facilitating interpretability across languages and data types. 5. User-Centric Adaptation and Privacy Framework (UCAPF): - UCAPF enrichs ALCUM with a user-centric design, focusing on interaction protocols that describe user engagement cycles, feedback loops, and privacy-preserving active learning paradigms, with practical workflows and pseudocode. - Aligns with GDPR and other privacy frameworks to identify trust and address the ethical use of data in dynamic learning environments. 6. Global Evaluation and Reflective Testing System (GETSTS): - GERTS expands CEB by presenting a multi-diered validation and reflection methodology to adjust system components based on a diverse set of metrics, including fairness, bias, interpretability, and computational efficiency. - Offers a structured approach to cross-cultural evaluation, including the use of demographic and regional diversity in forming test cohorts. 7. The experiment, named 'Redefined Experiment for Multimodal Knowledge-Aware Prompting System for Multilingual Zero-shot Question Answering (REMP-ZSOA/; aims to methodically develop and validate an advanced AI system. The experiment is streamlined to address feasibility, clarity, and reproducibility concerns while uploading robustness and validity by adhering to the following refined phases: 1. Detailed System Implementation Plan: - Provide a publicly accessible project roadmap with specific milestones, resource allocation, and timelines. 2. Dataset Carration with Clear Guidelines: - Publish precise annotation guidelines with strategies to prevent bias. - Document the dataset assembly process, including source selection and data processing procedures. 3. Transparent System Training: - Offer a detailed training protocol with hyperparameters, optimizing strategies, and Bayesian optimization processes used in PLON. 4. Structured Zero-Shot Evaluation: - Outline evaluation metrics derived from GERTS with benchmark datasets to test zero-shot capabilities. 5. Clarer Interdisciplinary Evaluation Protocol: - Specify the composition of the evaluation committee, criteria for assessments, and methods for integrating the feedback. 6. Iterative Improvement with Validation Metrics: - Describe statistical methods for reflective assessment and continuous improvement, aligned with multi-diered GERTS methodology. 7. Uuer-Centric Design and Privacy Compliance Evaluation: - Structure user studies with targeted data points to assess usability and cultural adaptability. - Outline privacy compliance protocols to adhere to international standards. 8. Detailed Global Scalability Evaluation Method: - Define evaluation metrics for scalability tests and describe diverse infrastructural setups. 9. Enhanced Reporting for Reproducibility: - Commit to creating a comprehensive report with precise specifications, configurations, and instructions for replication purposes. - Utilize GitHub for version-controlled deposition of code and datasets, and arXiv for openly accessible experiment protocols and findings. _Continued on the next page 

* [16] M. C. B. Krizhevsky, A. G. Hinton, and R. Fergus. Visualizing deep networks: a survey of machine learning techniques for image recognition. In _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pages 235-240, 2017.
* [17] J. L. Friedman, S. D. Friedman, and P. W. Zisserman. Deep residual learning with convolutional neural networks. _CoRR_ abs/1809.05580v1, 2018.
* [18] T. F. Friedman, S. D. Friedman, and P. W. Zisserman. Deep residual learning with convolutional neural networks. _CoRR_ abs/1809.05580v1, 2018.
* [19] H. E. Friedman, S. D. Friedman, and P. W. Zisserman. Deep residual learning with convolutional neural networks. _CoRR_ abs/1812.05580v1, 2018.
* [20] Y. Bengio et al. Generative adversarial nets for large data sets. In _Advances in Neural Information Processing Systems (NIPS'08)_, volume 1, pages 1-10, 2008.

[MISSING_PAGE_POST]

 

* [16] M. C. Bader, J.-M. Krizhevsky, and A. Vedaldi, "A new approach to the classification of deep neural networks for image recognition," in _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, pp. 2098-2103, 2014.
* [17] D. P. Kingma and S. Welling, "Adam: a method for stochastic optimization," in _Advances in Neural Information Processing Systems (NIPS'05)_, volume 1, pages 1349-1350, 2005.
* [18] R. L. LeCun, Y. Bengio, and G. Hinton, "Deep residual learning with applications to machine learning," _arXiv preprint arXiv:1412.6110_, 2014.
* [19] T. Lin, X. Liu, Z. Chen, and H. Zhang, "Learning generative adversarial nets for image recognition," in _IEEE International Conference on Acoustics and Speech (ICASSP)_, pp. 1-10, 2014.
* [20] I. Goodfellow, I. Guyon, J. Shlens, and E. Rabinovich, "Imagenet classification by jointly connected layers," in _Neural information processing systems_, vol. 31, no. 3, p. 1130-1138, 2014.
* [21] N. Mirza, F. Ozair, and A. Courville, "Deep residual learning for image recognition," _Journal of Machine Learning Research_, vol. 31, no. 3, p. 1130-1138, 2014.
* [22] O. Russakovsky, S. Girshick, and J. Sun, "Very accurate representations for large scale visual recognition," _CoRR_, abs/1412.04180, 2014.

[MISSING_PAGE_POST]

 

* [14] M. C. B. K. S. Chan, and J. A. L. Friedman, "A survey of the statistical foundations for scientific research: a review," in _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)_, volume 208 of _Lecture Notes in Artificial Intelligence_, pages 357-369. Springer Science & Business Media, 2009.

[MISSING_PAGE_POST]