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The paper proposes MotivGraph-SoIQ, a framework to improve LLM-based academic ideation by combining (i) a Motivational Knowledge Graph (MotivGraph) built from literature using SciMotivMiner, which extracts (problem, challenge, solution) triples and induces hierarchical parent nodes, and (ii) a dual-agent Q-Driven Socratic Ideator (mentor/researcher) that iteratively refines ideas via questioning along three axes (innovation, feasibility, rationality). The Exploration Phase provides tools for fuzzy graph search, relation retrieval, Semantic Scholar queries, and a "Get Random Nodes" mechanism to spur novelty; the Deliberation Phase engages multi-round Socratic critique to mitigate confirmation bias. Experiments on a 100-topic ICLR 2025 dataset compare against AI-Researcher, CycleResearcher, AI-Scientist-v2, SciPIP, and ResearchAgent using LLM judges (Fast-Reviewer), ELO-style Swiss tournaments, and limited human evaluation. The method shows gains in novelty, experiment feasibility, and motivation scores, and ablations attribute improvements to both the graph and the mentor loop.
Cross‑Modal Consistency: 34/50
Textual Logical Soundness: 22/30
Visual Aesthetics & Clarity: 10/20
Overall Score: 66/100
Detailed Evaluation (≤500 words):
Visual ground truth (image‑first)
• Figure 1: “MotivGraph Construction” pipeline. Left→right flow: SciMotivMiner extracts P/C/S nodes, parent‑node addition, resulting graph. Colour‑coded nodes (P orange, C blue, S green).
• Figure 2: “Exploration Phase” pipeline. Researcher agent, four API tools (node_search, node_relation, semantic_search, get_random_nodes) and a dense “Initial Idea” panel.
• Figure 3: “Deliberation Phase” pipeline. Mentor asks questions on novelty/feasibility/rationality; versions 1→3; final ACCEPT.
• Figure 4: Boxplot “Overall Score vs. Discussion Rounds” (rounds 0–5, score 4–10). Upward trend to R1–R2, mild dip later.
• Figure 5a–b: API usage analytics. (a) Pie: node_search largest share; (b) Stacked bars by call position (1–8) showing early node_search dominance, later semantic/get_random.
1. Cross‑Modal Consistency
• Major 1: Claimed “0.78 higher” Novelty over second‑best baseline conflicts with Table 1 (difference ≈0.32 vs 8.07). Evidence: Sec 4.1 “0.78 … higher”; Table 1 LLM‑evaluator Nov.
• Major 2: “10.2% improvement in novelty” not supported by numbers shown. Evidence: Contribution 4 “10.2% improvement in novelty”; Table 1 best 8.39 vs 8.07.
• Major 3: “We designed three API tools” but four are listed and used in figures. Evidence: Sec 2.2.1 “three API tools” then lists fuzzy search, relation, Semantic Scholar, get_random_nodes.
• Minor 1: Caption/label mismatch “motifgraph” vs “MotivGraph.” Evidence: Fig. 1 caption “motifgraph construction pipeline.”
• Minor 2: Inconsistent model/style names (Deepseek‑R1 vs deepseek‑r1; Qwen2.5‑7B vs qwen2.5‑7b). Evidence: Table 1 rows “deepseek-r1”, “qwen2.5‑7b”.
• Minor 3: Human‑evaluation narrative deltas do not consistently match Table 2 (e.g., Exp difference). Evidence: Sec 4.1 “0.05…0.25 higher”; Table 2 Human‑ELO.
2. Text Logic
• Major 1: Central claim of “mitigating confirmation bias” lacks direct metric or operationalization. Evidence: Abstract/Intro “mitigates confirmation bias”; Sec 3–4 report novelty/exp/motivation only.
• Minor 1: Ambiguity on whether “Real Paper” is a baseline or reference; narrative alternately includes/excludes it. Evidence: Table 2 label “RealPaper”; Sec 4.1 “except Real Paper”.
• Minor 2: Some procedural details deferred to appendices hinder reproducibility of parent‑node algorithm and evaluation setup. Evidence: Sec 2.1.2 “See Appendix B.4”; Sec 3.3 “Appendix B.7”.
3. Figure Quality
• Major 1: Fig. 2 and Fig. 3 contain extensive small text; illegible at print size in a two‑column layout. Evidence: Fig. 2/3 dense multi‑paragraph boxes, small fonts.
• Minor 1: Fig. 1 node labels and descriptions are small, challenging to read. Evidence: Fig. 1 small italic descriptions.
• Minor 2: Fig. 4 lacks unit/source for “Overall Score”; unclear evaluator. Evidence: Fig. 4 y‑axis “Overall Score” only.
• Minor 3: Fig. 5b x‑axis “1–8” not explained; legend lacks full titles. Evidence: Fig. 5b axis shows numbers without definition.
Key strengths:
• Clear two‑module method; helpful P/C/S graph formalism.
• Ablations and ELO tournament provide multi‑facet evaluation.
• Useful API‑usage analysis (Fig. 5) and round‑wise performance trend (Fig. 4).
Key weaknesses:
• Quantitative claims misaligned with tables; key novelty gain overstated.
• Confirmation‑bias mitigation is asserted but unmeasured.
• Critical pipeline figures are unreadable; API‑tool numbering inconsistent.
• Naming/style inconsistencies and minor caption errors.
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This paper introduces MotivGraph-SoIQ, a novel framework designed to enhance the ideation capabilities of Large Language Models (LLMs) in academic research. The core contribution lies in addressing two critical challenges: the lack of robust theoretical grounding in LLM-generated ideas and the presence of confirmation bias that hinders the refinement of these ideas. To tackle these issues, the authors propose an integrated approach combining a Motivational Knowledge Graph (MotivGraph) and a Q-Driven Socratic Ideator. The MotivGraph, constructed using SciMotivMiner, is a structured representation of academic knowledge, comprising problem, challenge, and solution nodes extracted from scientific literature. This graph aims to provide a solid foundation for LLMs by offering relevant context and inspiration. The Q-Driven Socratic Ideator, on the other hand, is a dual-agent system consisting of a researcher and a mentor. The researcher explores the MotivGraph and generates initial ideas, while the mentor employs Socratic questioning to critically evaluate and refine these ideas, mitigating confirmation bias. The framework operates in two phases: an exploration phase where the researcher agent gathers information from the MotivGraph and Semantic Scholar, and a deliberation phase where the mentor agent challenges the researcher's ideas. The authors evaluate their framework on a dataset of ICLR 2025 paper topics, comparing it against several baselines, including AI-Scientist-v2, ResearchAgent, and CycleResearcher. The results, assessed through both LLM-based evaluations and human evaluations, demonstrate that MotivGraph-SoIQ outperforms these baselines in terms of novelty, experimental feasibility, motivational rationality, and diversity. The paper also includes ablation studies to validate the contribution of individual components of the framework. Overall, this work presents a significant step towards leveraging LLMs for academic ideation by providing a structured and critical approach to idea generation.
I find several aspects of this paper to be particularly strong. The core idea of integrating a motivational knowledge graph with a Socratic dialogue framework is both novel and well-motivated. The authors have identified a significant gap in the current application of LLMs for academic research, namely the lack of grounding and the presence of confirmation bias, and have proposed a creative solution to address these issues. The construction of the MotivGraph using SciMotivMiner is a valuable contribution, as it provides a structured representation of academic knowledge that can be used to guide the ideation process. The use of a dual-agent system, with a researcher and a mentor, is also a clever approach to mitigating confirmation bias. The mentor agent's role in critically evaluating and refining the researcher's ideas through Socratic questioning is a key strength of the framework. Furthermore, the empirical results presented in the paper are compelling. The authors have conducted a thorough evaluation of their framework, comparing it against several strong baselines. The results, which include both LLM-based and human evaluations, consistently demonstrate that MotivGraph-SoIQ outperforms these baselines in terms of novelty, experimental feasibility, motivational rationality, and diversity. The ablation studies also provide valuable insights into the contribution of individual components of the framework. The paper is also well-written and clearly explains the proposed method and the experimental setup. The authors have provided sufficient details to allow for reproducibility, and the figures and tables are helpful in understanding the results. Overall, I believe that this paper makes a significant contribution to the field of LLM-based academic ideation, and I am impressed by the creativity and rigor of the proposed approach.
Despite the strengths of this paper, I have identified several weaknesses that warrant further discussion. Firstly, the paper's evaluation, while thorough, is limited by its reliance on a single dataset of ICLR 2025 paper topics. While the authors do test on a dataset from a different domain (medical), the primary evaluation is confined to a single conference. This raises concerns about the generalizability of the proposed method to other research domains and problem types. As the 'EXPERIMENT' section indicates, the evaluation is based on topics from ICLR 2025, and while the 'Generalizability to Other Scientific Domains' section presents results from a medical dataset, the main evaluation remains focused on ICLR. This limitation is significant because the structure and nature of research problems can vary greatly across different fields, and it is unclear whether the MotivGraph, which is constructed from ICLR papers, would be equally effective in other domains. Secondly, the paper lacks a detailed analysis of the computational cost and efficiency of the proposed method. The 'EXPERIMENT' section provides the average length of generated ideas, which can be a proxy for computational cost, but there is no explicit discussion of the time or resources required for the method, especially in comparison to the baseline methods. This is a critical omission, as the practical applicability of the method depends on its computational feasibility. Without a clear understanding of the computational overhead, it is difficult to assess the trade-offs between the quality of the generated ideas and the resources required to produce them. Thirdly, the paper does not provide a detailed analysis of the types of ideas generated by the framework. While the 'EXPERIMENT' section mentions that ideas are evaluated for novelty, experimental feasibility, and motivational rationality, there is no in-depth analysis of the characteristics of the generated ideas. For example, it is unclear whether the framework is more effective at generating theoretical or applied ideas, or whether it is better at addressing certain types of research problems. This lack of analysis makes it difficult to understand the strengths and limitations of the proposed method and to identify areas for future improvement. Fourthly, the paper does not adequately address the potential for the framework to perpetuate existing biases in the training data. The 'MOTIVATION' section acknowledges the probabilistic and biased nature of LLMs, but the paper does not discuss how the framework might inadvertently reinforce these biases. This is a significant concern, as it could lead to the generation of ideas that are not truly novel or that reflect existing prejudices. Fifthly, the paper lacks a detailed analysis of the knowledge graph's quality. While the 'MOTIVATION' section describes the construction of the MotivGraph using SciMotivMiner, there is no explicit discussion of the graph's quality, coverage, or potential biases. This is a critical omission, as the quality of the knowledge graph directly impacts the effectiveness of the ideation process. Without a clear understanding of the graph's limitations, it is difficult to assess the reliability of the generated ideas. Sixthly, the paper does not provide a detailed analysis of the Socratic dialogue process. While the 'Q-Driven Socratic Ideator' section describes the interaction between the researcher and mentor agents, there is no analysis of the types of questions asked, the effectiveness of different questioning strategies, or the impact of the dialogue on the quality of the generated ideas. This lack of analysis makes it difficult to understand how the Socratic dialogue contributes to the mitigation of confirmation bias and the improvement of idea quality. Finally, the paper does not provide a detailed analysis of the errors made by the framework. While the 'EXPERIMENT' section presents overall performance metrics, there is no discussion of the types of errors that the framework makes, such as generating ideas that are not novel or that are not feasible. This lack of error analysis makes it difficult to identify areas for improvement and to understand the limitations of the proposed method. The paper also lacks a discussion of the ethical implications of using LLMs for academic ideation, including the potential for misuse and the impact on the academic community. This is a critical omission, as the increasing use of LLMs in research raises important ethical questions that need to be addressed.
To address the identified weaknesses, I recommend several concrete improvements. Firstly, the authors should conduct a more thorough evaluation of their method across a wider range of datasets, including datasets from different scientific domains and with varying levels of complexity. This would provide a more robust assessment of the method's generalizability and its ability to handle diverse research problems. For example, datasets from fields such as biology, chemistry, or social sciences could be used to test the method's performance in different contexts. Secondly, the authors should provide a detailed analysis of the computational cost and efficiency of their method. This should include a breakdown of the time and resources required for each step of the process, such as graph construction, idea generation, and evaluation. This analysis should also compare the computational cost of the proposed method with that of the baseline methods, to provide a clear understanding of the trade-offs between performance and efficiency. Thirdly, the authors should conduct a more detailed analysis of the types of ideas generated by the framework. This should include an analysis of the characteristics of the generated ideas, such as whether they are theoretical or applied, and whether they address specific types of research problems. This analysis could also explore the relationship between the structure of the knowledge graph and the types of ideas that are generated. Fourthly, the authors should address the potential for the framework to perpetuate existing biases in the training data. This could involve exploring techniques for bias mitigation, such as using diverse training data or incorporating fairness constraints into the model. The authors should also discuss the potential ethical implications of using LLMs for academic ideation, including the potential for misuse and the impact on the academic community. Fifthly, the authors should provide a more detailed analysis of the knowledge graph's quality, including its coverage, accuracy, and potential biases. This analysis should also discuss the limitations of the graph and how these limitations might affect the performance of the framework. Sixthly, the authors should provide a more detailed analysis of the Socratic dialogue process, including the types of questions asked, the effectiveness of different questioning strategies, and the impact of the dialogue on the quality of the generated ideas. This analysis could also explore the relationship between the Socratic dialogue and the mitigation of confirmation bias. Finally, the authors should provide a detailed analysis of the errors made by the framework, including the types of errors and their frequency. This analysis could help to identify areas for improvement and to understand the limitations of the proposed method. In addition to these specific recommendations, I also suggest that the authors consider exploring the potential for human-in-the-loop interaction in their framework. While the current approach focuses on automated ideation, human researchers could provide valuable feedback and guidance, which could further enhance the quality and relevance of the generated ideas. The authors could also explore the potential for using their framework to support collaborative research, by enabling multiple researchers to contribute to the ideation process.
I have several questions that arise from my analysis of this paper. Firstly, how does the performance of the MotivGraph-SoIQ framework vary across different research domains? While the paper presents results from a medical dataset, I am curious about the framework's performance in other fields, such as biology, chemistry, or social sciences. Secondly, what is the computational cost of constructing and maintaining the MotivGraph, and how does this cost scale with the size of the dataset? I am also interested in the computational cost of the Socratic dialogue process, and how this cost compares to that of the baseline methods. Thirdly, what are the specific characteristics of the ideas generated by the framework, and how do these characteristics vary depending on the input topic and the structure of the knowledge graph? I am particularly interested in whether the framework is more effective at generating theoretical or applied ideas, and whether it is better at addressing certain types of research problems. Fourthly, what are the specific types of biases that might be present in the training data, and how might these biases affect the quality and diversity of the generated ideas? I am also interested in the techniques that could be used to mitigate these biases. Fifthly, what are the specific types of questions that are most effective in the Socratic dialogue process, and how can the questioning strategies be optimized to improve the quality of the generated ideas? I am also interested in the relationship between the Socratic dialogue and the mitigation of confirmation bias. Finally, what are the most common types of errors made by the framework, and what are the underlying causes of these errors? I am also interested in the techniques that could be used to reduce the frequency of these errors. These questions are aimed at clarifying key uncertainties and methodological choices, and I believe that addressing them would further strengthen the paper.