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The paper introduces MotivGraph-SoIQ, a framework to improve LLM-based academic ideation by combining: (i) MotivGraph, a motivational knowledge graph with three node types—problem (P), challenge (C), solution (S)—extracted from literature via SciMotivMiner with hierarchical parent node construction (Section 2.1.1–2.1.2); and (ii) a Q-Driven Socratic Ideator, a dual-agent system (mentor vs researcher) that iteratively refines ideas through triple-axis questioning—innovation, feasibility, rationality—and tool-augmented exploration (graph fuzzy search, graph relation retrieval, Semantic Scholar search, and a random-node novelty injection mechanism) (Section 2.2.1–2.2.2). On a dataset of ICLR 2025 paper topics (Section 3), the method reports improvements over baselines (AI-Scientist-v2, ResearchAgent, AI-Researcher, CycleResearcher, SciPIP) in LLM-based evaluation and Swiss tournament ELO, with limited human evaluation validating some gains (Table 1 and Table 2). Ablations indicate both the MotivGraph and mentor interaction contribute substantially to performance (Table 3).
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 in LLM-based ideation: the lack of robust theoretical grounding and the presence of confirmation bias. To tackle these issues, the authors propose a dual-agent system comprising a knowledge graph, termed MotivGraph, and a Socratic dialogue mechanism. MotivGraph is constructed using SciMotivMiner, a tool that extracts problem-challenge-solution triplets from scientific literature, thereby providing a structured representation of academic knowledge. The Socratic Ideator, on the other hand, employs a mentor-agent and researcher-agent dynamic, where the mentor-agent poses critical questions to the researcher-agent, prompting it to refine and improve its generated ideas. The framework operates in two phases: an exploration phase, where the researcher-agent gathers information from the MotivGraph and external resources, and a deliberation phase, where the mentor-agent challenges the researcher-agent's ideas. The authors evaluate their approach using 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 and human evaluations, suggest that MotivGraph-SoIQ outperforms these baselines in terms of novelty, experimental feasibility, and motivational rationality. The paper's significance lies in its attempt to move beyond superficial LLM-generated ideas by incorporating structured knowledge and critical self-reflection, thus aiming to produce more grounded and robust research proposals. However, as I will detail in the following sections, the paper also presents several limitations that warrant careful consideration.
I find several aspects of this paper to be commendable. The core idea of combining a structured knowledge graph with a Socratic dialogue framework to enhance LLM-based ideation is both novel and promising. The authors have identified a significant gap in the current application of LLMs for research, namely the lack of grounding and the susceptibility to confirmation bias. By introducing MotivGraph, they provide a mechanism for LLMs to access and utilize structured academic knowledge, moving beyond purely text-based generation. The use of SciMotivMiner to construct this knowledge graph, while not without its limitations, represents a valuable contribution in itself. Furthermore, the Socratic Ideator, with its dual-agent architecture, offers a unique approach to mitigating confirmation bias by encouraging critical self-reflection and iterative refinement of ideas. The paper's experimental results, while not without their flaws, do suggest that the proposed framework has the potential to outperform existing methods in terms of novelty, experimental feasibility, and motivational rationality. The authors' attempt to address the limitations of LLMs in academic ideation by incorporating structured knowledge and critical self-reflection is a significant step forward. The paper is also well-written and clearly explains the proposed methodology, making it accessible to a broad audience. The inclusion of a case study, while not a systematic analysis, does provide some insight into the practical application of the framework. Overall, the paper presents a compelling approach to enhancing LLM-based ideation, and I believe it has the potential to make a significant contribution to the field.
Despite the strengths of this paper, I have identified several weaknesses that warrant careful consideration. First, the paper's evaluation methodology, while including human evaluation, relies heavily on LLM-based evaluations, which I find to be problematic. The paper uses Fast-Reviewer for direct quality assessment and a Swiss Tournament Evaluation for pairwise comparisons, both of which are conducted by LLMs. While the authors do include human evaluations, these are limited to a subset of the generated ideas and are used to validate the LLM-based evaluations. The paper does not provide sufficient details on the human evaluation process, such as the number of evaluators, their expertise, or the specific instructions given. This lack of transparency makes it difficult to assess the reliability of the human evaluation results. Furthermore, the paper does not include a direct comparison of the LLM-based evaluation results with human judgments, which would be crucial for establishing the validity of the LLM-based evaluations. This reliance on LLM-based evaluations, without a thorough validation against human judgments, raises concerns about the robustness of the reported results. My confidence in this issue is high, as the paper explicitly states the use of LLM-based evaluations as a primary method and lacks a detailed description of the human evaluation process. Second, the paper's experimental results, particularly in Table 2, are difficult to interpret. The table presents results for different LLMs (DeepSeek-V3, DeepSeek-R1, and Qwen2.5-7B), and the performance of the proposed method varies across these models. The paper does not provide a clear explanation for why the method performs differently with different LLMs. For example, the paper notes that the Qwen2.5-7B model has insufficient API calls, but it does not delve into the underlying reasons for these differences. This lack of analysis makes it difficult to understand the generalizability of the proposed method and its dependence on specific LLM capabilities. The paper should have included a more detailed analysis of the reasons behind the performance variations across different LLMs. My confidence in this issue is high, as the paper acknowledges the performance differences but lacks a thorough explanation. Third, the paper's evaluation is limited to a single domain, namely ICLR 2025 paper topics. While the appendix includes some results in the medical domain, the main evaluation is focused on AI/ML topics. This narrow focus raises concerns about the generalizability of the proposed method to other scientific domains. The paper does not provide sufficient evidence to support the claim that the method can be effectively applied to diverse fields with varying methodologies and knowledge structures. The paper should have included experiments on datasets from other scientific domains to demonstrate the broader applicability of the proposed method. My confidence in this issue is high, as the paper explicitly states the use of ICLR 2025 topics for the main evaluation. Fourth, the paper's reliance on a manually constructed knowledge graph raises concerns about scalability and potential biases. The paper does not provide sufficient details on the construction process of the MotivGraph, including the specific rules and criteria used for extracting and representing knowledge. The paper also does not discuss the potential biases that may be introduced during the manual construction process. This lack of transparency makes it difficult to assess the reliability and generalizability of the knowledge graph. The paper should have included a more detailed description of the knowledge graph construction process and discussed the potential biases that may be introduced. My confidence in this issue is high, as the paper lacks a detailed description of the knowledge graph construction process. Fifth, the paper's description of the Socratic dialogue process is somewhat vague. While the paper describes the roles of the mentor and researcher agents and the types of questions asked, it does not provide a detailed explanation of how the dialogue is structured and how the agents interact. The paper also does not provide a clear explanation of how the dialogue is terminated. This lack of clarity makes it difficult to understand the inner workings of the Socratic dialogue mechanism. The paper should have included a more detailed description of the dialogue process, including the specific prompts used and the criteria for termination. My confidence in this issue is high, as the paper lacks a detailed explanation of the dialogue process. Finally, the paper's case study, while providing some insight into the practical application of the framework, is not a systematic analysis. The paper should have included more case studies to demonstrate the effectiveness of the proposed method in different scenarios. My confidence in this issue is high, as the paper only includes one case study.
Based on the identified weaknesses, I recommend several concrete improvements. First, the authors should significantly enhance their evaluation methodology by incorporating a more robust human evaluation process. This should include a larger pool of evaluators with diverse academic backgrounds, clear and detailed instructions for the evaluators, and a direct comparison of LLM-based evaluation results with human judgments. The authors should also explore using different evaluation metrics that are more aligned with human judgment, such as the quality of the research question, the feasibility of the proposed methodology, and the potential impact of the research. Second, the authors should conduct a more detailed analysis of the performance variations across different LLMs. This should include an investigation into the underlying reasons for these differences, such as the models' reasoning capabilities, their ability to handle long contexts, and their API call reliability. The authors should also explore techniques to mitigate these differences and improve the generalizability of the proposed method across different LLMs. Third, the authors should expand their evaluation to include datasets from other scientific domains. This would help to demonstrate the broader applicability of the proposed method and identify potential limitations in different contexts. The authors should also analyze how the method performs in domains with less structured knowledge or where the definition of a 'solution' is more ambiguous. Fourth, the authors should provide a more detailed description of the knowledge graph construction process, including the specific rules and criteria used for extracting and representing knowledge. The authors should also discuss the potential biases that may be introduced during the manual construction process and explore methods for mitigating these biases. The authors should also consider using automated or semi-automated methods for knowledge graph construction to improve scalability. Fifth, the authors should provide a more detailed description of the Socratic dialogue process, including the specific prompts used and the criteria for termination. The authors should also explore different dialogue strategies and analyze their impact on the quality of the generated ideas. The authors should also consider using a more structured approach to the dialogue, such as a predefined set of questions or a more formal logic. Finally, the authors should include more case studies to demonstrate the effectiveness of the proposed method in different scenarios. These case studies should provide a detailed analysis of the generated ideas and the reasoning behind them. By addressing these weaknesses, the authors can significantly strengthen their paper and make a more compelling contribution to the field.
I have several questions that arise from my analysis of this paper. First, regarding the knowledge graph, I am curious about the specific rules and criteria used for extracting and representing knowledge. What are the potential biases that may be introduced during the manual construction process, and how can these biases be mitigated? Second, concerning the Socratic dialogue, I would like to understand the specific prompts used for both the mentor and researcher agents. How is the dialogue structured, and what are the criteria for termination? How does the framework ensure that the dialogue remains focused and productive? Third, regarding the evaluation, I am interested in the details of the human evaluation process. How many evaluators were involved, what were their academic backgrounds, and what specific instructions were given? How were disagreements among evaluators resolved? Fourth, concerning the generalizability of the method, I am curious about how the framework would perform in domains with less structured knowledge or where the definition of a 'solution' is more ambiguous. What adaptations would be needed to apply the method to these domains? Fifth, regarding the LLM dependence, I would like to know more about the underlying reasons for the performance variations across different LLMs. What specific characteristics of the LLMs contribute to these differences, and how can these differences be mitigated? Finally, regarding the practical implications of the method, I am curious about the computational resources required to run the framework and the time needed to generate a research idea. How does the framework handle complex or multi-faceted research problems? These questions are crucial for understanding the limitations and potential of the proposed method and for guiding future research in this area.