Title: AI-Researcher: Autonomous Scientific Innovation
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM agents

AI for Science
Abstract: The powerful reasoning capabilities of Large Language Models (LLMs) in mathematics and coding, combined with their ability to automate complex tasks through agentic frameworks, present unprecedented opportunities for accelerating scientific innovation. In this paper, we introduce AI-Researcher, a fully autonomous research system that transforms how AI-driven scientific discovery is conducted and evaluated. Our framework seamlessly orchestrates the complete research pipeline--from literature review and hypothesis generation to algorithm implementation and publication-ready manuscript preparation--with minimal human intervention. To rigorously assess autonomous research capabilities, we develop Scientist-Bench, a comprehensive benchmark comprising state-of-the-art papers across diverse AI research domains, featuring both guided innovation and open-ended exploration tasks. Through extensive experiments, we demonstrate that AI-Researcher achieves remarkable implementation success rates and produces research papers that approach human-level quality. This work establishes new foundations for autonomous scientific innovation that can complement human researchers by systematically exploring solution spaces beyond cognitive limitations.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an autonomous research system, named AI-Researcher, with seamless research pipeline: literature review, hypothesis generation, algorithm implementation and publication-ready manuscript. To evaluate the performance of the framework, the paper also introduces a benchmark, Scientist-Bench, comprising state-of-the-art papers across diverse AI research domains.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths and Contributions\n\n1.\tThe topic of autonomous research is meaningful and interesting. \n2.\tThe paper also introduces a new benchmarking to evaluate the performance of autonomous AI research. \n\nLimitations / Weaknesses\n\n1.\tSome important discussions are missing. \nAutonomous scientific discovery is not a new topic, multiple existing methods (with whole research pipeline) are available, as illustrated in your appendix A.11.2. Discussion and highlighting the novelty of this paper with existing methods are necessary. \n\n2.\tMissing comparisons with existing AI scientist frameworks.\nThe paper does not include direct comparisons with other AI scientist systems (e.g., [1], [2]), which limits the ability to assess relative performance and novelty. \n\n3.\tMissing baseline comparisons with other foundation models.\nWhile the paper reports performance differences between GPT and Claude series, it lacks a broader baseline comparison. For a more comprehensive evaluation, results from additional foundation models—such as Gemini, or DeepSeek—should be included to validate the generality and robustness of the proposed framework across architectures.\n\n4.\tMissing experimental details and unclear evaluation criteria.\nIn line 304, the paper states that it evaluates aspects such as research motivation, methodology, innovation, and experimental validation. However, it is unclear how these dimensions are assessed—whether through human annotation, LLM-based scoring, or other means. Additionally, the interpretation of the scores presented in the corresponding table is not explained. Please clarify the evaluation protocol and the meaning of the numerical results.\n\n5.\tApplicability to broader scientific domains is unclear.\nThe current work focuses primarily on AI-related tasks. It remains uncertain whether the proposed framework generalizes to other scientific fields such as biomedicine, chemistry, or physics. Please clarify the system’s domain adaptability and any assumptions or limitations that may restrict its applicability.\n\n\n[1]. Lu, C., Lu, C., Lange, R. T., Foerster, J., Clune, J., & Ha, D. (2024). The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292.\n\n[2]. Weng, Y., Zhu, M., Bao, G., Zhang, H., Wang, J., Zhang, Y., & Yang, L. (2024). Cycleresearcher: Improving automated research via automated review. arXiv preprint arXiv:2411.00816.\n\nQUESTIONS:\nAdditional Feedback\n\n1.\tTo improve clarity, the figure should explicitly depict the internal components of each agent, along with the flow of information between them. Furthermore, vague terms such as “taking notes” lack definition—please clarify their meaning and role within the research pipeline.\n2.\tCertain elements in the paper lack proper citation. For instance, Table 1 is not cited or discussed in the main text, which reduces clarity and coherence.\n3.\tThe writing should be improved more to enhance readability.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors build a prompt-only, multi-agent LLM baseline that -- given a capsule of 15-20 reference papers (and optionally an instruction\n-- (i) writes runnable code and \n-- (ii) drafts a full LaTeX manuscript.\nThey wrap this in Scientist-Bench, a benchmark that evaluates such pipelines through two sequential gates:\n\nImplementation gate -- Did the code compile & run? Was the output logically consistent (1-to-5 “Correctness” from an LLM judge)?\n\nWriting gate -- A five-model LLM ensemble conducts pair-wise reviews between the AI paper and the human target on a 7-point scale.\n\nThis evaluation pipeline -- more than the agent design -- is the core contribution.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n**Evaluation design**: \tClever two-stage protocol; pair-wise scoring limits global optimism; ICLR alignment study gives the LLM-judge external anchor.\n\n**Reproducibility:**\tFull prompts + Docker recipes; anyone can swap the backbone or prompts and immediately record benchmark scores.\n\n**Baseline:**\tProvides a clear, minimal reference system on which future, more sophisticated agent frameworks can iterate.\n\nWeaknesses: \n\n1) The study uses prompt-only agents, so we can argue that the baseline is too trivial.\n\n2) No new reasoning or learning. Please show ablations: if you drop the Resource-Analyst or Idea-Generator, does quality actually fall?\n\n3) No comparison to other methods: CycleResearcher, AgentRxiv, AutoGPT-Researcher are cited but not re-run. Hard to tell if anything improved.\n\n4) No human user study, real scientists never inspected the AI papers; LLM judges may hallucinate quality.\n\n5) Need-for-code vs text-only ablation. If you skip the code stage and just write the paper, how much does the LLM score drop? Why force code execution?\n\n6) Prompt sensitivity:\nSmall prompt edits can swing results. Show variance across prompt seeds and backbones.\n\n7) Domain coverage\nAll 22 tasks are AI-software papers; nothing from empirical sciences, so “scientific innovation” claims are limited.\n\n8) Title oversells \"innovation\". The pipeline reorganises existing knowledge; evidence of new discoveries is absent. Consider renaming to something like “Scientist-Bench: A Baseline and Benchmark for End-to-End LLM Research Pipelines.”\n\nQUESTIONS:\n1) Necessity of each agent – Provide leave-one-out ablations (Idea-Generator off, Advisor off, etc.).\n\n2) Judge robustness – Insert adversarially wrong papers (e.g., swapped labels, impossible numbers). How often do LLM judges still pass them?\n\n3) Iteration budget – Report average prompt tokens, wall-clock time, and compute for each Advisor refinement loop.\n\n4) Human audit – Even a 10-paper, two-expert pilot study would calibrate LLM scores.\n\n5) Other agentic systems: CycleResearcher, AgentRxiv, AutoGPT-Researcher, is it difficult to test them in the same scenario?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AI-Researcher, a fully autonomous research system capable of executing the complete scientific pipeline,from literature review and hypothesis generation to algorithm implementation and automated scientific writing, with minimal human intervention. The system is built on a multi-agent architecture and rigorously evaluated using a newly introduced benchmark, Scientist-Bench, which tests both guided and open-ended research capabilities. Results show high implementation success rates and surprisingly strong performance in open-ended innovation, approaching human-level quality in some cases.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n• This is one of the first works to demonstrate full-stack autonomous scientific research using LLMs, from idea generation to paper writing.\n\n• The introduction of Scientist-Bench, covering both guided and open-ended research tasks, is valuable for evaluating autonomous research agents.\n\n• Thorough experiments (22 benchmark tasks across AI domains) show high implementation success rates (up to 93.8%) and moderate correctness, especially in open-ended tasks.\n\nWeaknesses:\n\n• There is notable variability across LLM-based evaluators (e.g., GPT-4o vs. Claude-3.7), highlighting the fragility of using LLMs alone to assess research quality.\n\n• Performance in compute-heavy domains (e.g., diffusion models) is capped by available resources rather than theoretical limitations.\n\n• While the system is compared to baselines in execution metrics, comparisons to other autonomous research frameworks (e.g., CAMEL, MetaGPT, AutoAgent) are missing.\n\nQUESTIONS:\n1. How do the authors plan to bridge the remaining quality gap between AI-generated and human-written papers? Could multi-pass refinement or retrieval augmentation help?\n2. The results show inconsistent assessments from GPT-4o, Claude, etc. Would involving human domain experts for a subset of evaluations strengthen the paper?\n3. How does AI-Researcher compare to recent autonomous frameworks (e.g., MetaGPT, CAMEL, AutoAgent) in terms of generality, innovation capacity, or end-to-end automation?\n4. For Level-2 tasks with low correctness scores, can the authors provide concrete examples or failure modes?\n5. Could the framework be extended to real-world interdisciplinary domains (e.g., bioinformatics, climate science)? What adaptations would be needed?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n&nbsp;\n\nThe authors introduce AI-Researcher an LLM-based multiagent framework for conducting machine learning research. The authors also introduce Scientist-Bench, a benchmark dataset of 22 papers for evaluating AI-Researcher on its ability to perform machine learning research on topics such as graph neural networks, vector quantization, and computer vision at two levels, to execute specific research instructions, and to formulate and execute novel research directions. The authors validate the performance of AI-Researcher using an LLM-as-a-judge evaluation methodology comparing the outputs against those of human researchers. Overall, while I think the system is interesting, novel, and impactful I have major concerns about the clarity of the paper, the broken link to the anonymous GitHub repository, as well as the robustness of the evaluation methodology. Concrete action points, include moving much of the technical details from the appendix to the main paper, performing a human expert evaluation in place of LLM-as-a-judge, and providing an operation anonymous GitHub link for the codebase. If these issues can be addressed in the rebuttal phase I will be inclined to increase my score.\n\n&nbsp;\n\n**Post-Rebuttal Update**: As promised, I have upgraded my score from 3 to 5 in light of the changes made by the authors during the rebuttal phase.\n\n&nbsp;\n\nSTRENGTHS AND WEAKNESSES:\n&nbsp;\n\nThe main strength of this work is that it showcases an interesting multiagent architecture that can be used to investigate, evaluate, and communicate machine learning research ideas. I think the topic is highly relevant for the NeurIPS community. In terms of the papers weaknesses I see three core issues related to a) clarity b) robustness of evaluation and c) reproducibility. I order the core issues in order of importance with a) being the most important. Given that clarity is the biggest issue I see with the current work I leave my comments below in temporal order so that the authors may understand the readers' experience making their way through the paper in its current form.\n\n&nbsp;\n\n**__MAJOR POINTS__**\n\n&nbsp;\n\n1. The link to the Anonymous GitHub repo in the abstract appears to be broken. It would be great if the authors could address this in the rebuttal phase. This appears just to be a simple oversight.\n\n2. The nature of the Scientist-Bench dataset is unclear from the description provided in the main paper. From reading the main text I only understood that Scientist-Bench is comprised of 22 papers and the research instruction is drawn from one of the papers. Section A.7 of the appendix is far more comprehensive and should be moved to the main text.\n\n3. In terms of the title of the paper it was unclear to me whether AI-Researcher was a generalist agent that performed any kind of research. Whereas after reading the paper it appears as though it is an LLM system that performs AI research specifically. It may be worth revising the title of the paper to aid in this disambiguation.\n\n4. Some aspects of the writing in the main paper appear not to be grounded in technical detail. For example the challenges paragraph within Section 3.1.2. What are \"Challenges\" from a technical perspective? Components of the prompt? Again, transferring material from the appendix to the main paper will greatly aid in improving the clarity of the paper.\n\n5. Again, it would greatly benefit clarity if the nature of the experiments used to evaluate AI-Researcher were described in the introduction. For example, the human performance comparison in Section 4.2 compares AI-Researcher generated papers to comparable human-authored papers using an LLM judge following ICLR guidelines. Such a description would help contextualize the evaluation criteria for the authors' method.\n\n6. The curation procedure of the papers for Scientist-Bench should be described in the main text e.g. the summary statistics in the table in Section A.8 of the appendix. Much of the main text appears to be repetitious and describes at a high level what AI-Researcher is meant to accomplish. Much of this material can be deleted in favor of concrete technical details.\n\n7. It seems that the criterion for near-human quality should be 0 on the 7-point scale and not -1.0? This will impact the comparability percentages reported in the main paper It would be worth introducing the 7-point scale in the main text.\n\n8. Although Figure 9 provides an example of a paper produced by AI-Researcher, it would be great to have experiments where the papers produced by AI-Researcher are evaluated by human experts. The paper in Figure 9 has a few features that would be unlikely to appear in human-generated research. For example, errorbars are not reported in the experiments and the introduction only appears to cite a single reference.\n\n&nbsp;\n\n**__MINOR POINTS__**\n\n&nbsp;\n\n1. It would be great to have a more descriptive title for the method introduced by the authors.\n\n2. It would be great if in-text and parenthetical citations could be used appropriately e.g. (Wang et al. 2023) in the introduction should be parenthetical since the work does not constitute part of the sentence narrative.\n\n3. Line 105, it is mentioned that the agent selects papers from scientific databases. Which scientific databases?\n\n4. Line 245, it appears that Level 1 and Level 2 tasks are first introduced without definition? They only seem to be defined in the caption of Figure 5 as adaptation and innovation tasks respectively.\n\n5. Figure 4, it would be worth specifying in the Figure caption that the correctness score is defined on a 1-5 scale as readers may misinterpret it as a percentage.\n\n6. Which Claude models are used? Where is the citation to the relevant system card? Was Sonnet or Haiku used?\n\n7. In Figure 5, the average scores across tasks depicted by the dotted horizontal lines are difficult to see.\n\n8. I would phrase Section 4.2 as a \"human performance comparison\" rather than a \"pairwise comparison\".\n\n9. In Section 4.2 there doesn't appear to be a description of the rating system used to score AI-Researcher's outputs. Why are the scores negative? How was comparable quality to human-authored research papers determined? Update: These details are provided in the appendix but were not apparent in the main paper.\n\n10. I believe the ablation study in Section 4.4 would be more correctly termed a sensitivity analysis since the authors are assessing the system's sensitivity to the choice of backbone LLM rather than adding or deleting a system component.\n\n11. There are missing capitalizations in the references e.g. \"ai\" in place of \"AI\", \"llm\" in place of \"LLM\".\n\n12. There are some missing journal/conference references e.g. SciBench [4] was published at ICML 2024.\n\n13. Line 1760, typo, \"groundtruth\".\n\n14. For the Guided Innovation task it is not clear how the reference papers are selected from the main paper? In the appendix the procedure is clarified.\n\n15. The number of random trials (16 for LLM-as-a-judge) and errorbars should be provided in the results of the main paper.\n\n16. In terms of multi-agent collaboration frameworks it would be worth mentioning [6] where the authors develop a framework for optimizing a multiagent graph with binary edge weights.\n\n17. In terms of agentic task execution systems it would be worth mentioning the related works of [4,5] which also leverage language agents for challenging scientific tasks.\n\n18. Although concurrent to the NeurIPS submission deadline, it may be worth the authors referencing Robin [1] as related work in this area.\n\n19. In Listing 16, there is some bias in the prompt of the experiment analysis agent, namely it is instructed to design experiments to show the superiority of the idea rather than to dispassionately examine the idea.\n\n20. Typo line 1223, \"Listing\" in place of \"List\"?\n\n21. Missing full stop at the end of Equation 1.\n\n22. It would be worth citing the system cards of the backbone LLMs use, e.g. [7] for o1.\n\n&nbsp;\n\n**__REFERENCES__**\n\n&nbsp;\n\n[1] Ghareeb, A.E., Chang, B., Mitchener, L., Yiu, A., Szostkiewicz, C.J., Laurent, J.M., Razzak, M.T., White, A.D., Hinks, M.M. and Rodriques, S.G., 2025. [Robin: A multi-agent system for automating scientific discovery](https://arxiv.org/abs/2505.13400). arXiv preprint arXiv:2505.13400.\n\n[2] Skarlinski, M.D., Cox, S., Laurent, J.M., Braza, J.D., Hinks, M., Hammerling, M.J., Ponnapati, M., Rodriques, S.G. and White, A.D., 2024. [Language agents achieve superhuman synthesis of scientific knowledge](https://arxiv.org/abs/2409.13740). arXiv preprint arXiv:2409.13740.\n\n[3] Wang, X., Hu, Z., Lu, P., Zhu, Y., Zhang, J., Subramaniam, S., Loomba, A.R., Zhang, S., Sun, Y. and Wang, W., 2024, July. [SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models](https://proceedings.mlr.press/v235/wang24z.html). In International Conference on Machine Learning (pp. 50622-50649). PMLR.\n\n[4] Kon, P.T.J., Liu, J., Ding, Q., Qiu, Y., Yang, Z., Huang, Y., Srinivasa, J., Lee, M., Chowdhury, M. and Chen, A., 2025. [Curie: Toward rigorous and automated scientific experimentation with AI agents](https://arxiv.org/abs/2502.16069). arXiv preprint arXiv:2502.16069.\n\n[5] Narayanan, S., Braza, J.D., Griffiths, R.R., Ponnapati, M., Bou, A., Laurent, J., Kabeli, O., Wellawatte, G., Cox, S., Rodriques, S.G. and White, A.D., 2024. [Aviary: training language agents on challenging scientific tasks](https://arxiv.org/abs/2412.21154). arXiv preprint arXiv:2412.21154.\n\n[6] Zhuge, M., Wang, W., Kirsch, L., Faccio, F., Khizbullin, D. and Schmidhuber, J., 2024, July. [GPTSwarm: Language agents as optimizable graphs](https://openreview.net/forum?id=uTC9AFXIhg). In Forty-first International Conference on Machine Learning.\n\n[7] Jaech, A., Kalai, A., Lerer, A., Richardson, A., El-Kishky, A., Low, A., Helyar, A., Madry, A., Beutel, A., Carney, A. and Iftimie, A., 2024. [OpenAI o1 system card](https://arxiv.org/abs/2412.16720). arXiv preprint arXiv:2412.16720.\n\n&nbsp;\n\nQUESTIONS:\n&nbsp;\n\n1. In Table 6 are the average ratings computed across both the accepted and rejected papers?\n\n2. As far as I can tell, AI-Researcher is reliant on reference papers provided by a human user. Is there scope to integrate AI-Researcher with components of PaperQA2 [2], which would enable it to perform open-ended search over the scientific literature?\n\n&nbsp;\n\nLIMITATIONS:\n&nbsp;\n\n1. One limitation is that the datasets need to be provided upfront. Would be better if the system could locate its own data.\n\n&nbsp;", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes AI-Researcher, an autonomous multi-agent system designed to orchestrate the entire scientific discovery pipeline, from literature review, algorithm implementation to writeup. To evaluate such systems, the paper also introduces Scientist-Bench, a new comprehensive benchmark based on 22 state-of-the-art AI research papers across four domains. The benchmark defines two task levels: guided innovation (Level-1) and open-ended exploration (Level-2). The paper demonstrates that AI-Researcher can achieve high implementation success rates and produce research papers that approach human-level quality. One of the key findings is that AI-Researcher performs better on open-ended exploration tasks, suggesting a strong capability for internal knowledge synthesis.\n\nSTRENGTHS AND WEAKNESSES:\n#### Strengths:\n- The paper addresses the grand challenge of automating scientific discovery, which is both important and timely. \n- AI-Researcher is well-designed end-to-end multi-agent system. The modular architecture uses specialized agents for each research stage (e.g., Knowledge Acquisition, Idea Generation, Code Agent, Documentation Agent)\n- Scientist-Bench provides a standardized framework for evaluating autonomous research agents, based on real, high-impact papers. I read the appendix regarding how they constructed this benchmark, and it's well-designed. \n- AI-Researcher is tested across multiple AI domains, using modern LLMs as backbones. Their evaluation criteria  (such as completeness, correctness) make sense (see weaknesses for some questions though). Pairwise comparison \n\n#### Weaknesses\n- The claim of producing \"publication-ready manuscripts\" may be an overstatement. The quantitative results (Table 1) show that AI-generated papers are still rated as inferior to human-authored ones, although some are comparable. Other parts of the writing also contain overstatements or unclear phrasing. See the Questions section for additional examples. (My overall rating assumes that these issues will be addressed in the revision.)\n- I couldn't find detailed analysis or discussions regarding the cost of producing a single paper using AI-Researcher in terms of hours and API costs.\n- Although the appendix touches on this, the authors may wish to discuss the ethical implications of autonomous science generation (e.g., the creation of plausible but flawed research, issues of accountability, and risks of paper-milling) more thoroughly in the main paper, although this is more a matter of taste and does not affect my overall rating.\n\nQUESTIONS:\n- Are figure generations fully automated as well? If so, do you use VLMs for figure review? In my experience, VLMs still sometimes struggle to evaluate the quality of figures for experimental findings. How did the authors address this issue?\n\n- The superior performance on Level-2 (open-ended) tasks is a major finding. Could you elaborate more on this? For example, did you control for possible biases in the reviewer agent that might lower scores for incremental or deliberate work, which could be easier to produce for Level-1 tasks?\n\n- Regarding the \"Knowledge Acquisition Agent,\" why can't the AI-Researcher itself gather 10–15 references? Why is human input still required for this step?\n\n- When the Resource Agent creates atomic components with mappings between math and code, are unit tests also included?\n\n- I believe the \"Idea Generator\" is conditioned on the 10–15 references provided by human researchers, but this isn’t explicitly stated in Section 3.1.2.\n\n- Line 55: \"This recursive refinement mechanism enables continuous bidirectional feedback between theoretical concepts and their implementations.\" -- I did not understand this on my first read.\n\n- Line 63: \"structured feedback cycles\" -- In what sense are they structured? This should refer to the appendix for examples.\n\n- Line 166: \"This approach increases implementation success rates with test-time scaling capabilities.\" -- Is this statement supported by empirical evidence? I couldn’t find any test-time scaling experiments in the paper.\n\n- Line 170: \"It enforces strict code independence principles while ensuring faithful translation of academic concepts into working code.\"--How do you verify that the system ensures faithful translation of concepts into code?\n\n- Line 176: \"specialized navigation tools\" -- I believe this refers to a set of function calls defined in the appendix. If so, they should be referenced from the main paper.\n\n- Line 184: \"while persistently unsuccessful implementations receive an ‘unfeasible’ classification after multiple refinement attempts.\" -- I’d be curious to see more examples of failure cases resulting in this \"unfeasible\" classification.\n\n- Line 187: What exactly do \"validation studies\" refer to?\n\n- Line 201: \"while maintaining scientific integrity and narrative coherence\" -- How is it verified that the system maintains scientific integrity and narrative coherence?\n\n- Line 216: What are \"domain-appropriate templates\"?\n\n- Line 219: Can you clarify what \"academic checklists\" are?\n\n- Line 221: \"manuscripts meet publication standards without the hallucinations and inconsistencies that typically plague LLM-generated long-form content.\" -- Is this claim verified? It might be an overstatement.\n\n- Line 252: \"despite multiple debugging iterations.\" -- How many debugging attempts were performed?\n\n- Figure 5 should clarify what the bold numbers represent. I didn’t understand them when first looking at the figure. I only later realized they refer to the average performance for Claude vs. the 4o series. Also why did the Claude series perform so poorly on GNN?\n\n- Line 300: Typo--\"((RQ2)\" should be fixed.\n\n- Line 307: \"a substantial proportion of AI-generated papers (15.79% to 78.95%) demonstrate quality comparable to human research.\" -- 15.79% does not seem substantial.\n\n- Line 330: \"suggesting that AI-Researcher maintains consistent performance across different research domains without catastrophic degradation in any particular field.\" -- This conclusion doesn’t follow from the preceding statements. The results rather suggest inconsistency across domains, not consistent performance.\n\nLIMITATIONS:\n- The authors have adequately identified key limitations in an appendix section. They correctly point out the limited diversity of LLM backbones used in the evaluation and the need for a more systematic treatment of ethical and safety considerations.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-10"}