Pith. sign in

REVIEW 5 major objections 4 minor 3 cited by

Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions

T0 review · 5 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This survey argues that LLM-driven scientific discovery is best understood as a pipeline from data to hypothesis generation to validation, and it maps the methods, datasets, and open problems along that pipeline.

desk verdict The survey's taxonomy is sensible, but the factual map is unreliable: several named tools are misdescribed or lack supporting references, which is a load-bearing flaw for a field-map. read the letter →

arxiv 2505.04651 v1 pith:PYMTFCAC submitted 2025-05-06 cs.CL cs.LG

Adithya Kulkarni , Fatimah Alotaibi , Xinyue Zeng , Longfeng Wu , Tong Zeng , Barry Menglong Yao , Minqian Liu , Shuaicheng Zhang
show 2 more authors
Lifu Huang Dawei Zhou
This is my paper · ORCID
classification cs.CLcs.LG
keywords scientifichypothesisgenerationvalidationlargelanguagemodelsknowledgegraphsretrieval-augmentedcausalinferencemulti-agentsystemsbenchmarkdatasets
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey's central claim is a mapping claim: it asserts that the scattered landscape of LLM-powered scientific hypothesis generation and validation can be organized into a coherent, interdisciplinary taxonomy. On the generation side it distinguishes symbolic discovery systems from modern LLM pipelines and groups methods into knowledge-driven, data-driven, AI-exploration, text-mining, simulation, collaborative, causal, dynamic-knowledge, and multi-agent families; on the validation side it groups methods into experimental, simulation-based, predictive, cross-domain, human-AI/crowdsourced, causal, benchmarking, multi-agent, explainability, and hybrid families. It also holds up two resources as concrete contributions: AHTech, a high-throughput electrolyte-additive dataset for battery research, and CSKG-600, a set of 600 expert-labeled hypothesis triples over scholarly knowledge graphs. If the map is accurate, the paper gives researchers a tool-selection guide and a forward agenda organized around novelty-aware generation, feasible validation, and ethical safeguards.

What carries the argument

The machinery carrying the survey is its taxonomy-cum-pipeline: data input (literature, knowledge graphs, datasets) flows into hypothesis generation, then into iterative validation, refinement, and deployment, and the paper decomposes each stage into named approach families. The formal anchors are compact definitions: novelty as the inverse of average cosine similarity to existing hypotheses, feasibility as a weighted sum of empirical and theoretical scores, and quality as the weighted aggregate $Q(H) = w_N N(H) + w_F F(H) + w_R R(H)$ with weights summing to one. These equations let the survey translate qualitative debates about novelty and feasibility into measurable criteria, while the taxonomy itself is what organizes the hundreds of tools and datasets into a navigable map.

What would settle it

Draw a random sample of twenty named systems and datasets from the survey (for instance SciFact, CausalNet, BayesCausality, InterveneAI, AHTech, and CSKG-600), and verify each against its cited source or a public repository; if a sizable share cannot be located or are characterized in ways their own documentation contradicts, the survey's central claim of providing a trustworthy map fails.

Watch

Extended reading notes

Core claim

The paper's central claim is an ordering claim: the many LLM-based systems for scientific discovery form a recognizable pipeline, and progress can be assessed by how well each stage—data integration, hypothesis creation, validation, and refinement—is served by named methods. It argues that early symbolic discovery systems, which search over explicit rule spaces, and LLM-based generative systems, which produce hypotheses through probabilistic token prediction, are complementary rather than competing, and that hybrid pipelines coupling generation with simulation, causal inference, and human oversight are becoming the norm. The survey also presents two resources as concrete contributions: AHTech, a high-throughput dataset of 180 electrolyte additives tested across 200 electrochemical cycles for aqueous zinc batteries, and CSKG-600, a set of 600 expert-labeled candidate hypotheses over scholarly knowledge graphs for evaluating link-prediction systems. The intended payoff is a roadmap that lets researchers choose methods, datasets, and validation strategies by matching them to the structure of their scientific question.

Load-bearing premise

The whole map depends on the accuracy of its descriptions of dozens of named tools and datasets; if many of those descriptions are wrong or the tools do not exist as described, the survey ceases to be a reliable guide.

Editorial extensions

If this is right

  • If the map is right, a researcher facing unstructured biomedical text can pick retrieval-augmented and knowledge-graph generation methods, while a researcher with well-structured symbolic data can use rule-search approaches, instead of guessing.
  • The two highlighted resources give the community concrete evaluation points: AHTech for high-throughput electrochemical screening and CSKG-600 for hypothesis generation over scholarly knowledge graphs.
  • The validation taxonomy implies that credible LLM discovery systems should combine at least two validation families, such as simulation plus causal inference, to catch both feasibility and mechanistic errors.
  • The roadmap's future directions—novelty-aware training, risk-sensitive evaluation, explainable orchestration, and multi-agent reasoning—define concrete criteria for judging next-generation systems.
  • Because the survey frames generation and validation as an iterative loop, it implies that benchmarks should measure the full loop rather than isolated stages.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper leaves implicit: use CSKG-600's expert labels to compare retrieval-augmented generation against plain LLM generation on the validity of the resulting hypotheses.
  • The survey implies that generation and validation are becoming one closed loop, but it never formalizes the loop; a natural next step is a unified scoring function that updates novelty and feasibility weights from validation feedback.
  • If the map holds, the field's bottleneck shifts from data access to novelty measurement, since generation lacks standardized novelty metrics beyond cosine similarity.
  • The two highlighted datasets are small enough that pooling them with existing graph and materials benchmarks would be a low-cost way to test cross-domain generalization of hypothesis-generation systems.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. This manuscript presents itself as a structured survey of LLM-driven scientific hypothesis generation and validation, covering methods, datasets, tools, and future directions. It proposes a taxonomy of generation approaches (knowledge-driven, data-driven, AI-driven exploration, text mining, simulation, interactive, causal, dynamic, and multi-agent) and validation approaches (experimental, simulation, predictive, cross-domain, human-AI, causal, benchmarking, multi-agent, explainability, and hybrid). The abstract additionally claims to introduce two new resources, AHTech and CSKG-600. The paper's stated central contribution, per §1.3, is to provide a reliable, interdisciplinary roadmap of the field that researchers can use for tool selection and research planning.

Significance. If the survey were factually reliable, it would fill a useful niche: a cross-domain map of LLM-based hypothesis generation and validation, with a taxonomy, dataset summaries, and a forward-looking roadmap. The paper also ships a large set of worked definitions (Eqs. 1–8), a structured table of datasets, and a discussion of ethical and regulatory concerns. However, the value of a survey lies in the accuracy of the field map it provides. The manuscript contains multiple misattributions, unsupported tool names, and a misrepresentation of its own contributions, which calls into question the reliability of the entire roadmap. The existence of machine-checkable derivations is not relevant here because the central claim is factual accuracy, not formal proof.

major comments (5)
  1. [§4.4] The description of SciFact is factually incorrect and load-bearing for the text-mining subsection. The text states that 'SciFact leverages co-occurrence patterns in scientific literature to propose new drug-disease relationships,' but the canonical SciFact system (Wadden et al., ACL 2020) is a benchmark for verifying scientific claims against evidence, not a hypothesis-generation or drug-repurposing tool. Since the survey's usefulness depends on correctly characterizing representative systems, this error undermines the credibility of the taxonomical claims in §4.
  2. [§4.7] The tools CausalNet, BayesCausality, and InterveneAI are presented as concrete systems for causal inference, but the citations provided are general references on causal modeling (Peters et al. 2017, Lucas 2007, Neuberg 2003) and do not establish the existence of these named tools. Without verifiable sources, readers cannot determine whether these tools exist or perform as described, and the causal-inference section of the survey therefore fails as a reliable map of available methods.
  3. [§5.4] The cross-domain validation subsection names CrossValNet, InterdisciplinaryTest, and TransferTest, with citations to Sybrandt et al. [2018], Zhou et al. [2024], and Touvron et al. [2023]. None of these cited works appear to contain the named systems. This is not a minor citation slip; the survey is asserting the existence of specific tools as examples of a methodological category, and the absence of supporting references for three consecutive named systems indicates a systematic verification failure in this subsection.
  4. [Abstract and §3 / Tables 2–3] The abstract claims the paper is 'introducing new resources like AHTech and CSKG-600,' but the manuscript text attributes AHTech to Lin et al. [2025] and CSKG-600 to Borrego et al. [2025] as pre-existing works. Tables 2 and 3 likewise list these as third-party datasets. This is a direct misrepresentation of the survey's own contribution, and it compounds the credibility problem: the central claim of introducing new resources is not supported by the paper's own content.
  5. [General accuracy of the field map] The recurring pattern of misdescribed or unsupported named tools is not confined to one subsection; it appears in §4.4, §4.7, and §5.4. Because the survey's primary contribution is to provide a reliable field map, these errors are not isolated presentational flaws. They undermine the central claim of §1.3 that the survey offers a 'structured, interdisciplinary overview' that can guide tool selection and future research. As written, the roadmap cannot be trusted without extensive independent verification.
minor comments (4)
  1. [Throughout] Several inline citations are incomplete, including 'Džeroski et al.' with no year and multiple references to 'Zhou et al. [2024]' that do not disambiguate between at least two distinct works. The references for tools such as CrowdScience, ConceptNet, ExplanatoryAI, and FeedbackLoopAI are missing or only indirectly implied.
  2. [Figures 3–4] The figures contain garbled labels and formatting artifacts, e.g., 'MeS', 'C O C O Datas', and 'Open Graph Benchmar' in Figure 3, and inconsistent use of punctuation in Figure 4. These reduce readability and should be corrected.
  3. [§4.4] In the sentence 'MOLIERE Sybrandt et al. [2017, 2018]demonstrates how text mining...', there is a missing space before 'demonstrates', and the same pattern appears elsewhere in the manuscript.
  4. [§2.1, Eq. (2) and (5)] The definitions of feasibility and quality include weights w_emp, w_theo, w_N, w_F, and w_R that are never specified or exemplified. This is acceptable as a formal definition, but the text would be clearer if it noted how these weights might be set in practice.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a survey whose equations are definitions and whose claims rest on external literature, not on fitted predictions or self-derived premises.

full rationale

The paper is a survey, not a derivation. The formal expressions in Section 2 (Eqs. 1-6) define novelty, feasibility, open and closed domains, hypothesis quality, and bad-hypothesis classification as weighted aggregates or threshold conditions; they contain no fitted parameters, are not used to generate empirical predictions, and are not claimed to follow from one another. The taxonomy in Sections 4-5 organizes existing systems and cites external works for their existence and behavior. No step fits a parameter to a subset of data and then 'predicts' that subset or a closely related quantity. The citations to Zhou et al. and Huang et al. are ordinary literature references; even if some are self-citations, none is invoked as a uniqueness theorem or as the sole justification for a central premise. The abstract's phrasing that AHTech and CSKG-600 are 'new resources' while the text attributes them to Lin et al. [2025] and Borrego et al. [2025] is an attribution inconsistency, not a circular reduction. The skeptic's complaint about misdescribed tools (SciFact, CausalNet, InterveneAI, etc.) concerns factual accuracy and verifiability of external references, which is outside the circularity definition. A survey's reliability depends on its citations being correct, but incorrect citation characterization is not the same as the survey's conclusions being equivalent to its inputs by construction. Therefore no circular step is identified and the circularity score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 6 invented entities

The survey's proposed scoring equations depend on unspecified weights and thresholds. The factual sections depend on the existence and correct characterization of many named tools and datasets, several of which cannot be verified. No free parameters are fitted to data in this paper; they are undefined constants in definitions.

free parameters (4)
  • Weights w_emp and w_theo in feasibility F(H) (Eq. 2)
    Defined as tunable weights with w_emp plus w_theo equal to 1; no values or fitting procedure are given, so the feasibility definition is unfalsifiable as stated.
  • Weights w_N, w_F, w_R in quality Q(H) (Eq. 5)
    Defined as tunable weights with sum 1; no method for assigning them from data or expert judgment is provided.
  • Thresholds tau_N and tau_F for bad hypothesis (Eq. 6)
    The paper says thresholds are domain-specific but does not specify how they should be set; any classification of bad hypotheses depends on unstated constants.
  • Weights alpha, beta, lambda in symbolic scoring (Eq. 7)
    Illustrative balancing weights for fit versus complexity in the symbolic discovery description; no estimation procedure is provided.
assumptions (4)
  • domain assumption Cosine similarity in embedding space is a valid measure of hypothesis novelty.
    Equation (1) defines novelty as one minus average cosine similarity between hypothesis embeddings, assuming embeddings capture semantic difference and that a linear average is meaningful.
  • domain assumption Feasibility separates into independent empirical and theoretical components that combine linearly.
    Equation (2) asserts F(H) equals w_emp times f_empirical plus w_theo times f_theoretical with weights summing to 1, with no justification of additivity or independence.
  • domain assumption Hypothesis quality is an additive weighted sum of novelty, feasibility, and relevance.
    Equation (5) defines Q(H) as a weighted sum of N, F, and R, implying these dimensions are commensurable and independent.
  • ad hoc to paper The named tools and datasets in Sections 4 and 5 exist and perform as described.
    The survey relies on the accuracy of descriptions such as SciFact, CausalNet, BayesCausality, InterveneAI, and the Section 5.4 tools, several of which are unverifiable or misattributed.
invented entities (6)
  • CausalNet
    purpose: Presented as a biomedical causal inference tool built on structural causal models.
    The only citation, Peters et al. [2017], is a textbook on causal inference and does not document a system named CausalNet.
  • BayesCausality
    purpose: Presented as a Bayesian-network causal discovery tool for social and biomedical sciences.
    Citation to Lucas [2007] is insufficient; no verifiable system of this name is documented.
  • InterveneAI
    purpose: Presented as an interventional analysis tool for materials science.
    Citation to Neuberg [2003] does not identify a software system named InterveneAI.
  • CrossValNet
    purpose: Presented in Section 5.4 as a cross-domain validation tool integrating genomics, pharmacology, and environmental data.
    No reference is provided for this named system.
  • InterdisciplinaryTest
    purpose: Presented in Section 5.4 as a validation tool across physics, chemistry, and biology.
    No reference is provided; the name appears to be illustrative rather than an existing system.
  • TransferTest
    purpose: Presented in Section 5.4 as a transfer-learning validation tool across scientific domains.
    No reference is provided; the description is generic and unverifiable.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions." pith.science (2026). https://pith.science/paper/PYMTFCAC

@misc{pith2026250504651,
  author       = {Pith},
  title        = {Pith review of: Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PYMTFCAC}},
  note         = {Machine review of arXiv:2505.04651}
}
read the original abstract

Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmentation. This survey provides a structured overview of LLM-driven approaches, including symbolic frameworks, generative models, hybrid systems, and multi-agent architectures. We examine techniques such as retrieval-augmented generation, knowledge-graph completion, simulation, causal inference, and tool-assisted reasoning, highlighting trade-offs in interpretability, novelty, and domain alignment. We contrast early symbolic discovery systems (e.g., BACON, KEKADA) with modern LLM pipelines that leverage in-context learning and domain adaptation via fine-tuning, retrieval, and symbolic grounding. For validation, we review simulation, human-AI collaboration, causal modeling, and uncertainty quantification, emphasizing iterative assessment in open-world contexts. The survey maps datasets across biomedicine, materials science, environmental science, and social science, introducing new resources like AHTech and CSKG-600. Finally, we outline a roadmap emphasizing novelty-aware generation, multimodal-symbolic integration, human-in-the-loop systems, and ethical safeguards, positioning LLMs as agents for principled, scalable scientific discovery.

Figures

Figures reproduced from arXiv: 2505.04651 by the authors.

Figure 1
Figure 1. Overview of the scientific hypothesis generation and validation pipeline integrating LLMs, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Flow diagram of the survey structure. This figure guides the reader through the organization [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Modular pipeline for AI-driven hypothesis generation. The figure illustrates how multimodal [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Pipeline for AI-assisted hypothesis validation. The figure outlines multiple validation [PITH_FULL_IMAGE:figures/full_fig_p030_4.png]
Figure 5
Figure 5. Figure 5: Roadmap for future directions in LLM-based scientific hypothesis generation and valida [PITH_FULL_IMAGE:figures/full_fig_p043_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Style Wins, Substance Loses: A Diagnosis of LLM-as-Judge in Idea Generation

    cs.CL 2026-08 conditional novelty 6.0 of 10

    LLM judges of scientific ideas are measurably swayed by writing style; a style-detecting module reduces but does not remove the bias.

  2. Structural Enforcement of Statistical Rigor in AI-Driven Discovery: A Functional Architecture

    cs.SE 2025-11 conditional novelty 6.0 of 10

    An AI-Scientist guard architecture combining a Haskell monad for online FDR accounting with declarative scaffolding against data leakage; simulation supports it, but the advertised Lean/SPARK verification is absent fr...

  3. CMAX-CAMEL: A Coarse-to-Fine Adaptive, Memory-Efficient, and Low-Power Edge Processor for Contrast Maximization

    cs.AR 2026-05 unverdicted novelty 5.0 of 10

    CMAX-CAMEL is a runtime-adaptive, memory-centric FPGA processor for contrast maximization that reports up to 19% accuracy gain and 52% energy reduction versus fixed schedules.

Reference graph

Works this paper leans on

165 extracted references · 23 canonical work pages · cited by 3 Pith papers

  1. [1]

    Studying scientific discovery by computer simulation

    Gary F Bradshaw, Patrick W Langley, and Herbert A Simon. Studying scientific discovery by computer simulation. Science, 222 0 (4627): 0 971--975, 1983

    1983

  2. [2]

    Scientific discovery as problem solving: Reply to critics

    Herbert A Simon. Scientific discovery as problem solving: Reply to critics. 1992

    1992

  3. [3]

    A computational model of scientific insight

    Pat Langley and Randolph Jones. A computational model of scientific insight. The nature of creativity: Contemporary psychological perspectives, 177 0 (201): 0 2, 1988

    1988

  4. [4]

    The computer-aided discovery of scientific knowledge

    Pat Langley. The computer-aided discovery of scientific knowledge. In International Conference on Discovery Science, pages 25--39. Springer, 1998

    1998

  5. [5]

    The computational support of scientific discovery

    Pat Langley. The computational support of scientific discovery. International Journal of Human-Computer Studies, 53 0 (3): 0 393--410, 2000

    2000

  6. [6]

    Computational discovery of scientific knowledge

    Sa s o D z eroski, Pat Langley, and Ljup c o Todorovski. Computational discovery of scientific knowledge. In Computational discovery of scientific knowledge: Introduction, techniques, and applications in environmental and life sciences, pages 1--14. Springer

  7. [7]

    The central role of learning in cognition

    Pat Langley and Herbert A Simon. The central role of learning in cognition. In Cognitive skills and their acquisition, pages 361--380. Psychology Press, 2013

    2013

  8. [8]

    Integrated systems for computational scientific discovery

    Pat Langley. Integrated systems for computational scientific discovery. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence, pages 22598--22606, 2024

    2024

Show all 165 references
  1. [9]

    Scaling learning algorithms towards AI

    Yoshua Bengio and Yann LeCun. Scaling learning algorithms towards AI . In Large Scale Kernel Machines. MIT Press, 2007

    2007

  2. [10]

    Hinton, Simon Osindero, and Yee Whye Teh

    Geoffrey E. Hinton, Simon Osindero, and Yee Whye Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18: 0 1527--1554, 2006

    2006

  3. [11]

    Margaret A. Boden. Creativity and artificial intelligence. Artificial Intelligence, 103 0 (1): 0 347--356, 1998. ISSN 0004-3702. doi:https://doi.org/10.1016/S0004-3702(98)00055-1. URL https://www.sciencedirect.com/science/article/pii/S0004370298000551. Artificial Intelligence ...

    1998 doi

  4. [12]

    Llama 3 model card

    AI@Meta. Llama 3 model card. 2024. URL https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md

    2024

  5. [13]

    Gpt-4o system card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024

    2024 arXiv

  6. [14]

    Scibert: A pretrained language model for scientific text

    Iz Beltagy, Kyle Lo, and Arman Cohan. Scibert: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), p...

    2019

  7. [15]

    Large language models as biomedical hypothesis generators: A comprehensive evaluation

    Biqing Qi, Kaiyan Zhang, Kai Tian, Haoxiang Li, Zhang-Ren Chen, Sihang Zeng, Ermo Hua, Hu Jinfang, and Bowen Zhou. Large language models as biomedical hypothesis generators: A comprehensive evaluation. arXiv preprint arXiv:2407.08940, 2024

    2024 arXiv

  8. [16]

    Scimon: Scientific inspiration machines optimized for novelty

    Qingyun Wang, Doug Downey, Heng Ji, and Tom Hope. Scimon: Scientific inspiration machines optimized for novelty. arXiv preprint arXiv:2305.14259, 2023

    2023 arXiv

  9. [17]

    Knowledge-guided large language model for material science

    Guanjie Wang, Jingjing Hu, Jian Zhou, Sen Liu, Qingjiang Li, and Zhimei Sun. Knowledge-guided large language model for material science. Review of Materials Research, page 100007, 2025. ISSN 3050-9130. doi:https://doi.org/10.1016/j.revmat.2025.100007. URL https://www.sciencedi...

    2025

  10. [18]

    Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts

    Rui Ding, Jianguo Liu, Kang Hua, Xuebin Wang, Xiaoben Zhang, Minhua Shao, Yuxin Chen, and Junhong Chen. Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts. Science Advances, 11 0 (14): 0 eadr9038...

    2025 doi

  11. [19]

    Leveraging llms for automated analysis of biomedical data

    Rong Ji, Kai Gong, Lihong Huang, Wenxian Yang, and Rongshan Yu. Leveraging llms for automated analysis of biomedical data. In 2024 9th International Conference on Communication, Image and Signal Processing (CCISP), pages 67--71, 2024. doi:10.1109/CCISP63826.2024.10765518

    2024

  12. [20]

    Deep learning, volume 1

    Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning, volume 1. MIT Press, 2016

    2016

  13. [21]

    Large-scale validation of hypothesis generation systems via candidate ranking

    Justin Sybrandt, Micheal Shtutman, and Ilya Safro. Large-scale validation of hypothesis generation systems via candidate ranking. In 2018 IEEE International Conference on Big Data (Big Data), pages 1494--1503. IEEE, 2018

    2018

  14. [22]

    Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning

    Alireza Ghafarollahi and Markus J Buehler. Protagents: protein discovery via large language model multi-agent collaborations combining physics and machine learning. Digital Discovery, 2024 a

    2024

  15. [23]

    null, Lingyu Zhang, Krish N

    Dian-Zhao Lin, Kai-Jui Pan, Yuyin Li, Charles B. null, Lingyu Zhang, Krish N. Jayarapu, Tianchen Li, Jasmine Vy Tran, William A. Goddard, Zhengtang Luo, and Yayuan Liu. A high-throughput experimentation platform for data-driven discovery in electrochemistry. Science Advances, ...

    2025 doi

  16. [24]

    Chemcrow: Augmenting large-language models with chemistry tools

    Andres M Bran, Sam Cox, Oliver Schilter, Carlo Baldassari, Andrew D White, and Philippe Schwaller. Chemcrow: Augmenting large-language models with chemistry tools. arXiv preprint arXiv:2304.05376, 2023

    2023 arXiv

  17. [25]

    3d metamaterials

    Muamer Kadic, Graeme W Milton, Martin van Hecke, and Martin Wegener. 3d metamaterials. Nature Reviews Physics, 1 0 (3): 0 198--210, 2019

    2019

  18. [26]

    Flexible mechanical metamaterials

    Katia Bertoldi, Vincenzo Vitelli, Johan Christensen, and Martin Van Hecke. Flexible mechanical metamaterials. Nature Reviews Materials, 2 0 (11): 0 1--11, 2017

    2017

  19. [27]

    Engineering lattice metamaterials for extreme property, programmability, and multifunctionality

    Zian Jia, Fan Liu, Xihang Jiang, and Lifeng Wang. Engineering lattice metamaterials for extreme property, programmability, and multifunctionality. Journal of Applied Physics, 127 0 (15), 2020

    2020

  20. [28]

    Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

    Pengcheng Jiao and Amir H Alavi. Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends. International Materials Reviews, 66 0 (6): 0 365--393, 2021

    2021

  21. [29]

    Nanolattices: an emerging class of mechanical metamaterials

    Jens Bauer, Lucas R Meza, Tobias A Schaedler, Ruth Schwaiger, Xiaoyu Zheng, and Lorenzo Valdevit. Nanolattices: an emerging class of mechanical metamaterials. Advanced Materials, 29 0 (40): 0 1701850, 2017

    2017

  22. [30]

    Ai methods in materials design, discovery and manufacturing: A review

    Ioannis Papadimitriou, Ilias Gialampoukidis, Stefanos Vrochidis, and Ioannis Kompatsiaris. Ai methods in materials design, discovery and manufacturing: A review. Computational Materials Science, 235: 0 112793, 2024

    2024

  23. [31]

    Large language models for automated open-domain scientific hypotheses discovery

    Zonglin Yang, Xinya Du, Junxian Li, Jie Zheng, Soujanya Poria, and Erik Cambria. Large language models for automated open-domain scientific hypotheses discovery. arXiv preprint arXiv:2309.02726, 2023

    2023 arXiv

  24. [32]

    Llm-sr: Scientific equation discovery via programming with large language models

    Parshin Shojaee, Kazem Meidani, Shashank Gupta, Amir Barati Farimani, and Chandan K Reddy. Llm-sr: Scientific equation discovery via programming with large language models. arXiv preprint arXiv:2404.18400, 2024

    2024 arXiv

  25. [33]

    Mathematical discoveries from program search with large language models

    Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Matej Balog, M Pawan Kumar, Emilien Dupont, Francisco JR Ruiz, Jordan S Ellenberg, Pengming Wang, Omar Fawzi, et al. Mathematical discoveries from program search with large language models. Nature, 625 0 (7...

    2024

  26. [34]

    Solving olympiad geometry without human demonstrations

    Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. Solving olympiad geometry without human demonstrations. Nature, 625 0 (7995): 0 476--482, 2024

    2024

  27. [35]

    Harnessing the power of artificial intelligence to transform hearing healthcare and research

    Nicholas A Lesica, Nishchay Mehta, Joseph G Manjaly, Li Deng, Blake S Wilson, and Fan-Gang Zeng. Harnessing the power of artificial intelligence to transform hearing healthcare and research. Nature Machine Intelligence, 3 0 (10): 0 840--849, 2021

    2021

  28. [36]

    The ai scientist: Towards fully automated open-ended scientific discovery

    Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292, 2024

    2024 arXiv

  29. [37]

    Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning

    Alireza Ghafarollahi and Markus J Buehler. Sciagents: Automating scientific discovery through multi-agent intelligent graph reasoning. arXiv preprint arXiv:2409.05556, 2024 b

    2024 arXiv

  30. [38]

    Chemist-x: Large language model-empowered agent for reaction condition recommendation in chemical synthesis

    K Chen et al. Chemist-x: Large language model-empowered agent for reaction condition recommendation in chemical synthesis. Preprint at, 2024

    2024

  31. [39]

    Hypothesis generation with large language models, 2024

    Yangqiaoyu Zhou, Haokun Liu, Tejes Srivastava, Hongyuan Mei, and Chenhao Tan. Hypothesis generation with large language models, 2024. URL https://arxiv.org/abs/2404.04326

    2024 arXiv

  32. [40]

    Moliere: Automatic biomedical hypothesis generation system

    Justin Sybrandt, Michael Shtutman, and Ilya Safro. Moliere: Automatic biomedical hypothesis generation system. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1633--1642, 2017

    2017

  33. [41]

    Hypothesis generation from text based on co-evolution of biomedical concepts

    Kishlay Jha, Guangxu Xun, Yaqing Wang, and Aidong Zhang. Hypothesis generation from text based on co-evolution of biomedical concepts. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 843--851, 2019

    2019

  34. [42]

    Discovering faster matrix multiplication algorithms with reinforcement learning

    Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J R Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, et al. Discovering faster matrix multiplication algorithms with reinforcement learning. N...

    2022

  35. [43]

    Llama: Open and efficient foundation language models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth \'e e Lacroix, Baptiste Rozi \`e re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  36. [44]

    The art of SOCRATIC QUESTIONING : Recursive thinking with large language models

    Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang, and Lifu Huang. The art of SOCRATIC QUESTIONING : Recursive thinking with large language models. In Houda Bouamor, Juan Pino, and Kalika Bali, editors, Proceedings of the 2023 Conference on Empirical Methods ...

    2023 doi

  37. [45]

    Highly accurate protein structure prediction with alphafold

    John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Z \' dek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. nature, 596 0 (7873): 0 583--589, 2021

    2021

  38. [46]

    Crispr-gpt: An llm agent for automated design of gene-editing experiments

    Kaixuan Huang, Yuanhao Qu, Henry Cousins, William A Johnson, Di Yin, Mihir Shah, Denny Zhou, Russ Altman, Mengdi Wang, and Le Cong. Crispr-gpt: An llm agent for automated design of gene-editing experiments. arXiv preprint arXiv:2404.18021, 2024

    2024 arXiv

  39. [47]

    The materials project: Accelerating materials design through theory-driven data and tools

    Anubhav Jain, Joseph Montoya, Shyam Dwaraknath, Nils ER Zimmermann, John Dagdelen, Matthew Horton, Patrick Huck, Donny Winston, Shreyas Cholia, Shyue Ping Ong, et al. The materials project: Accelerating materials design through theory-driven data and tools. Handbook of Materia...

    2020

  40. [48]

    Discovering drug--drug interactions: a text-mining and reasoning approach based on properties of drug metabolism

    Luis Tari, Saadat Anwar, Shanshan Liang, James Cai, and Chitta Baral. Discovering drug--drug interactions: a text-mining and reasoning approach based on properties of drug metabolism. Bioinformatics, 26 0 (18): 0 i547--i553, 2010

    2010

  41. [49]

    In search of verifiability: Explanations rarely enable complementary performance in ai-advised decision making

    Raymond Fok and Daniel S Weld. In search of verifiability: Explanations rarely enable complementary performance in ai-advised decision making. AI Magazine, 45 0 (3): 0 317--332, 2024

    2024

  42. [50]

    Agentpeertalk: Empowering students through agentic-ai-driven discernment of bullying and joking in peer interactions in schools, 2024

    Aditya Paul, Chi Lok Yu, Eva Adelina Susanto, Nicholas Wai Long Lau, and Gwenyth Isobel Meadows. Agentpeertalk: Empowering students through agentic-ai-driven discernment of bullying and joking in peer interactions in schools, 2024. URL https://arxiv.org/abs/2408.01459

    2024 arXiv

  43. [51]

    Building living software systems with generative & agentic ai, 2024

    Jules White. Building living software systems with generative & agentic ai, 2024. URL https://arxiv.org/abs/2408.01768

    2024 arXiv

  44. [52]

    Harms from increasingly agentic algorithmic systems

    Alan Chan, Rebecca Salganik, Alva Markelius, Chris Pang, Nitarshan Rajkumar, Dmitrii Krasheninnikov, Lauro Langosco, Zhonghao He, Yawen Duan, Micah Carroll, Michelle Lin, Alex Mayhew, Katherine Collins, Maryam Molamohammadi, John Burden, Wanru Zhao, Shalaleh Rismani, Konstanti...

    2023

  45. [53]

    Antonin Sulc, Thorsten Hellert, Raimund Kammering, Hayden Houscher, and Jason St. John. Towards agentic ai on particle accelerators, 2024. URL https://arxiv.org/abs/2409.06336

    2024 arXiv

  46. [54]

    Interactive agents: Simulating counselor-client psychological counseling via role-playing llm-to-llm interactions

    Huachuan Qiu and Zhenzhong Lan. Interactive agents: Simulating counselor-client psychological counseling via role-playing llm-to-llm interactions. arXiv preprint arXiv:2408.15787, 2024

    2024 arXiv

  47. [55]

    Agentic ai for scientific discovery: A survey of progress, challenges, and future directions

    Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes, and Christina Mack. Agentic ai for scientific discovery: A survey of progress, challenges, and future directions. arXiv preprint arXiv:2503.08979, 2025

    2025 arXiv

  48. [56]

    Towards an ai co-scientist

    Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, et al. Towards an ai co-scientist. arXiv preprint arXiv:2502.18864, 2025

    2025 arXiv

  49. [57]

    Fine-tuned language models generate stable inorganic materials as text

    Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C Lawrence Zitnick, and Zachary Ulissi. Fine-tuned language models generate stable inorganic materials as text. arXiv preprint arXiv:2402.04379, 2024

    2024 arXiv

  50. [58]

    Prioritizing safeguarding over autonomy: Risks of llm agents for science

    Xiangru Tang, Qiao Jin, Kunlun Zhu, Tongxin Yuan, Yichi Zhang, Wangchunshu Zhou, Meng Qu, Yilun Zhao, Jian Tang, Zhuosheng Zhang, et al. Prioritizing safeguarding over autonomy: Risks of llm agents for science. arXiv preprint arXiv:2402.04247, 2024

    2024 arXiv

  51. [59]

    Practices for governing agentic ai systems

    Yonadav Shavit, Sandhini Agarwal, Miles Brundage, Steven Adler, Cullen O’Keefe, Rosie Campbell, Teddy Lee, Pamela Mishkin, Tyna Eloundou, Alan Hickey, et al. Practices for governing agentic ai systems. Research Paper, OpenAI, December, 2023

    2023

  52. [60]

    A comprehensive survey of scientific large language models and their applications in scientific discovery

    Yu Zhang, Xiusi Chen, Bowen Jin, Sheng Wang, Shuiwang Ji, Wei Wang, and Jiawei Han. A comprehensive survey of scientific large language models and their applications in scientific discovery. arXiv preprint arXiv:2406.10833, 2024 a

    2024 arXiv

  53. [61]

    Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S

    Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jare...

    2021

  54. [62]

    Velma: Verbalization embodiment of llm agents for vision and language navigation in street view

    Raphael Schumann, Wanrong Zhu, Weixi Feng, Tsu-Jui Fu, Stefan Riezler, and William Yang Wang. Velma: Verbalization embodiment of llm agents for vision and language navigation in street view. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 189...

    2024

  55. [63]

    Virtualplant: a software platform to support systems biology research

    Manpreet S Katari, Steve D Nowicki, Felipe F Aceituno, Damion Nero, Jonathan Kelfer, Lee Parnell Thompson, Juan M Cabello, Rebecca S Davidson, Arthur P Goldberg, Dennis E Shasha, et al. Virtualplant: a software platform to support systems biology research. Plant physiology, 15...

    2010

  56. [64]

    An llm-based knowledge synthesis and scientific reasoning framework for biomedical discovery

    Oskar Wysocki, Magdalena Wysocka, Danilo Carvalho, Alex Teodor Bogatu, Danilo Miranda Gusicuma, Maxime Delmas, Harriet Unsworth, and Andre Freitas. An llm-based knowledge synthesis and scientific reasoning framework for biomedical discovery. arXiv preprint arXiv:2406.18626, 2024

    2024 arXiv

  57. [65]

    The role of ai in drug discovery: challenges, opportunities, and strategies

    Alexandre Blanco-Gonzalez, Alfonso Cabezon, Alejandro Seco-Gonzalez, Daniel Conde-Torres, Paula Antelo-Riveiro, Angel Pineiro, and Rebeca Garcia-Fandino. The role of ai in drug discovery: challenges, opportunities, and strategies. Pharmaceuticals, 16 0 (6): 0 891, 2023

    2023

  58. [66]

    Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique

    Ilya Tyagin and Ilya Safro. Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique. BMC bioinformatics, 25, 2024

    2024

  59. [67]

    Lab-bench: Measuring capabilities of language models for biology research

    Jon M Laurent, Joseph D Janizek, Michael Ruzo, Michaela M Hinks, Michael J Hammerling, Siddharth Narayanan, Manvitha Ponnapati, Andrew D White, and Samuel G Rodriques. Lab-bench: Measuring capabilities of language models for biology research. arXiv preprint arXiv:2407.10362, 2024

    2024 arXiv

  60. [68]

    Agentclinic: a multimodal agent benchmark to evaluate ai in simulated clinical environments

    Samuel Schmidgall, Rojin Ziaei, Carl Harris, Eduardo Reis, Jeffrey Jopling, and Michael Moor. Agentclinic: a multimodal agent benchmark to evaluate ai in simulated clinical environments. arXiv preprint arXiv:2405.07960, 2024

    2024 arXiv

  61. [69]

    Research hypothesis generation using link prediction in a bipartite graph

    Jung-Hun Kim and Aviv Segev. Research hypothesis generation using link prediction in a bipartite graph. In 2018 IEEE International Conference on Big Data (Big Data), pages 2863--2867. IEEE, 2018

    2018

  62. [70]

    Sciagent: Tool-augmented language models for scientific reasoning

    Yubo Ma, Zhibin Gou, Junheng Hao, Ruochen Xu, Shuohang Wang, Liangming Pan, Yujiu Yang, Yixin Cao, Aixin Sun, Hany Awadalla, et al. Sciagent: Tool-augmented language models for scientific reasoning. arXiv preprint arXiv:2402.11451, 2024

    2024 arXiv

  63. [71]

    Llms as research tools: Applications and evaluations in hci data work

    Marianne Aubin Le Qu \'e r \'e , Hope Schroeder, Casey Randazzo, Jie Gao, Ziv Epstein, Simon Tangi Perrault, David Mimno, Louise Barkhuus, and Hanlin Li. Llms as research tools: Applications and evaluations in hci data work. In Extended Abstracts of the CHI Conference on Human...

    2024

  64. [72]

    PubMed : A resource for biomedical literature, 2025

    National Center for Biotechnology Information . PubMed : A resource for biomedical literature, 2025. URL https://pubmed.ncbi.nlm.nih.gov/

    2025

  65. [73]

    Gene ontology: tool for the unification of biology

    Michael Ashburner, Catherine A Ball, Judith A Blake, David Botstein, Heather Butler, J Michael Cherry, Allan P Davis, Kara Dolinski, Selina S Dwight, Janan T Eppig, et al. Gene ontology: tool for the unification of biology. Nature genetics, 25 0 (1): 0 25--29, 2000

    2000

  66. [74]

    Uk biobank: a globally important resource for cancer research

    Megan C Conroy, Ben Lacey, Jelena Be s evi \'c , Wemimo Omiyale, Qi Feng, Mark Effingham, Jonathan Sellers, Simon Sheard, Mahesh Pancholi, Gareth Gregory, et al. Uk biobank: a globally important resource for cancer research. British Journal of Cancer, 128 0 (4): 0 519--527, 2023

    2023

  67. [75]

    Chembench: a cheminformatics workbench

    Theo Walker, Christopher M Grulke, Diane Pozefsky, and Alexander Tropsha. Chembench: a cheminformatics workbench. Bioinformatics, 26 0 (23): 0 3000--3001, 2010

    2010

  68. [76]

    Ethical and social risks of harm from language models, 2021

    Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, Zac Kenton, Sasha Brown, Will Hawkins, Tom Stepleton, Courtney Biles, Abeba Birhane, Julia Haas, Laura Rimell, Lisa Anne Hendricks...

    2021

  69. [77]

    A comprehensive survey on contrastive learning

    Haigen Hu, Xiaoyuan Wang, Yan Zhang, Qi Chen, and Qiu Guan. A comprehensive survey on contrastive learning. Neurocomputing, page 128645, 2024

    2024

  70. [78]

    Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases

    Jiawei Han and Yongjian Fu. Dynamic generation and refinement of concept hierarchies for knowledge discovery in databases. In KDD workshop, pages 157--168, 1994

    1994

  71. [79]

    Llms as research tools: Applications and evaluations in hci data work

    Marianne Aubin Le Qu\' e r\' e , Hope Schroeder, Casey Randazzo, Jie Gao, Ziv Epstein, Simon Tangi Perrault, David Mimno, Louise Barkhuus, and Hanlin Li. Llms as research tools: Applications and evaluations in hci data work. In Extended Abstracts of the 2024 CHI Conference on ...

    2024

  72. [80]

    Rise of the robo scientists

    Ross D King. Rise of the robo scientists. Scientific American, 304 0 (1): 0 72--77, 2011

    2011

  73. [81]

    Archemist: Autonomous robotic chemistry system architecture

    Hatem Fakhruldeen, Gabriella Pizzuto, Jakub Glowacki, and Andrew Ian Cooper. Archemist: Autonomous robotic chemistry system architecture. In 2022 International Conference on Robotics and Automation (ICRA), pages 6013--6019. IEEE, 2022

    2022

  74. [82]

    Eve: Integration of machine learning with compound testing in a robot scientist

    Kevin Williams, Elizabeth Bilsland, Andrew Sparkes, Wayne Aubrey, Michael Young, Larisa N Soldatova, Kurt De Grave, Jan Ramon, Michaela De Clare, Worachart Sirawaraporn, et al. Eve: Integration of machine learning with compound testing in a robot scientist. In Antenna Live: Ro...

    2015

  75. [83]

    An audit on the perspectives and challenges of hallucinations in nlp

    Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, and Shomir Wilson. An audit on the perspectives and challenges of hallucinations in nlp. arXiv preprint arXiv:2404.07461, 2024

    2024 arXiv

  76. [84]

    Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge

    Mohamad Yaser Jaradeh, Allard Oelen, Kheir Eddine Farfar, Manuel Prinz, Jennifer D'Souza, G \'a bor Kismih \'o k, Markus Stocker, and S \"o ren Auer. Open research knowledge graph: next generation infrastructure for semantic scholarly knowledge. In Proceedings of the 10th inte...

    2019

  77. [85]

    model cards for model reporting

    DeBrae Kennedy-Mayo and Jake Gord. “model cards for model reporting” in 2024: Reclassifying category of ethical considerations in terms of trustworthiness and risk management. In Future of Information and Communication Conference, pages 179--196. Springer, 2025

    2024

  78. [86]

    Using model cards for ethical reflection on machine learning models: an interview-based study

    Jos \'e Luiz Nunes, Gabriel DJ Barbosa, Clarisse Sieckenius de Souza, and Simone DJ Barbosa. Using model cards for ethical reflection on machine learning models: an interview-based study. Journal on Interactive Systems, 15 0 (1): 0 1--19, 2024

    2024

  79. [87]

    Propositions about novelty

    Ulrich Witt. Propositions about novelty. Journal of Economic Behavior & Organization, 70 0 (1-2): 0 311--320, 2009

    2009

  80. [88]

    Scientific novelty beyond the experiment

    John E Hallsworth, Zulema Udaondo, Carlos Pedr \'o s-Ali \'o , Juan H \"o fer, Kathleen C Benison, Karen G Lloyd, Radam \'e s JB Cordero, Claudia BL de Campos, Michail M Yakimov, and Ricardo Amils. Scientific novelty beyond the experiment. Microbial Biotechnology, 16 0 (6): 0 ...

    2023

  81. [89]

    Measuring novelty in science with word embedding

    Sotaro Shibayama, Yutaro Baba, and John P Walsh. Measuring novelty in science with word embedding. PloS one, 16 0 (7): 0 e0254034, 2021. doi:10.1371/journal.pone.0254034

    2021 doi

  82. [90]

    Enhancing contrastive learning with efficient combinatorial positive pairing, 2024

    Jaeill Kim, Duhun Hwang, Eunjung Lee, Jangwon Suh, Jimyeong Kim, and Wonjong Rhee. Enhancing contrastive learning with efficient combinatorial positive pairing, 2024. URL https://arxiv.org/abs/2401.05730

    2024 arXiv

  83. [91]

    How feasible is automated discovery? IEEE Intelligent Systems, 2 0 (01): 0 69--82, 1987

    Michael G Walker. How feasible is automated discovery? IEEE Intelligent Systems, 2 0 (01): 0 69--82, 1987

    1987

  84. [92]

    Step feasibility-aware and error-correctable entailment tree generation

    Junyue Song, Xin Wu, and Yi Cai. Step feasibility-aware and error-correctable entailment tree generation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15296--15308, 2024

    2024

  85. [93]

    A survey for large language models in biomedicine

    Chong Wang, Mengyao Li, Junjun He, Zhongruo Wang, Erfan Darzi, Zan Chen, Jin Ye, Tianbin Li, Yanzhou Su, Jing Ke, et al. A survey for large language models in biomedicine. arXiv preprint arXiv:2409.00133, 2024 a

    2024 arXiv

  86. [94]

    National Library of Medicine

    U.S. National Library of Medicine . Medical Subject Headings (MeSH) , 2025. URL https://www.nlm.nih.gov/mesh/meshhome.html

    2025

  87. [95]

    The chembl database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods

    Barbara Zdrazil, Eloy Felix, Fiona Hunter, Emma J Manners, James Blackshaw, Sybilla Corbett, Marleen de Veij, Harris Ioannidis, David Mendez Lopez, Juan F Mosquera, Maria Paula Magarinos, Nicolas Bosc, Ricardo Arcila, Tevfik Kizilören, Anna Gaulton, A Patrícia Bento, Melissa F...

    2023

  88. [96]

    Genia corpus—a semantically annotated corpus for bio-textmining

    J-D Kim, Tomoko Ohta, Yuka Tateisi, and Jun’ichi Tsujii. Genia corpus—a semantically annotated corpus for bio-textmining. Bioinformatics, 19 0 (suppl\_1): 0 i180--i182, 2003

    2003

  89. [97]

    Open graph benchmark: Datasets for machine learning on graphs

    Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33: 0 22118--22133, 2020

    2020

  90. [98]

    Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm

    Alexander Dunn, Qi Wang, Alex Ganose, Daniel Dopp, and Anubhav Jain. Benchmarking materials property prediction methods: the matbench test set and automatminer reference algorithm. npj Computational Materials, 6 0 (1): 0 138, 2020

    2020

  91. [99]

    Climatenet: An expert-labelled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather

    Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, et al. Climatenet: An expert-labelled open dataset and deep learning architecture for enabling high-precision analys...

    2020

  92. [100]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll \'a r, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, procee...

    2014

  93. [101]

    Research hypothesis generation over scientific knowledge graphs

    Agustín Borrego, Danilo Dessì, Daniel Ayala, Inma Hernández, Francesco Osborne, Diego Reforgiato Recupero , Davide Buscaldi, David Ruiz, and Enrico Motta. Research hypothesis generation over scientific knowledge graphs. Knowledge-Based Systems, 315: 0 113280, 2025. ISSN 0950-7...

    2025

  94. [102]

    Drugbank 5.0: a major update to the drugbank database for 2018

    David S Wishart, Yannick D Feunang, An C Guo, Elvis J Lo, Ana Marcu, Jason R Grant, Tanvir Sajed, Daniel Johnson, Carin Li, Zinat Sayeeda, et al. Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic acids research, 46 0 (D1): 0 D1074--D1082, 2018

    2018

  95. [103]

    Think you have solved question answering? try arc, the ai2 reasoning challenge

    Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018

    2018 arXiv

  96. [104]

    Commentary: The materials project: A materials genome approach to accelerating materials innovation

    Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL materi...

    2013

  97. [105]

    Kegg: kyoto encyclopedia of genes and genomes

    Minoru Kanehisa and Susumu Goto. Kegg: kyoto encyclopedia of genes and genomes. Nucleic acids research, 28 0 (1): 0 27--30, 2000

    2000

  98. [106]

    American community survey, 2025

    United States Census Bureau. American community survey, 2025. URL https://www.census.gov/programs-surveys/acs

    2025

  99. [107]

    United states patent and trademark office patent database, 2025

    United States Patent and Trademark Office. United states patent and trademark office patent database, 2025. URL https://www.uspto.gov/patents

    2025

  100. [108]

    Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization

    Shashi Narayan, Shay B Cohen, and Mirella Lapata. Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. arXiv preprint arXiv:1808.08745, 2018

    2018 arXiv

  101. [109]

    The cosmic (catalogue of somatic mutations in cancer) database and website

    Sally Bamford, Emily Dawson, Simon Forbes, Jody Clements, Roger Pettett, Ahmet Dogan, A Flanagan, Jon Teague, P Andrew Futreal, Michael R Stratton, et al. The cosmic (catalogue of somatic mutations in cancer) database and website. British journal of cancer, 91 0 (2): 0 355--358, 2004

    2004

  102. [110]

    Dspy-based neural-symbolic pipeline to enhance spatial reasoning in llms, 2024 b

    Rong Wang, Kun Sun, and Jonas Kuhn. Dspy-based neural-symbolic pipeline to enhance spatial reasoning in llms, 2024 b . URL https://arxiv.org/abs/2411.18564

    2024 arXiv

  103. [111]

    Gpt-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023

    2023 arXiv

  104. [112]

    A unified approach to interpreting model predictions

    Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. Advances in neural information processing systems, 30, 2017

    2017

  105. [113]

    Model-agnostic interpretability of machine learning

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386, 2016

    2016 arXiv

  106. [114]

    Towards scientific intelligence: A survey of llm-based scientific agents, 2025

    Shuo Ren, Pu Jian, Zhenjiang Ren, Chunlin Leng, Can Xie, and Jiajun Zhang. Towards scientific intelligence: A survey of llm-based scientific agents, 2025. URL https://arxiv.org/abs/2503.24047

    2025

  107. [115]

    Learning advance: Robotics-llm guided hypotheses generation for the discovery of chemical knowledge

    TianZhixi Yin, Ruozhu Feng, Jie Bao, Peiyuan Gao, Yangang Liang, Job Heather, Alan Aspuru-Guzik, and Wei Wang. Learning advance: Robotics-llm guided hypotheses generation for the discovery of chemical knowledge. ChemRxiv, 2025. doi:10.26434/chemrxiv-2025-n1b4l

    2025 doi

  108. [116]

    Skogaker, Anne Aarvik, Magnus K

    Mathilde Resell, Elisabeth Pimpisa Graarud, Hanne-Line Rabben, Animesh Sharma, Lars Hagen, Linh Hoang, Nan T. Skogaker, Anne Aarvik, Magnus K. Svensson, Manoj Amrutkar, Caroline S. Verbeke, Surinder K. Batra, Gunnar Qvigstad, Timothy C. Wang, Anil Rustgi, Duan Chen, and Chun-M...

    2025 doi

  109. [117]

    Ai for science: A comprehensive review on innovations, challenges, and future directions

    Zhenyu Yu. Ai for science: A comprehensive review on innovations, challenges, and future directions. International Journal of Artificial Intelligence for Science (IJAI4S), 1 0 (1), 2025

    2025

  110. [118]

    Towards safety aware ai agents

    Thomas Steinecker, Thorsten Luettel, and Mirko Maehlisch. Towards safety aware ai agents. https://www.researchgate.net/publication/389351017_Towards_Safety_Aware_AI_Agents, 2025. Preprint on ResearchGate, accessed April 3, 2025

    2025

  111. [119]

    Combining knowledge graphs and large language models

    Amanda Kau, Xuzeng He, Aishwarya Nambissan, Aland Astudillo, Hui Yin, and Amir Aryani. Combining knowledge graphs and large language models. arXiv preprint arXiv:2407.06564, 2024

    2024 arXiv

  112. [120]

    Taylor, et al

    Jie Bai, Sebastian Mosbach, Christopher J. Taylor, et al. A dynamic knowledge graph approach to distributed self-driving laboratories. Nature Communications, 15 0 (1): 0 462, 2024 a . doi:10.1038/s41467-023-44599-9. URL https://doi.org/10.1038/s41467-023-44599-9

    2024 doi

  113. [121]

    Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendation

    Yu Zhang, Zhiyong Cheng, Fan Liu, Xun Yang, and Yuxin Peng. Decoupled domain-specific and domain-conditional representation learning for cross-domain recommendation. Information Processing & Management, 61 0 (3): 0 103689, 2024 b

    2024

  114. [122]

    Advancing abductive reasoning in knowledge graphs through complex logical hypothesis generation

    Jiaxin Bai, Yicheng Wang, Tianshi Zheng, Yue Guo, Xin Liu, and Yangqiu Song. Advancing abductive reasoning in knowledge graphs through complex logical hypothesis generation. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: L...

    2024

  115. [123]

    Ontology-based context representation and reasoning using owl and swrl

    C-H Liu, K-L Chang, Jason J-Y Chen, and S-C Hung. Ontology-based context representation and reasoning using owl and swrl. In 2010 8th Annual Communication Networks and Services Research Conference, pages 215--220. IEEE, 2010

    2010

  116. [124]

    Multiomics, artificial intelligence, and precision medicine in perinatology

    Mohan Pammi, Nima Aghaeepour, and Josef Neu. Multiomics, artificial intelligence, and precision medicine in perinatology. Pediatric Research, 93: 0 308--315, 2023. doi:10.1038/s41390-022-02181-x. URL https://doi.org/10.1038/s41390-022-02181-x

    2023 doi

  117. [125]

    Linkclimate: An interoperable knowledge graph platform for climate data

    Jiantao Wu, Fabrizio Orlandi, Declan O’Sullivan, and Soumyabrata Dev. Linkclimate: An interoperable knowledge graph platform for climate data. Computers & Geosciences, 169: 0 105215, 2022

    2022

  118. [126]

    Leveraging ai-driven hypothesis generation for niche and obscure fields: Applications in musicology

    Paul Pajo. Leveraging ai-driven hypothesis generation for niche and obscure fields: Applications in musicology. https://www.researchgate.net/publication/390066146_Leveraging_AI-Driven_Hypothesis_Generation_for_Niche_and_Obscure_Fields_Applications_in_Musicology, March 2025. Pr...

    2025

  119. [127]

    Simulation intelligence: Towards a new generation of scientific methods

    Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, At l m G \"u ne s Baydin, et al. Simulation intelligence: Towards a new generation of scientific methods. arXiv preprint arXiv:2112.03...

    2021 arXiv

  120. [128]

    Biplex: Creative problem-solving by planning for experimentation

    Vasanth Sarathy and Matthias Scheutz. Biplex: Creative problem-solving by planning for experimentation. In International Conference on Computational Creativity, 2022

    2022

  121. [129]

    Simulating a computational biological model, rather than reading, elicits changes in brain activity during biological reasoning

    Caron AC Clark, Tom \'a s Helikar, and Joseph Dauer. Simulating a computational biological model, rather than reading, elicits changes in brain activity during biological reasoning. CBE—Life Sciences Education, 19 0 (3): 0 ar45, 2020

    2020

  122. [130]

    Knowledge integration in creative problem solving

    S \'e bastien H \'e lie and Ron Sun. Knowledge integration in creative problem solving. In Proceedings of the 30th Annual Meeting of the Cognitive Science Society, pages 1681--1686. Austin, TX: Cognitive Science Society, 2008

    2008

  123. [131]

    An introduction to neural networks for the social sciences

    Gechun Lin and Christopher Lucas. An introduction to neural networks for the social sciences. 2023

    2023

  124. [132]

    Ideaflow: the only business metric that matters

    Jeremy Utley and Perry Klebahn. Ideaflow: the only business metric that matters. Penguin, 2022

    2022

  125. [133]

    Learning particle physics by example: location-aware generative adversarial networks for physics synthesis

    Luke de Oliveira, Michela Paganini, and Benjamin Nachman. Learning particle physics by example: location-aware generative adversarial networks for physics synthesis. Computing and Software for Big Science, 1 0 (1): 0 4, 2017

    2017

  126. [134]

    Alcm: Autonomous llm-augmented causal discovery framework

    Elahe Khatibi, Mahyar Abbasian, Zhongqi Yang, Iman Azimi, and Amir M Rahmani. Alcm: Autonomous llm-augmented causal discovery framework. arXiv preprint arXiv:2405.01744, 2024

    2024 arXiv

  127. [135]

    Elements of causal inference: foundations and learning algorithms

    Jonas Peters, Dominik Janzing, and Bernhard Sch \"o lkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017

    2017

  128. [136]

    Biomedical applications of bayesian networks

    Peter JF Lucas. Biomedical applications of bayesian networks. Advances in probabilistic graphical models, pages 333--358, 2007

    2007

  129. [137]

    Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000

    Leland Gerson Neuberg. Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000. Econometric Theory, 19 0 (4): 0 675--685, 2003

    2000

  130. [138]

    A biomedical knowledge graph system to propose mechanistic hypotheses for real-world environmental health observations: cohort study and informatics application

    Karamarie Fecho, Chris Bizon, Frederick Miller, Shepherd Schurman, Charles Schmitt, William Xue, Kenneth Morton, Patrick Wang, Alexander Tropsha, et al. A biomedical knowledge graph system to propose mechanistic hypotheses for real-world environmental health observations: coho...

    2021

  131. [139]

    Scigraph: A knowledge graph constructed by function and topic annotation of scientific papers

    Yuchen Yan and Chong Chen. Scigraph: A knowledge graph constructed by function and topic annotation of scientific papers

  132. [140]

    Multiscale cross-domain thermochemical knowledge-graph

    Sebastian Mosbach, Angiras Menon, Feroz Farazi, Nenad Krdzavac, Xiaochi Zhou, Jethro Akroyd, and Markus Kraft. Multiscale cross-domain thermochemical knowledge-graph. Journal of Chemical Information and Modeling, 60 0 (12): 0 6155--6166, 2020

    2020

  133. [141]

    Temporal graph networks for deep learning on dynamic graphs

    Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637, 2020

    2006 arXiv

  134. [142]

    Forecasting high-impact research topics via machine learning on evolving knowledge graphs, 2025

    Xuemei Gu and Mario Krenn. Forecasting high-impact research topics via machine learning on evolving knowledge graphs, 2025. URL https://arxiv.org/abs/2402.08640

    2025 arXiv

  135. [143]

    Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation

    Haoyang Su, Renqi Chen, Shixiang Tang, Xinzhe Zheng, Jingzhe Li, Zhenfei Yin, Wanli Ouyang, and Nanqing Dong. Two heads are better than one: A multi-agent system has the potential to improve scientific idea generation. arXiv preprint arXiv:2410.09403, 2024

    arXiv

  136. [144]

    Researchagent: Iterative research idea generation over scientific literature with large language models

    Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang. Researchagent: Iterative research idea generation over scientific literature with large language models. arXiv preprint arXiv:2404.07738, 2024

    2024 arXiv

  137. [145]

    An unconventional iap-binding motif revealed by target-assisted iterative screening (tais) of the bir3-ciap1 domain

    Alexei Kurakin and Dale E Bredesen. An unconventional iap-binding motif revealed by target-assisted iterative screening (tais) of the bir3-ciap1 domain. Journal of Molecular Recognition: An Interdisciplinary Journal, 20 0 (1): 0 39--50, 2007

    2007

  138. [146]

    Leveraging chemistry foundation models to facilitate structure focused retrieval augmented generation in multi-agent workflows for catalyst and materials design

    Nathaniel H Park, Tiffany J Callahan, James L Hedrick, Tim Erdmann, and Sara Capponi. Leveraging chemistry foundation models to facilitate structure focused retrieval augmented generation in multi-agent workflows for catalyst and materials design. arXiv preprint arXiv:2408.11793, 2024

    2024 arXiv

  139. [147]

    Toward a team of ai-made scientists for scientific discovery from gene expression data

    Haoyang Liu, Yijiang Li, Jinglin Jian, Yuxuan Cheng, Jianrong Lu, Shuyi Guo, Jinglei Zhu, Mianchen Zhang, Miantong Zhang, and Haohan Wang. Toward a team of ai-made scientists for scientific discovery from gene expression data. arXiv preprint arXiv:2402.12391, 2024

    2024 arXiv

  140. [148]

    Labkey server, 2025

    LabKey . Labkey server, 2025. URL https://www.labkey.org/

    2025

  141. [149]

    Simulink: Simulation and Model-Based Design

    MathWorks . Simulink: Simulation and Model-Based Design. The MathWorks, Inc., Natick, MA, 2025. URL https://www.mathworks.com/products/simulink.html

    2025

  142. [150]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. scikit-learn: Machine learning in python, 2011. URL https://scikit-learn.org/

    2011

  143. [151]

    Forecasting at scale

    Sean J Taylor and Benjamin Letham. Forecasting at scale. The American Statistician, 72 0 (1): 0 37--45, 2018

    2018

  144. [152]

    Tensorflow serving, 2025

    TensorFlow . Tensorflow serving, 2025. URL https://www.tensorflow.org/tfx/guide/serving

    2025

  145. [153]

    Ibm watson, 2025

    IBM . Ibm watson, 2025. URL https://www.ibm.com/watson

    2025

  146. [154]

    Zooniverse: People-powered research, 2025

    The Zooniverse Team . Zooniverse: People-powered research, 2025. URL https://www.zooniverse.org/

    2025

  147. [155]

    Project jupyter: Jupyter notebooks, 2025

    Project Jupyter . Project jupyter: Jupyter notebooks, 2025. URL https://jupyter.org/

    2025

  148. [156]

    Tetrad: Causal discovery software, 2025

    Center for Causal Discovery . Tetrad: Causal discovery software, 2025. URL https://www.ccd.pitt.edu/tools/tetrad/

    2025

  149. [157]

    Mlperf: Fair and useful benchmarks for machine learning, 2025

    MLPerf . Mlperf: Fair and useful benchmarks for machine learning, 2025. URL https://mlperf.org/

    2025

  150. [158]

    A meta-transfer objective for learning to disentangle causal mechanisms

    Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, S \'e bastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, and Christopher Pal. A meta-transfer objective for learning to disentangle causal mechanisms. arXiv preprint arXiv:1901.10912, 2019

    1901 arXiv

  151. [159]

    A comprehensive survey on graph neural networks

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32 0 (1): 0 4--24, 2020

    2020

  152. [160]

    Causal inference in statistics: An overview

    Judea Pearl. Causal inference in statistics: An overview. 2009

    2009

  153. [161]

    The central role of the propensity score in observational studies for causal effects

    Paul R Rosenbaum and Donald B Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70 0 (1): 0 41--55, 1983

    1983

  154. [162]

    Identification of causal effects using instrumental variables

    Joshua D Angrist, Guido W Imbens, and Donald B Rubin. Identification of causal effects using instrumental variables. Journal of the American statistical Association, 91 0 (434): 0 444--455, 1996

    1996

  155. [163]

    Man is to computer programmer as woman is to homemaker? debiasing word embeddings

    Tolga Bolukbasi, Kai-Wei Chang, James Y Zou, Venkatesh Saligrama, and Adam T Kalai. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems, 29, 2016

    2016

  156. [164]

    Towards debiasing sentence representations

    Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, and Louis-Philippe Morency. Towards debiasing sentence representations. arXiv preprint arXiv:2007.08100, 2020

    2007 arXiv

  157. [165]

    Bold: Dataset and metrics for measuring biases in open-ended language generation

    Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. Bold: Dataset and metrics for measuring biases in open-ended language generation. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparen...

    2021

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.