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REVIEW 3 major objections 6 minor 111 references

Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This review argues that medical LLMs should be judged by how they reason, not just what they answer, and proposes two frameworks to expose that reasoning.

desk verdict Useful medical-LLM reasoning primer with a sensible evaluation taxonomy, but the survey's 'striking lack' claim and the trace-fidelity premise of its proposed frameworks need serious repair before the transparency claims hold. read the letter →

arxiv 2412.15748 v2 pith:WCYRXI6P submitted 2024-12-20 cs.CL cs.AIcs.LG

Shamus Sim , Tyrone Chen This is my paper · ORCID
classification cs.CLcs.AIcs.LG
keywords largelanguagemodelsmedicalartificialintelligencereasoningbehaviourexplainableAIchain-of-thoughtclinicalsymbolicevaluation
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 review argues that the medical AI field has neglected the reasoning behaviour of large language models, focusing on benchmark accuracy while leaving the models' internal logic invisible. It adapts a definition of reasoning behaviour from general LLM research—the specific flow of logic that leads to a conclusion—and claims that understanding this flow in medical LLMs is functionally equivalent to achieving explainable AI. The authors survey current evaluation approaches, sorting them into conclusion-based, rationale-based, mechanistic, and interactive categories, and report that only a few studies actually assess reasoning. They then propose two theoretical frameworks meant to expose reasoning behaviour in a task-agnostic way. If taken up, the implication is that medical LLM evaluation would include process-level checks, not just final answers.

What carries the argument

The carrying object is the notion of reasoning behaviour as a distinct entity from reasoning outcome, borrowed and adapted from prior LLM research. Everything else hangs on this distinction: if evaluation shifts from final answers to the flow of logic, then new tools and metrics are needed. The paper's first framework exploits the deterministic structure of tree-based models (such as gradient boosting or random forests) so that model training yields interpretable decision sets. The second framework is an LLM-plus-symbolic pipeline: a medical LLM generates chain-of-thought explanations, attention weights, and candidate diagnostic rules, while a symbolic reasoner applies formal rules to patient data; the extracted traces are combined, and a process reward model can score their logical coherence. This second design is the main mechanism making low-level reasoning visible, and its transparency depends on treating CoT, attention, and extracted rules as faithful traces.

What would settle it

Run a medical multiple-choice benchmark, record each model's chain-of-thought before its final answer, and measure how often the trace supports a different option than the one chosen. If CoT mismatch proves frequent and unpredictable from the trace itself, the transparency claims of the proposed frameworks would be undercut, because the extracted reasoning would not be the reasoning the model actually used.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that reasoning behaviour can be defined, surveyed, and made inspectable in medical LLMs. Reasoning is the process of drawing conclusions from available information; reasoning outcome is the event where reasoning reaches a conclusion; reasoning behaviour is the specific flow of logic that leads to that outcome. The paper's strongest claim is that understanding reasoning behaviour in this setting is functionally equivalent to explainable AI, since both aim to reveal how a model is predisposed toward an outcome. Existing work mostly evaluates conclusions, and the authors find a striking scarcity of process-level evaluation in medicine. To fill the gap, they offer two frameworks: one that standardises input data and uses interpretable tree-based models whose decision sets can be read directly, and a second, more complex design that pairs an LLM (for hypotheses, literature, and diagnostic rules) with a symbolic reasoning system whose inference chains, combined with the LLM's chain-of-thought and attention weights, form an inspectable reasoning trace. The paper also catalogs open challenges, including reasoning-knowledge entanglement, scarce high-quality reasoning traces, reward-model scalability, and the autoregressive limitation that prevents LLMs from backtracking.

Load-bearing premise

The load-bearing premise is that what a model writes out as its reasoning—its chain-of-thought text, attention weights, and extracted rules—faithfully reflects the internal process that produced the answer; the paper itself cites documented cases in which the written reasoning supports one answer while the model selects a different final option.

Editorial extensions

If this is right

  • If the paper is right, medical LLM benchmarks should add process-level scoring—coherence, logical validity, completeness of reasoning traces—alongside accuracy.
  • The LLM-plus-symbolic framework would give clinicians a readable inference chain plus attention and CoT, making disagreements between machine and expert diagnosis inspectable.
  • Interactive evaluations like sequential diagnosis benchmarks would spread, since they expose reasoning under uncertainty and cost constraints better than static multiple-choice tests.
  • Process reward models trained on reasoning traces could be folded into RLHF training, so models are rewarded for sound step-by-step logic rather than only final answers.
  • Because CoT mismatch exists, no single trace type should be trusted alone; the paper's position implies evaluation should combine several paradigms simultaneously.

Reading between the lines

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

  • A testable extension of the second framework is to build it on a public clinical dataset and check whether the symbolic rules it extracts remain stable when patient data are perturbed; stability would be evidence that the rules are real reasoning, not memorised phrasing.
  • If reasoning transparency is treated as XAI, then medical AI regulation could eventually require trace-level documentation, similar to how clinical documentation records a clinician's differential reasoning.
  • The same reasoning-behaviour lens could be applied to other high-stakes domains such as legal or financial LLMs, where a trace that contradicts the final decision is equally dangerous.
  • One implicit prediction is that models trained with explicit trace supervision will show fewer hallucinations; this could be tested by comparing hallucination rates in large reasoning models with and without process reward training.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. This manuscript is a survey-and-position paper on reasoning behaviour in medical large language models (LLMs). It adapts definitions of reasoning, reasoning outcome, and reasoning behaviour from Mondorf & Plank; proposes a four-way taxonomy of evaluation paradigms (conclusion-based, rationale-based, mechanistic, and interactive); tabulates existing medical reasoning methods; argues that there is a striking lack of process-level evaluation; and proposes two theoretical frameworks intended to expose low-level reasoning behaviour. The first framework uses structured data and interpretable tree-based models, and the second combines an LLM with a symbolic solver, extracting CoT explanations, attention weights, and diagnostic rules as reasoning traces. The paper concludes that understanding reasoning behaviour in medical LLMs is functionally equivalent to explainable AI (XAI) and that this transparency will increase clinician trust and accelerate adoption.

Significance. The paper occupies a useful niche by directing attention to process-level evaluation of medical LLMs, which is genuinely underrepresented relative to accuracy-based benchmarking. Its four-way taxonomy in Table 3 is a plausible organizing device, and the surveys in Table 2 and Section 5 gather otherwise scattered recent work, including clinician-led CoT evaluations and process reward models. The authors should be credited for explicitly citing evidence against their own trace-fidelity assumption—the CoT-mismatch failure mode in Section 5.2—and for clearly labelling the proposed frameworks as theoretical rather than implemented. If the central claims could be supported, the paper would provide a compelling argument for complementing accuracy benchmarks with reasoning-process evaluation. As it stands, however, the constructive claims are asserted rather than demonstrated: the trace-fidelity premise is unvalidated, the 'striking lack' conclusion rests on an undocumented selection of works, and the XAI equivalence in Section 7.4 is definitional.

major comments (3)
  1. [§6.1 and §5.2]
  2. [§7.4 and Abstract]
  3. [§4.4 and §7.1]
minor comments (6)
  1. [Abstract] The abstract contains a duplicated word: 'evaluating reasoning reasoning in medical LLMs' should read 'evaluating reasoning in medical LLMs'.
  2. [§2.4] Section 2.4 ends with the incomplete placeholder text 'ref to other dag papers here', which should be removed or replaced with actual citations.
  3. [§5] The opening of Section 5 contains an unresolved figure reference, '[Figure ??]', which should be fixed or deleted.
  4. [Table 2] Table 2 is difficult to read because cells are densely packed and some entries are not separated clearly; the inconsistent author-label format (e.g., 'Binbin et al' versus 'Emre Kıcıman [51]') should be normalized.
  5. [References] Reference [67] is malformed, listing author names without a clear title boundary and ending with 'Towards conversational diagnostic AI. 2024.'; it should be formatted in the journal's reference style.
  6. [§6.1] The abbreviation 'PRM' is first used in Section 6.1 ('the PRM can be trained on reasoning traces') but is only defined later in Section 6.2.2 as 'Process Reward Models'; the definition should be moved to first use.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper makes no fitted predictions, and its reasoning-behaviour/XAI link is a definitional framing rather than a derivation from its own inputs.

full rationale

This paper is a review and position piece, not an empirical derivation. It proposes two explicitly theoretical frameworks for exposing reasoning behaviour, but it does not fit parameters, run experiments, or predict outcomes from data, so the fitted-input-called-prediction and self-definitional derivation patterns do not arise. The statement in Section 7.4 that 'understanding reasoning behaviour of medical LLMs is functionally equivalent to achieving explainable AI (XAI)' is an asserted equivalence, not a conclusion derived from a definition of XAI that already contains reasoning behaviour; it is therefore a framing claim, not a circular reduction. Similarly, the proposed frameworks' reliance on CoT explanations, attention weights, and LLM-extracted diagnostic rules as reasoning traces is an unvalidated premise, and the paper itself cites CoT mismatch in Section 5.2 as a known failure mode. That is a validity or soundness concern about the framework, not a circularity in which an output is equivalent to an input by construction. The only self-citations are [7] and [9], used to illustrate clinical summarisation and to exclude genomic applications; they are not load-bearing for any central claim. No equation, fitted value, or cited uniqueness theorem forces the paper's conclusions, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper contains no fitted numbers, no quantitative experiments, and no parameter estimation. All load-bearing premises are domain assumptions about interpretability and trace validity, listed above; no invented entities are introduced.

assumptions (5)
  • domain assumption Mondorf & Plank's definitions of reasoning, reasoning outcome, and reasoning behaviour are adopted unchanged as the lens for medical LLMs.
    Section 3 states the definitions are 'slightly adapted' from [45] and applies them without independent validation.
  • domain assumption CoT explanations and attention weights stand for the model's internal reasoning behaviour.
    Section 6.1 extracts these artifacts as reasoning traces; Section 5.2's CoT mismatch discussion shows the assumption is fragile.
  • domain assumption Tree-based models and extracted decision sets provide interpretable reasoning behaviour.
    Section 6.1's first framework relies on the inherent interpretability of tree models and decision sets, but does not demonstrate that these expose the model's reasoning rather than a post hoc rule summary.
  • ad hoc to paper An LLM can extract faithful diagnostic rules from clinical guidelines for use by a symbolic solver.
    Section 6.1's second framework assumes rule extraction with acceptable fidelity and omits any error analysis; this is a paper-specific design assumption.
  • ad hoc to paper Dual-process System 1/System 2 theory is a valid design frame for medical reasoning transparency.
    Section 6.1 uses System 1/System 2 thinking to motivate the neurosymbolic framework while explicitly noting 'we neither support nor disregard this overall viewpoint.'

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Cite this review

Pith. "Pith review of Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models." pith.science (2026). https://pith.science/paper/WCYRXI6P

@misc{pith2026241215748,
  author       = {Pith},
  title        = {Pith review of: Critique of Impure Reason: Unveiling the reasoning behaviour of medical Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WCYRXI6P}},
  note         = {Machine review of arXiv:2412.15748}
}
read the original abstract

Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to explainable AI (XAI) in this context. In particular, achieving XAI in medical LLMs used in the clinical domain will have a significant impact across the healthcare sector. Results: Therefore, in this work, we adapt the existing concept of reasoning behaviour and articulate its interpretation within the specific context of medical LLMs. We survey and categorise current state-of-the-art approaches for modeling and evaluating reasoning reasoning in medical LLMs. Additionally, we propose theoretical frameworks which can empower medical professionals or machine learning engineers to gain insight into the low-level reasoning operations of these previously obscure models. We also outline key open challenges facing the development of Large Reasoning Models. Conclusion: The subsequent increased transparency and trust in medical machine learning models by clinicians as well as patients will accelerate the integration, application as well as further development of medical AI for the healthcare system as a whole.

Figures

Figures reproduced from arXiv: 2412.15748 by the authors.

Figure 1
Figure 1. A graphical abstract illustrating the current state of medical LLMs in the context of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. An illustration of the contrast in modalities between computer vision and natural language processing. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. A schematic diagram illustrating different strategies for solving problems using LLMs. Each rectangular box [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: A sample Directed Acyclic Graph. DAGs are commonly used to represent processes or systems where order matters and repetition is not allowed [40]. For example, DAGs can clarify whether the observed link between paracetamol use and childhood wheezing is due to a true cau…
Figure 5
Figure 5. Figure 5: A graphical representation of reasoning, reasoning outcome and reasoning behaviour. Reasoning encapsulates the process of drawing conclusions, arriving at a reasoning outcome. At a more fundamental level, reasoning behaviour describes the logical flow through the syste…
Figure 6
Figure 6. Figure 6: Two frameworks with a focus on exposing reasoning behaviour. Note that the two frameworks are independent but shown together to facilitate easier comparison. Top: Input data is standardised and fed to tree-based models. The deterministic nature of trees are exploited f…
Figure 7
Figure 7. Figure 7: An illustration of the spectrum of "System 1" fundamental thought processes to "System 2" analytical thought [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

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Reference graph

Works this paper leans on

111 extracted references · 30 canonical work pages

  1. [1]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017

    2017

  2. [2]

    Drhouse: An llm-empowered diagnostic reasoning system through harnessing outcomes from sensor data and expert knowledge

    Bufang Yang, Siyang Jiang, Lilin Xu, Kaiwei Liu, Hai Li, Guoliang Xing, Hongkai Chen, Xiaofan Jiang, and Zhenyu Yan. Drhouse: An llm-empowered diagnostic reasoning system through harnessing outcomes from sensor data and expert knowledge. arXiv preprint arXiv:2405.12541, 2024

    arXiv 2024

  3. [3]

    Sequential diagnosis with language models

    Harsha Nori, Mayank Daswani, Christopher Kelly, Scott Lundberg, Marco Tulio Ribeiro, Marc Wilson, Xiaoxuan Liu, Viknesh Sounderajah, Jonathan Carlson, Matthew P Lungren, et al. Sequential diagnosis with language models. arXiv preprint arXiv:2506.22405, 2025

    arXiv 2025

  4. [4]

    Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine.NPJ Digital Medicine, 7(1):20, 2024

    Thomas Savage, Ashwin Nayak, Robert Gallo, Ekanath Rangan, and Jonathan H Chen. Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine.NPJ Digital Medicine, 7(1):20, 2024

    2024

  5. [5]

    Medvlm-r1: Incentivizing medical reasoning capability of vision-language models (vlms) via reinforcement learning

    Jiazhen Pan, Che Liu, Junde Wu, Fenglin Liu, Jiayuan Zhu, Hongwei Bran Li, Chen Chen, Cheng Ouyang, and Daniel Rueckert. Medvlm-r1: Incentivizing medical reasoning capability of vision-language models (vlms) via reinforcement learning. arXiv preprint arXiv:2502.19634, 2025

    arXiv 2025

  6. [6]

    Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models

    Yuxiang Lai, Jike Zhong, Ming Li, Shitian Zhao, and Xiaofeng Yang. Med-r1: Reinforcement learning for generalizable medical reasoning in vision-language models. arXiv preprint arXiv:2503.13939, 2025

    arXiv 2025

  7. [7]

    Enhancing medical summarization with parameter efficient fine tuning on local cpus

    Shamus Sim Zi Yang, Goh Man Fye, Wei Chung Yap, and Diong Zi Yu. Enhancing medical summarization with parameter efficient fine tuning on local cpus. In 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE), pages 1–7. IEEE, 2024

    2024

  8. [8]

    Ehragent: Code empowers large language models for complex tabular reasoning on electronic health records

    Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, and May D Wang. Ehragent: Code empowers large language models for complex tabular reasoning on electronic health records. arXiv preprint arXiv:2401.07128, 2024

    arXiv 2024

Show all 111 references
  1. [9]

    genomicbert: A light-weight foundation model for genome analysis using unigram tokenization and specialized dna vocabulary

    Tyrone Chen, Naima Vahab, Navya Tyagi, Eleanor Cummins, Anton Y Peleg, and Sonika Tyagi. genomicbert: A light-weight foundation model for genome analysis using unigram tokenization and specialized dna vocabulary. bioRxiv, pages 2023–05, 2023

    2023

  2. [10]

    What disease does this patient have? a large-scale open domain question answering dataset from medical exams, 2020

    Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. What disease does this patient have? a large-scale open domain question answering dataset from medical exams, 2020

    2020

  3. [11]

    Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering

    Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering. In Conference on health, inference, and learning, pages 248–260. PMLR, 2022. 20 Sim and Chen

    2022

  4. [12]

    Pubmedqa: A dataset for biomedical research question answering

    Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. Pubmedqa: A dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146, 2019

    1909 arXiv

  5. [13]

    Measuring massive multitask language understanding

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020

    2009 arXiv

  6. [14]

    Ai legal innovations: The benefits and drawbacks of chat-gpt and generative ai in the legal industry

    Laura A Lorek. Ai legal innovations: The benefits and drawbacks of chat-gpt and generative ai in the legal industry. Ohio Northern University Law Review, 50(3):4, 2024

    2024

  7. [15]

    Ai detection’s high false positive rates and the psychological and material impacts on students

    Whitney Gegg-Harrison and Claire Quarterman. Ai detection’s high false positive rates and the psychological and material impacts on students. In Academic Integrity in the Age of Artificial Intelligence, pages 199–219. IGI Global, 2024

    2024

  8. [16]

    Better alone than in bad company: Addressing the risks of companion chatbots through data protection by design

    Pierre Dewitte. Better alone than in bad company: Addressing the risks of companion chatbots through data protection by design. Computer Law & Security Review, 54:106019, 2024

    2024

  9. [17]

    The google engineer who thinks the company’s ai has come to life

    Nitasha Tiku. The google engineer who thinks the company’s ai has come to life. The Washington Post, 11:2022, 2022

    2022

  10. [18]

    Artificial intelligence algorithm for predicting mortality of patients with acute heart failure

    Joon-myoung Kwon, Kyung-Hee Kim, Ki-Hyun Jeon, Sang Eun Lee, Hae-Young Lee, Hyun-Jai Cho, Jin Oh Choi, Eun-Seok Jeon, Min-Seok Kim, Jae-Joong Kim, et al. Artificial intelligence algorithm for predicting mortality of patients with acute heart failure. PloS one, 14(7):e0219302, 2019

    2019

  11. [19]

    Axiomatic attribution for deep networks

    Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In International conference on machine learning, pages 3319–3328. PMLR, 2017

    2017

  12. [20]

    Grad-cam: Visual explanations from deep networks via gradient-based localization

    Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618–626, 2017

    2017

  13. [21]

    A novel visual interpretability for deep neural networks by optimizing activation maps with perturbation

    Qinglong Zhang, Lu Rao, and Yubin Yang. A novel visual interpretability for deep neural networks by optimizing activation maps with perturbation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3377–3384, 2021

    2021

  14. [22]

    Ecco: An open source library for the explainability of transformer language models

    J Alammar. Ecco: An open source library for the explainability of transformer language models. In Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing: System demonstr...

    2021

  15. [23]

    The language interpretability tool: Extensible, interactive visualizations and analysis for nlp models

    Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, et al. The language interpretability tool: Extensible, interactive visualizations and analysis for nlp models. arXiv prepri...

    2008 arXiv

  16. [25]

    Self-consistency improves chain of thought reasoning in language models

    Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. arXiv preprint arXiv:2203.11171, 2022

    2022 arXiv

  17. [26]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. Tree of thoughts: Deliberate problem solving with large language models, 2023

    2023

  18. [27]

    A survey of llm-based agents in medicine: How far are we from baymax? arXiv preprint arXiv:2502.11211, 2025

    Wenxuan Wang, Zizhan Ma, Zheng Wang, Chenghan Wu, Jiaming Ji, Wenting Chen, Xiang Li, and Yixuan Yuan. A survey of llm-based agents in medicine: How far are we from baymax? arXiv preprint arXiv:2502.11211, 2025

    2025 arXiv

  19. [28]

    Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning

    Ling Yue, Sixue Xing, Jintai Chen, and Tianfan Fu. Clinicalagent: Clinical trial multi-agent system with large language model-based reasoning. In Proceedings of the 15th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pages 1–10, 2024

    2024

  20. [29]

    Menti: Bridging medical calculator and llm agent with nested tool calling

    Yakun Zhu, Shaohang Wei, Xu Wang, Kui Xue, Xiaofan Zhang, and Shaoting Zhang. Menti: Bridging medical calculator and llm agent with nested tool calling. arXiv preprint arXiv:2410.13610, 2024

    2024 arXiv

  21. [30]

    Argmed-agents: Explainable clinical decision reasoning with large language models via argumentation schemes

    Shengxin Hong, Liang Xiao, Xin Zhang, and Jianxia Chen. Argmed-agents: Explainable clinical decision reasoning with large language models via argumentation schemes. arXiv preprint arXiv:2403.06294, 2024

    2024 arXiv

  22. [31]

    Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024

    Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024

    2024

  23. [32]

    Marco-o1: Towards open reasoning models for open-ended solutions, 2024

    Yu Zhao, Huifeng Yin, Bo Zeng, Hao Wang, Tianqi Shi, Chenyang Lyu, Longyue Wang, Weihua Luo, and Kaifu Zhang. Marco-o1: Towards open reasoning models for open-ended solutions, 2024. 21 Sim and Chen

    2024

  24. [33]

    Large language models encode clinical knowledge

    Karan Singhal, Shekoofeh Azizi, Tao Tu, S Sara Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, et al. Large language models encode clinical knowledge. Nature, 620(7972):172–180, 2023

    2023

  25. [34]

    Deep reinforcement learning from human preferences

    Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. Deep reinforcement learning from human preferences. Advances in neural information processing systems, 30, 2017

    2017

  26. [35]

    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

  27. [36]

    Training language models to follow instructions with human feedback

    Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:2773...

    2022

  28. [37]

    Proximal policy optimization algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017

    2017 arXiv

  29. [38]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in neural information processing systems, 36:53728–53741, 2023

    2023

  30. [39]

    Deepseekmath: Pushing the limits of mathematical reasoning in open language models

    Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024

    2024 arXiv

  31. [40]

    Directed acyclic graphs

    Ronja Foraita, Jacob Spallek, and Hajo Zeeb. Directed acyclic graphs. In Handbook of epidemiology, pages 1481–1517. Springer, 2014

    2014

  32. [41]

    Directed acyclic graphs: a tool for causal studies in paediatrics

    Thomas C Williams, Cathrine C Bach, Niels B Matthiesen, Tine B Henriksen, and Luigi Gagliardi. Directed acyclic graphs: a tool for causal studies in paediatrics. Pediatric research, 84(4):487–493, 2018

    2018

  33. [42]

    Causal reasoning and large language models: Opening a new frontier for causality

    Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality. arXiv preprint arXiv:2305.00050, 2023

    2023 arXiv

  34. [43]

    Applying large language models for causal structure learning in non small cell lung cancer

    Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo Bifulco, et al. Applying large language models for causal structure learning in non small cell lung cancer. In 2024 IEEE 12th I...

    2024

  35. [44]

    Inferbert: a transformer-based causal inference framework for enhancing pharmacovigilance

    Xingqiao Wang, Xiaowei Xu, Weida Tong, Ruth Roberts, and Zhichao Liu. Inferbert: a transformer-based causal inference framework for enhancing pharmacovigilance. Frontiers in Artificial Intelligence, 4:659622, 2021

    2021

  36. [45]

    Beyond accuracy: Evaluating the reasoning behavior of large language models–a survey

    Philipp Mondorf and Barbara Plank. Beyond accuracy: Evaluating the reasoning behavior of large language models–a survey. arXiv preprint arXiv:2404.01869, 2024

    2024 arXiv

  37. [46]

    Cambridge Handbook Of Thinking And Reasoning Ebook

    Cambridge Handbook Of Thinking. Cambridge Handbook Of Thinking And Reasoning Ebook . Psychology Press: London, UK, 1999

    1999

  38. [47]

    Causal models: How people think about the world and its alternatives

    S Sloman. Causal models: How people think about the world and its alternatives . Oxford University Press, 2009

    2009

  39. [48]

    Philosophy of mathematics, 2007

    Leon Horsten. Philosophy of mathematics, 2007

    2007

  40. [49]

    A survey of reasoning with foundation models

    Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, et al. A survey of reasoning with foundation models. arXiv preprint arXiv:2312.11562, 2023

    2023 arXiv

  41. [50]

    Ifqa: A dataset for open-domain question answering under counterfactual presuppositions

    Wenhao Yu, Meng Jiang, Peter Clark, and Ashish Sabharwal. Ifqa: A dataset for open-domain question answering under counterfactual presuppositions. arXiv preprint arXiv:2305.14010, 2023

    2023 arXiv

  42. [51]

    Causal reasoning and large language models: Opening a new frontier for causality, 2024

    Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan. Causal reasoning and large language models: Opening a new frontier for causality, 2024

    2024

  43. [52]

    Mycin: a knowledge-based consultation program for infectious disease diagnosis

    William Van Melle. Mycin: a knowledge-based consultation program for infectious disease diagnosis. Interna- tional journal of man-machine studies, 10(3):313–322, 1978

    1978

  44. [53]

    Internist-i, an experimental computer-based diagnostic consultant for general internal medicine

    Randolph A Miller, Harry E Pople Jr, and Jack D Myers. Internist-i, an experimental computer-based diagnostic consultant for general internal medicine. In Computer-assisted medical decision making , pages 139–158. Springer, 1985

    1985

  45. [54]

    Neurosymbolic artificial intelligence (why, what, and how)

    Amit Sheth, Kaushik Roy, and Manas Gaur. Neurosymbolic artificial intelligence (why, what, and how). IEEE Intelligent Systems, 38(3):56–62, 2023. 22 Sim and Chen

    2023

  46. [55]

    A brief overview of chatgpt: The history, status quo and potential future development

    Tianyu Wu, Shizhu He, Jingping Liu, Siqi Sun, Kang Liu, Qing-Long Han, and Yang Tang. A brief overview of chatgpt: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5):1122–1136, 2023

    2023

  47. [56]

    Llama: Open and efficient foundation language models

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

    2023 arXiv

  48. [57]

    Opportunities and risks of chatgpt in medicine, science, and academic publishing: a modern promethean dilemma

    Jan Homolak. Opportunities and risks of chatgpt in medicine, science, and academic publishing: a modern promethean dilemma. Croatian Medical Journal, 64(1):1, 2023

    2023

  49. [58]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824–24837, 2022

    2022

  50. [59]

    Huatuogpt-o1, towards medical complex reasoning with llms

    Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, and Benyou Wang. Huatuogpt-o1, towards medical complex reasoning with llms. arXiv preprint arXiv:2412.18925, 2024

    2024 arXiv

  51. [60]

    A generalist medical language model for disease diagnosis assistance

    Xiaohong Liu, Hao Liu, Guoxing Yang, Zeyu Jiang, Shuguang Cui, Zhaoze Zhang, Huan Wang, Liyuan Tao, Yongchang Sun, Zhu Song, et al. A generalist medical language model for disease diagnosis assistance. Nature medicine, 31(3):932–942, 2025

    2025

  52. [61]

    Crossing the trust gap in medical ai: Building an abductive bridge for xai

    Steven S Gouveia and Jaroslav Malík. Crossing the trust gap in medical ai: Building an abductive bridge for xai. Philosophy & Technology, 37(3):105, 2024

    2024

  53. [62]

    Mimic-iii, a freely accessible critical care database

    Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientific data, 3(1):1–9, 2016

    2016

  54. [63]

    Large language models are clinical reasoners: Reasoning- aware diagnosis framework with prompt-generated rationales

    Taeyoon Kwon, Kai Tzu-iunn Ong, Dongjin Kang, Seungjun Moon, Jeong Ryong Lee, Dosik Hwang, Beomseok Sohn, Yongsik Sim, Dongha Lee, and Jinyoung Yeo. Large language models are clinical reasoners: Reasoning- aware diagnosis framework with prompt-generated rationales. In Proceedi...

    2024

  55. [64]

    Meddm: Llm-executable clinical guidance tree for clinical decision-making

    Binbin Li, Tianxin Meng, Xiaoming Shi, Jie Zhai, and Tong Ruan. Meddm: Llm-executable clinical guidance tree for clinical decision-making. arXiv preprint arXiv:2312.02441, 2023

    2023 arXiv

  56. [65]

    Patterson, Matthew M

    Yanjun Gao, Ruizhe Li, Emma Croxford, Samuel Tesch, Daniel To, John Caskey, Brian W. Patterson, Matthew M. Churpek, Timothy Miller, Dmitriy Dligach, et al. Large language models and medical knowledge grounding for diagnosis prediction. medRxiv, pages 2023–11, 2023

    2023

  57. [66]

    Interpretable medical diagnostics with structured data extraction by large language models

    Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Pietro Lio, and Andrija Petrovic. Interpretable medical diagnostics with structured data extraction by large language models. arXiv preprint arXiv:2306.05052, 2023

    2023 arXiv

  58. [67]

    Towards conversational diagnostic ai

    Mike Schaekermann Khaled Saab Jan Freyberg Ryutaro Tanno Amy Wang Brenna Li Mohamed Amin Ne- nad Tomasev Shekoofeh Azizi Karan Singhal Yong Cheng Le Hou Albert Webson Kavita Kulkarni S Sara Mahdavi Christopher Semturs Juraj Gottweis Joelle Barral Katherine Chou Greg S Corrado ...

    2024

  59. [68]

    Towards trustworthy automatic diagnosis systems by emulating doctors’ reasoning with deep reinforcement learning

    Arsene Fansi Tchango, Rishab Goel, Julien Martel, Zhi Wen, Gaetan Marceau Caron, and Joumana Ghosn. Towards trustworthy automatic diagnosis systems by emulating doctors’ reasoning with deep reinforcement learning. Advances in Neural Information Processing Systems, 35:24502–24515, 2022

    2022

  60. [69]

    Mediq: Question-asking llms for adaptive and reliable medical reasoning

    Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, Jonathan Ilgen, Emma Pierson, Pang Wei Koh, and Yulia Tsvetkov. Mediq: Question-asking llms for adaptive and reliable medical reasoning. arXiv preprint arXiv:2406.00922, 2024

    2024 arXiv

  61. [70]

    Causality extraction from medical text using large language models (llms), 2024

    Seethalakshmi Gopalakrishnan, Luciana Garbayo, and Wlodek Zadrozny. Causality extraction from medical text using large language models (llms), 2024

    2024

  62. [71]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025

    2025 arXiv

  63. [72]

    A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions

    Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, et al. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232, 2023

    2023 arXiv

  64. [73]

    Yanjun Gao, Dmitriy Dligach, Timothy Miller, John Caskey, Brihat Sharma, Matthew M Churpek, and Majid Afshar. Dr. bench: Diagnostic reasoning benchmark for clinical natural language processing. Journal of biomedical informatics, 138:104286, 2023. 23 Sim and Chen

    2023

  65. [74]

    Sok: Memorization in general-purpose large language models, 2023

    Valentin Hartmann, Anshuman Suri, Vincent Bindschaedler, David Evans, Shruti Tople, and Robert West. Sok: Memorization in general-purpose large language models, 2023

    2023

  66. [75]

    Ddxplus: A new dataset for automatic medical diagnosis

    Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, and Joumana Ghosn. Ddxplus: A new dataset for automatic medical diagnosis. Advances in neural information processing systems, 35:31306–31318, 2022

    2022

  67. [76]

    Automatic interactive evaluation for large language models with state aware patient simulator

    Yusheng Liao, Yutong Meng, Yuhao Wang, Hongcheng Liu, Yanfeng Wang, and Yu Wang. Automatic interactive evaluation for large language models with state aware patient simulator. arXiv preprint arXiv:2403.08495, 2024

    2024 arXiv

  68. [77]

    Toward clinical generative ai: Conceptual framework

    Nicola Luigi Bragazzi and Sergio Garbarino. Toward clinical generative ai: Conceptual framework. JMIR AI, 3(1):e55957, 2024

    2024

  69. [78]

    Towards metacognitive clinical reasoning: Benchmark- ing md-pie against state-of-the-art llms in medical decision-making

    Yasma Esteitieh, Shaurjya Mandal, and George Laliotis. Towards metacognitive clinical reasoning: Benchmark- ing md-pie against state-of-the-art llms in medical decision-making. medRxiv, pages 2025–01, 2025

    2025

  70. [79]

    Clinical reasoning of a generative artificial intelligence model compared with physicians

    Stephanie Cabral, Daniel Restrepo, Zahir Kanjee, Philip Wilson, Byron Crowe, Raja-Elie Abdulnour, and Adam Rodman. Clinical reasoning of a generative artificial intelligence model compared with physicians. JAMA internal medicine, 184(5):581–583, 2024

    2024

  71. [80]

    Medical reasoning in llms: an in-depth analysis of deepseek r1

    Birger Moëll, Fredrik Sand Aronsson, and Sanian Akbar. Medical reasoning in llms: an in-depth analysis of deepseek r1. Frontiers in Artificial Intelligence, 8:1616145, 2025

    2025

  72. [81]

    Automating expert-level medical reasoning evaluation of large language models

    Shuang Zhou, Wenya Xie, Jiaxi Li, Zaifu Zhan, Meijia Song, Han Yang, Cheyenna Espinoza, Lindsay Welton, Xinnie Mai, Yanwei Jin, et al. Automating expert-level medical reasoning evaluation of large language models. arXiv preprint arXiv:2507.07988, 2025

    2025 arXiv

  73. [82]

    Medcasereasoning: Evaluating and learning diagnostic reasoning from clinical case reports

    Kevin Wu, Eric Wu, Rahul Thapa, Kevin Wei, Angela Zhang, Arvind Suresh, Jacqueline J Tao, Min Woo Sun, Alejandro Lozano, and James Zou. Medcasereasoning: Evaluating and learning diagnostic reasoning from clinical case reports. arXiv preprint arXiv:2505.11733, 2025

    2025 arXiv

  74. [83]

    Quantifying the reasoning abilities of llms on real-world clinical cases

    Pengcheng Qiu, Chaoyi Wu, Shuyu Liu, Weike Zhao, Zhuoxia Chen, Hongfei Gu, Chuanjin Peng, Ya Zhang, Yanfeng Wang, and Weidi Xie. Quantifying the reasoning abilities of llms on real-world clinical cases. arXiv preprint arXiv:2503.04691, 2025

    2025 arXiv

  75. [84]

    Applying large language models for causal structure learning in non small cell lung cancer

    Narmada Naik, Ayush Khandelwal, Mohit Joshi, Madhusudan Atre, Hollis Wright, Kavya Kannan, Scott Hill, Giridhar Mamidipudi, Ganapati Srinivasa, Carlo B Bifulco, Brian Piening, and Kevin Matlock. Applying large language models for causal structure learning in non small cell lun...

    2024

  76. [85]

    A unified approach to interpreting model predictions

    Scott Lundberg. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874, 2017

    2017 arXiv

  77. [86]

    Doctor xavier: Explainable diagnosis on physician-patient dialogues and xai evaluation

    Hillary Ngai and Frank Rudzicz. Doctor xavier: Explainable diagnosis on physician-patient dialogues and xai evaluation. arXiv preprint arXiv:2204.10178, 2022

    2022 arXiv

  78. [87]

    An interactive programming learning environment supporting paper computing and immediate evaluation for making thinking visible and traceable

    YungYu Zhuang, Yu-Hsuan Lin, Mahesh Liyanawatta, Andito Haryo Saputro, Yuniati Dwi Utami, and Jen-Hang Wang. An interactive programming learning environment supporting paper computing and immediate evaluation for making thinking visible and traceable. Interactive Learning Envi...

    2023

  79. [88]

    Interactive natural language processing

    Zekun Wang, Ge Zhang, Kexin Yang, Ning Shi, Wangchunshu Zhou, Shaochun Hao, Guangzheng Xiong, Yizhi Li, Mong Yuan Sim, Xiuying Chen, et al. Interactive natural language processing. arXiv preprint arXiv:2305.13246, 2023

    2023 arXiv

  80. [89]

    A systematic analysis of large language models as soft reasoners: The case of syllogistic inferences

    Leonardo Bertolazzi, Albert Gatt, and Raffaella Bernardi. A systematic analysis of large language models as soft reasoners: The case of syllogistic inferences. arXiv preprint arXiv:2406.11341, 2024

    2024 arXiv

  81. [90]

    Chain-of-thought reasoning without prompting, 2024

    Xuezhi Wang and Denny Zhou. Chain-of-thought reasoning without prompting, 2024

    2024

  82. [91]

    Qwen2 technical report

    Qwen Team. Qwen2 technical report. arXiv preprint arXiv:2407.10671, 2024

    2024 arXiv

  83. [92]

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025

    2025 arXiv

  84. [93]

    Xgboost: A scalable tree boosting system

    Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016

    2016

  85. [94]

    Random forests

    Leo Breiman. Random forests. Machine learning, 45:5–32, 2001

    2001

  86. [95]

    Machine learning and complex biological data

    Chunming Xu and Scott A Jackson. Machine learning and complex biological data. Genome biology, 20:1–4, 2019

    2019

  87. [96]

    From formal boosted tree explanations to interpretable rule sets

    Jinqiang Yu, Alexey Ignatiev, and Peter J Stuckey. From formal boosted tree explanations to interpretable rule sets. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Schloss-Dagstuhl-Leibniz Zentrum für Informatik, 2023. 24 Sim and Chen

    2023

  88. [97]

    Dual-process theories of higher cognition: Advancing the debate

    Jonathan St BT Evans and Keith E Stanovich. Dual-process theories of higher cognition: Advancing the debate. Perspectives on psychological science, 8(3):223–241, 2013

    2013

  89. [98]

    On the measure of intelligence

    François Chollet. On the measure of intelligence. arXiv preprint arXiv:1911.01547, 2019

    1911 arXiv

  90. [99]

    Arc-agi-2: A new challenge for frontier ai reasoning systems

    Francois Chollet, Mike Knoop, Gregory Kamradt, Bryan Landers, and Henry Pinkard. Arc-agi-2: A new challenge for frontier ai reasoning systems. arXiv preprint arXiv:2505.11831, 2025

    2025 arXiv

  91. [100]

    Chain of thought monitorability: A new and fragile opportunity for ai safety

    Tomek Korbak, Mikita Balesni, Elizabeth Barnes, Yoshua Bengio, Joe Benton, Joseph Bloom, Mark Chen, Alan Cooney, Allan Dafoe, Anca Dragan, et al. Chain of thought monitorability: A new and fragile opportunity for ai safety. arXiv preprint arXiv:2507.11473, 2025

    2025 arXiv

  92. [101]

    From generation to judgment: Opportunities and challenges of llm-as-a-judge, 2025

    Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattachar- jee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, et al. From generation to judgment: Opportunities and challenges of llm-as-a-judge, 2025. URL https://arxiv. org/abs/2411.16594, 2025

    2025

  93. [102]

    Let’s verify step by step, 2023

    Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. Let’s verify step by step, 2023

    2023

  94. [103]

    Solving math word problems with process- and outcome-based feedback, 2022

    Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. Solving math word problems with process- and outcome-based feedback, 2022

    2022

  95. [104]

    Self-supervised learning from images with a joint-embedding predictive architecture

    Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, and Nicolas Ballas. Self-supervised learning from images with a joint-embedding predictive architecture. In Proceedings of the IEEE/CVF Conference on Computer Vision and P...

    2023

  96. [105]

    A survey on latent reasoning

    Rui-Jie Zhu, Tianhao Peng, Tianhao Cheng, Xingwei Qu, Jinfa Huang, Dawei Zhu, Hao Wang, Kaiwen Xue, Xuanliang Zhang, Yong Shan, et al. A survey on latent reasoning. arXiv preprint arXiv:2507.06203, 2025

    2025 arXiv

  97. [106]

    Training large language models to reason in a continuous latent space

    Shibo Hao, Sainbayar Sukhbaatar, DiJia Su, Xian Li, Zhiting Hu, Jason Weston, and Yuandong Tian. Training large language models to reason in a continuous latent space. arXiv preprint arXiv:2412.06769, 2024

    2024 arXiv

  98. [107]

    Gemini: a family of highly capable multimodal models

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalk- wyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805, 2023

    2023 arXiv

  99. [108]

    From llm to nmt: Advancing low-resource machine translation with claude

    Maxim Enis and Mark Hopkins. From llm to nmt: Advancing low-resource machine translation with claude. arXiv preprint arXiv:2404.13813, 2024

    2024 arXiv

  100. [109]

    Mmed-rag: Versatile multimodal rag system for medical vision language models, 2024

    Peng Xia, Kangyu Zhu, Haoran Li, Tianze Wang, Weijia Shi, Sheng Wang, Linjun Zhang, James Zou, and Huaxiu Yao. Mmed-rag: Versatile multimodal rag system for medical vision language models, 2024

    2024

  101. [110]

    Medical large language models are susceptible to targeted misinformation attacks

    Tianyu Han, Sven Nebelung, Firas Khader, Tianci Wang, Gustav Müller-Franzes, Christiane Kuhl, Sebastian Försch, Jens Kleesiek, Christoph Haarburger, Keno K Bressem, et al. Medical large language models are susceptible to targeted misinformation attacks. npj Digital Medicine, 7...

    2024

  102. [111]

    Securing large language models: Addressing bias, misinformation, and prompt attacks

    Benji Peng, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Junyu Liu, and Qian Niu. Securing large language models: Addressing bias, misinformation, and prompt attacks. arXiv preprint arXiv:2409.08087, 2024

    2024

  103. [112]

    Cares: A comprehensive benchmark of trustworthiness in medical vision language models, 2024

    Peng Xia, Ze Chen, Juanxi Tian, Yangrui Gong, Ruibo Hou, Yue Xu, Zhenbang Wu, Zhiyuan Fan, Yiyang Zhou, Kangyu Zhu, Wenhao Zheng, Zhaoyang Wang, Xiao Wang, Xuchao Zhang, Chetan Bansal, Marc Niethammer, Junzhou Huang, Hongtu Zhu, Yun Li, Jimeng Sun, Zongyuan Ge, Gang Li, James ...

    2024

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.