Pith. sign in

REVIEW 3 major objections 5 minor 31 references

Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read This paper claims that a two-stage teacher-student framework lets open-weight multimodal language models forecast eGFR decline with accuracy comparable to proprietary models while generating structured clinical explanations.

desk verdict Own Table I invalidates the paper's central claim; the framework is an incremental but clearly presented combination, not a breakthrough. read the letter →

arxiv 2507.22464 v1 pith:OOJMTS5H submitted 2025-07-30 cs.LG cs.AIcs.MAstat.AP

Peng-Yi Wu , Pei-Cing Huang , Ting-Yu Chen , Chantung Ku , Ming-Yen Lin , Yihuang Kang This is my paper
classification cs.LGcs.AIcs.MAstat.AP
keywords estimatedglomerularfiltrationratelargemultimodalmodelsexplainableAIchronickidneydiseaseknowledgetransfershort-termmemoryabductivereasoningeGFRforecasting
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

The paper claims that a two-stage collaborative framework brings open-weight multimodal language models up to the predictive accuracy of proprietary API-based models for forecasting estimated glomerular filtration rate (eGFR), the main marker of kidney function decline. In the first stage, a proprietary teacher model reads de-identified eGFR trend charts and produces structured textual interpretations, with the same model scoring its own outputs to select the best one. In the second stage, a locally deployable open-weight student receives the charts, clinical and laboratory variables, and the teacher's summaries, then uses chain-of-thought prompting to predict the next eGFR and to explain that prediction, aided by short-term memory of prior steps. Reported experiments show the open-weight models improve with knowledge transfer and memory and approach or match commercial models on MAE, while generating two-part abductive explanations that combine observed-data rationale with plausible hypotheses. The paper also notes that the best raw MAE in the study belongs to a non-interpretable random forest, so the framework's advantage is the combination of competitive accuracy for open-weight LMMs with per-patient explanations.

What carries the argument

The load-bearing machinery is a two-stage visual-knowledge transfer loop around eGFR trend charts. Patient histories are drawn as de-identified line charts, and a proprietary teacher model interprets them, then evaluates its own interpretations against a rubric and keeps the top-scoring summary. The open-weight student consumes those summaries together with the charts and tabular clinical variables, uses chain-of-thought prompting to separate prediction from explanation, and maintains short-term memory of previous prompt, prediction, explanation, and ground truth to refine the next step. Explanations are split into selective abduction, which reasons from observed clinical data, and creative abduction, which hypothesizes unobserved factors, a structure borrowed from abductive reasoning theory. This combination is what carries the paper's claim that accuracy and interpretability can be improved without retraining.

What would settle it

Run the student model with the teacher's rubric-selected interpretations versus randomly chosen teacher interpretations on the same validation split; if validation MAE does not improve under rubric selection, the knowledge-transfer and self-evaluation mechanism is not carrying the reported gains.

Watch

Extended reading notes

Core claim

The central claim is that a modular teacher-student LMM collaboration can make open-weight models competitive with proprietary API models for eGFR forecasting while providing clinically meaningful explanations. The pipeline converts each patient's eGFR history into a sequence of de-identified trend charts; a proprietary teacher LMM interprets each chart and, acting as its own evaluator with a rubric, selects the highest-scoring interpretation. The open-weight student LMM takes the chart, clinical and laboratory variables, and the teacher's interpretation, predicts the next eGFR, and then grounds a structured explanation in that prediction. Selective abduction supplies data-driven rationale from trends and biomarkers, while creative abduction offers plausible unobserved causes such as poor medication adherence or uncontrolled blood glucose. Short-term memory stores each prior prompt, prediction, explanation, and the ground-truth outcome so the student can self-correct on subsequent steps. With both knowledge transfer and memory, the open-weight student models reach MAE values close to those of the proprietary comparators, with GPT-4o the strongest zero-shot LMM and a random forest the numeric MAE leader but without explanations.

Load-bearing premise

The framework assumes the teacher model's self-scored 'best' interpretation of each eGFR chart is clinically accurate and useful for the student, and the study does not check those summaries against external ground truth or expert judgment.

Editorial extensions

If this is right

  • Clinical settings that cannot send patient data to external APIs could deploy open-weight LMMs locally and still get eGFR forecasts close to commercial-model accuracy.
  • Each forecast comes with a two-part explanation, one grounded in observed labs and trends and one hypothesizing unobserved factors, giving clinicians an audit trail for the prediction.
  • The modular design lets teams pick components per model: knowledge transfer for models with weaker visual-language skills, memory alone for models with strong native multimodal ability.
  • Because the students are not retrained, adding the framework to a new site works from inference-time inputs and avoids the cost of training bespoke models.
  • The same reasoning structure could be transferred to medical education, where junior clinicians practice building data-driven and hypothesis-driven explanations.

Reading between the lines

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

  • A testable extension beyond eGFR would apply the same teacher-student pipeline to other longitudinal biomarkers, such as blood pressure or HbA1c, to see whether visual trend transfer and short-term memory generalize.
  • The paper's component comparison suggests a tuning heuristic: models with weak vision-language integration gain most from knowledge transfer, while models with strong native multimodal ability may need only the memory component.
  • An ablation that replaces the teacher's rubric-selected summaries with randomly chosen summaries would reveal whether the self-evaluation step, rather than the content of transfer, drives the student's improvement.
  • The strongest raw MAE in the study belongs to a non-interpretable random forest, so the framework's practical case rests on making open-weight LMMs competitive while adding per-patient explanations, not on being the most accurate predictor in absolute terms.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript proposes a two-stage teacher-student framework for forecasting estimated glomerular filtration rate (eGFR) with open-source large multimodal models (LMMs). In the first stage, Gemini 1.5 Pro, acting as a teacher LMM, interprets de-identified eGFR trend charts and produces textual summaries; the same model, acting as an evaluator LMM, scores these interpretations and selects the highest-scoring one. In the second stage, an open-source student LMM receives the charts, clinical variables, and the teacher's selected interpretation, and generates eGFR predictions with chain-of-thought and abductive reasoning. A short-term memory mechanism is also introduced to carry forward prior prompts, predictions, and explanations. The paper claims that the proposed framework achieves predictive performance and interpretability comparable to proprietary models. The experimental comparison in Section IV and Table I, however, does not support this claim: the open-source models' best validation MAE is 3.87, versus 2.50 for GPT-4o, and their validation MAPE is more than double GPT-4o's.

Significance. If the claim were valid, the framework would be a meaningful step toward privacy-preserving, locally deployable clinical forecasting with interpretable reasoning. The paper deserves credit for a modular design, for comparing several open- and closed-source LMM families, and for making the quantitative results available in a single table. However, the central performance claim is contradicted by the reported numbers, and the knowledge-transfer stage relies on a self-evaluation loop without external ground truth. These issues are load-bearing, so the current manuscript does not establish its stated contribution.

major comments (3)
  1. [Abstract; Section IV, Table I] The central claim that the framework achieves predictive performance 'comparable to proprietary models' is contradicted by the paper's own Table I. For Llama 3.2 Vision 11B with knowledge transfer and short-term memory, the validation MAE is 3.87 and validation MAPE is 24.54%; for Gemma 3 12B, the corresponding values are 3.84 and 28.34%. GPT-4o zero-shot achieves validation MAE 2.50 and MAPE 11.72%, and GPT-4o with short-term memory achieves 2.55 and 11.51%. Even Gemini 1.5 Pro and Gemini 2.0 Flash, with validation MAEs of 2.82 and 2.65 respectively, are markedly better. The best open-source validation MAE is thus roughly 45–55% higher than GPT-4o's, and the MAPE is more than double. This is not 'comparable' in any standard quantitative sense. The text highlights the MAE improvements from 4.33 to 3.87 for Llama and from 4.16 to 3.84 for Gemma, but omits that the validation MAPE simultaneously worsens from 18.68% to 24.54% for Llama and from 17.10% to 28.34% for Gemma. The abstract and Section IV require revision to state what the data actually show, namely that the proposed components improve training MAE in some settings while validation MAPE degrades.
  2. [Section III, Image Interpretation stage] The knowledge-transfer stage is a self-referential selection loop. As described, the T-LMM (Gemini 1.5 Pro) generates interpretations, and then the same model, acting as the E-LMM, evaluates its own outputs with a predefined rubric and selects the highest-scoring interpretation. There is no external ground truth for the clinical accuracy or coherence of the selected interpretation. The paper therefore has not validated that the transferred knowledge is clinically sound; the reported improvements from knowledge transfer could be driven by the self-selection bias, by the mere addition of textual context, or by an artifact of the rubric. A concrete test would compare the student's predictions when given (a) the self-selected best interpretation, (b) a randomly selected interpretation, (c) a fixed template summary, and (d) clinician-rated interpretations. Without such a comparison, the claim that knowledge transfer improves prediction accuracy and interpretability is unsupported.
  3. [Section IV, Model evaluation and Table I] The evaluation methodology is not sufficient to support the reported improvements. Section IV states that accuracy is assessed using MSE and MAPE, but Table I reports MAE and MAPE; this inconsistency should be corrected. More importantly, the dataset is small (570 observations from 50 patients, with a 70/30 patient split, so roughly 15 validation patients), and no confidence intervals, standard deviations, or significance tests are reported. A paired per-patient comparison is needed to determine whether differences such as validation MAE 4.06 vs. 3.87 for Llama are real or within noise. The opposite movements of MAE and MAPE for the same predictions (MAE improving while MAPE worsens) also need an explanation, such as whether errors are concentrated on low-eGFR/high-percentage-error cases. Without this analysis, the phrase 'substantial gains' in Section IV is not justified.
minor comments (5)
  1. [Section IV] The text refers to 'Gemma 9b' in one place, while Table I and later text refer to 'Gemma 3 12B'; the model name and size should be consistent throughout.
  2. [Section III] There is a typo in 'reducing in consistencies or fragmented responses'; it should read 'reducing inconsistencies'.
  3. [Section IV] The sentence 'Qwen 2.5 vision 32b showed the best result without using any complementary techniques' should clarify that this is the best among open-source models; Table I shows several proprietary models with better accuracy.
  4. [Section III and Figure 2] The description of the short-term memory mechanism would benefit from explicit prompt templates and an explicit statement of what is stored and retrieved; the paper says interactions are stored but does not give the exact memory format. Figure 2 also appears to show two different predictions (47 and 11.8) without a clear caption explaining whether these are two patients or two stages; please clarify.
  5. [References] Reference [9] is described in the text as 'Scao et al.', but the cited work is by Mohtashami et al.; the in-text attribution should be corrected to match the reference.

Circularity Check

1 steps flagged · score 4.0 of 10

Self-evaluation loop makes 'best interpretation' definitionally circular, but numeric eGFR predictions remain externally benchmarked; no full derivation collapse.

  1. self definitional [Section III, 'Image Interpretation' paragraph (framework description around Fig. 1)]
    "The T-LMM produces structured interpretations for each chart, which are then evaluated by the same model, acting as an Evaluator LMM (E-LMM), using a predefined rubric to assess clinical accuracy and coherence. The highest-scoring interpretation is selected as the final output."

    Clinical accuracy and coherence are operationalized as the rubric score assigned by the same model (T-LMM/E-LMM) that generated the candidate interpretations, so 'best interpretation' is true by construction: 'highest-scoring' means 'self-preferred', not clinically validated by any external ground truth. These self-selected summaries are then labeled 'external knowledge' for the S-LMM, so the reported benefit of knowledge transfer measures the effect of feeding a model its own self-consistent summaries rather than independently validated clinical content. This is a definitional circularity in the interpretation-quality claim.

full rationale

The paper's central numeric claim is not reduced to its inputs by equations or fitted parameters: eGFR predictions are scored against observed eGFR via MAE/MAPE, and Table I independently benchmarks GPT-4o, Gemini, Llama, Gemma, Qwen, and Claude, so the central performance comparison is externally grounded. The self-citation [7] (prior LMM eGFR work) is not load-bearing because the paper re-runs proprietary models here and reports their numbers directly. The one genuine circular element is the teacher/evaluator loop: the same model generates and scores interpretations, making 'best interpretation' self-definitional, and these summaries are then called 'external knowledge.' Because the framework's own Table I actually shows knowledge transfer worsening validation MAPE for Llama and Gemma, the claimed 'comparable to proprietary models' is unsupported, but that is a correctness/validity problem rather than a derivation-equivalence problem. Overall, the numeric prediction pipeline is externally evaluated, but the knowledge-transfer quality claim contains a definitional self-selection circle, yielding a partial circularity score of 4.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. The free parameters and axioms listed above are the key choices and assumptions that the reported performance depends on, in addition to the standard assumptions about LMM reliability.

free parameters (1)
  • Iterations for T-LMM interpretation (5 iterations) = 5
    Figure 1 indicates 5 iterations of interpretation generation; the paper does not justify this choice or test sensitivity to it.
assumptions (4)
  • domain assumption The T-LMM's self-evaluated selected interpretation is clinically accurate and coherent.
    Section III: Gemini 1.5 Pro generates interpretations and, acting as E-LMM, scores them with a rubric and picks the best; there is no external ground truth for this selection.
  • ad hoc to paper The E-LMM's predefined rubric is a valid measure of interpretation quality.
    The rubric is not described in the paper; the claim that the highest-scoring interpretation is best depends on an unspecified instrument.
  • domain assumption The subset of 570 observations from 50 patients is representative of the CKD population.
    Section IV: the subset is randomly selected, but no power analysis or representativeness check (beyond CKD stage balance) is reported; all conclusions depend on this small sample.
  • domain assumption The generated explanations reflect clinically meaningful abductive reasoning.
    Section IV presents one illustrative example; no clinician evaluation is provided, and the authors acknowledge validation is pending.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework." pith.science (2026). https://pith.science/paper/OOJMTS5H

@misc{pith2026250722464,
  author       = {Pith},
  title        = {Pith review of: Towards Interpretable Renal Health Decline Forecasting via Multi-LMM Collaborative Reasoning Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OOJMTS5H}},
  note         = {Machine review of arXiv:2507.22464}
}
read the original abstract

Accurate and interpretable prediction of estimated glomerular filtration rate (eGFR) is essential for managing chronic kidney disease (CKD) and supporting clinical decisions. Recent advances in Large Multimodal Models (LMMs) have shown strong potential in clinical prediction tasks due to their ability to process visual and textual information. However, challenges related to deployment cost, data privacy, and model reliability hinder their adoption. In this study, we propose a collaborative framework that enhances the performance of open-source LMMs for eGFR forecasting while generating clinically meaningful explanations. The framework incorporates visual knowledge transfer, abductive reasoning, and a short-term memory mechanism to enhance prediction accuracy and interpretability. Experimental results show that the proposed framework achieves predictive performance and interpretability comparable to proprietary models. It also provides plausible clinical reasoning processes behind each prediction. Our method sheds new light on building AI systems for healthcare that combine predictive accuracy with clinically grounded interpretability.

Figures

Figures reproduced from arXiv: 2507.22464 by the authors.

Figure 1
Figure 1. Overview of the proposed two-stage eGFR prediction. generation. The framework, as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. An example of the prediction and explanation generated by Qwen 2.5 vision. complementary techniques. This demonstrates that strong baseline results are possible with certain architectures. While our framework is designed to be modular and applicable to any open-source model, we further evaluated additional models to examine the impact of each component. Llama 3.2 vision demonstrated substantial gains when both knowl… view at source ↗

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

31 extracted references · 18 canonical work pages

  1. [1]

    The global burden of chronic kidney disease: estimates, variability and pitfalls,

    R. J. Glassock, D. G. Warnock, and P. Delanaye, “The global burden of chronic kidney disease: estimates, variability and pitfalls,” Nat Rev Nephrol , vol. 13, no. 2, pp. 104 –114, Feb. 2017, doi: 10.1038/nrneph.2016.163

    work page doi:10.1038/nrneph.2016.163 2017

  2. [2]

    L. Chu et al., “AWAREness of Diagnosis and Treatment of Chronic Kidney Disease in Adults With Type 2 Diabetes (AWARE-CKD in Predicted eGFR value: 11.8 (A) Selective Abduction (Data-driven Explanation) (B) Creative Abduction (Hypothesis-driven Explanation) The consistent historical decline in eGFR, combined with the patient's diabetes, severe albuminuria, ...

    work page

  3. [3]

    Uncontrolled Blood Glucose Levels: While blood glucose levels are not provided in the data, assuming that the patient’s diabetes is not optimally controlled could accelerate the decline in eGFR

    work page

  4. [4]

    Non-compliance could lead to a faster decline in kidney function

    Inadequate Compliance with Treatment: The patient may not be adhering to prescribed treatments (e.g., ACE inhibitors/ARBs, antihypertensives, or blood glucose management). Non-compliance could lead to a faster decline in kidney function. ... T2D),” Canadian Journal of Diabetes, vol. 46, no. 5, pp. 464–472, Jul. 2022, doi: 10.1016/j.jcjd.2022.01.008

    work page doi:10.1016/j.jcjd.2022.01.008 2022

  5. [5]

    New Equations for Estimating Glomerular Filtration Rate,

    A. A. Killeen and G. L. Horowitz, “New Equations for Estimating Glomerular Filtration Rate,” Clinical Chemistry, vol. 68, no. 4, pp. 491–493, Nov. 2021, doi: 10.1093/clinchem/hvab260

    work page doi:10.1093/clinchem/hvab260 2021

  6. [6]

    Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review,

    C. Xiao, E. Choi, and J. Sun, “Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review,” Journal of the American Medical Informatics Association, vol. 25, no. 10, pp. 1419–1428, Oct. 2018, doi: 10.1093/jamia/ocy068

    work page doi:10.1093/jamia/ocy068 2018

  7. [7]

    On the Opportunities and Risks of Foundation Models,

    R. Bommasani et al. , “On the Opportunities and Risks of Foundation Models,” Jul. 12, 2022, arXiv: arXiv:2108.07258. doi: 10.48550/arXiv.2108.07258

    arXiv doi:10.48550/arxiv.2108.07258 2022

  8. [8]

    The future landscape of large language models in medicine,

    J. Clusmann et al., “The future landscape of large language models in medicine,” Commun Med, vol. 3, no. 1, pp. 1 –8, Oct. 2023, doi: 10.1038/s43856-023-00370-1

    doi:10.1038/s43856-023-00370-1 2023

Show all 31 references
  1. [9]

    Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models,

    C.-Y. Li, J. -T. Wu, C. Hsu, M. -Y. Lin, and Y. Kang, “Understanding eGFR Trajectories and Kidney Function Decline via Large Multimodal Models,” Sep. 04, 2024, arXiv: arXiv:2409.02530. doi: 10.48550/arXiv.2409.02530

    2024 doi arXiv

  2. [10]

    Large language models in healthcare and medical domain: A review,

    Z. A. Nazi and W. Peng, “Large language models in healthcare and medical domain: A review,” in Informatics, MDPI, 2024, p. 57

    2024

  3. [11]

    Social Learning: Towards Collaborative Learning with Large Language Models,

    A. Mohtashami, F. Hartmann, S. Gooding, L. Zilka, M. Sharifi, and B. A. y Arcas, “Social Learning: Towards Collaborative Learning with Large Language Models,” Feb. 08, 2024, arXiv: arXiv:2312.11441. doi: 10.48550/arXiv.2312.11441

    2024 doi arXiv

  4. [12]

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

    J. Wei et al., “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems, vol. 35, pp. 24824–24837, 2022

    2022

  5. [13]

    Interpretation as abduction,

    J. R. Hobbs, M. E. Stickel, D. E. Appelt, and P. Martin, “Interpretation as abduction,” Artificial Intelligence, vol. 63, no. 1, pp. 69 –142, 1993, doi: https://doi.org/10.1016/0004 - 3702(93)90015-4

    1993 doi

  6. [14]

    Chronic kidney disease and the global public health agenda: an international consensus,

    A. Francis et al. , “Chronic kidney disease and the global public health agenda: an international consensus,” Nat Rev Nephrol , vol. 20, no. 7, pp. 473–485, Jul. 2024, doi: 10.1038/s41581-024-00820- 6

    2024 doi

  7. [15]

    KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease,

    P. E. Stevens et al., “KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease,” Kidney International, vol. 105, no. 4, pp. S117 –S314, Apr. 2024, doi: 10.1016/j.kint.2023.10.018

    2024 doi

  8. [16]

    Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning,

    C.-C. Kuo et al., “Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning,” npj Digital Medicine, vol. 2, no. 1, p. 29, Apr. 2019, doi: 10.1038/s41746-019-0104-2

    2019 doi

  9. [17]

    Development and External Validation of a Machine Learning Model for Progression of CKD,

    T. Ferguson et al. , “Development and External Validation of a Machine Learning Model for Progression of CKD,” Kidney Int Rep, vol. 7, no. 8, pp. 1772 –1781, Aug. 2022, doi: 10.1016/j.ekir.2022.05.004

    2022 doi

  10. [18]

    A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,

    G. Schwalbe and B. Finzel, “A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts,” Data Mining and Knowledge Discovery , vol. 38, no. 5, pp. 3043–3101, Sep. 2024, doi: 10.1007/s10618-022- 00867-8

    2024 doi

  11. [19]

    A Unified Approach to Interpreting Model Predictions,

    S. Lundberg and S. -I. Lee, “A Unified Approach to Interpreting Model Predictions,” Nov. 25, 2017, arXiv: arXiv:1705.07874. doi: 10.48550/arXiv.1705.07874

    2017 doi arXiv

  12. [20]

    ‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,

    M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why Should I Trust You?’: Explaining the Predictions of Any Classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , San Francisco California USA: ACM, Aug. 2016, pp. 1135 –...

    2016

  13. [21]

    ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations.,

    B. R. Kim, K. Srinivasan, S. H. Kong, J. H. Kim, C. S. Shin, and S. Ram, “ROLEX: A Novel Method for Interpretable Machine Learning Using Robust Local Explanations.,” MIS Quarterly, vol. 47, no. 3, 2023

    2023

  14. [22]

    TabLLM: Few -shot Classification of Tabular Data with Large Language Models,

    S. Hegselmann, A. Buendia, H. Lang, M. Agrawal, X. Jiang, and D. Sontag, “TabLLM: Few -shot Classification of Tabular Data with Large Language Models,” in Proceedings of The 26th International Conference on Artificial Intelligence and Statistics , PMLR, Apr. 2023, pp. 5549–558...

    2023

  15. [23]

    Gemini: A Family of Highly Capable Multimodal Models,

    G. Team et al., “Gemini: A Family of Highly Capable Multimodal Models,” Jun. 17, 2024, arXiv: arXiv:2312.11805. doi: 10.48550/arXiv.2312.11805

    2024 doi arXiv

  16. [24]

    GPT -4o System Card,

    OpenAI et al. , “GPT -4o System Card,” Oct. 25, 2024, arXiv: arXiv:2410.21276. doi: 10.48550/arXiv.2410.21276

    2024 doi arXiv

  17. [25]

    Mm-vet v2: A challenging benchmark to evaluate large multimodal models for integrated capabilities,

    W. Yu et al., “Mm-vet v2: A challenging benchmark to evaluate large multimodal models for integrated capabilities,” arXiv preprint arXiv:2408.00765, 2024

    2024 arXiv

  18. [26]

    A Survey on the Memory Mechanism of Large Language Model based Agents,

    Z. Zhang et al., “A Survey on the Memory Mechanism of Large Language Model based Agents,” Apr. 21, 2024, arXiv: arXiv:2404.13501. doi: 10.48550/arXiv.2404.13501

    2024 doi arXiv

  19. [27]

    Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI,

    S. S. Gouveia and J. Malík, “Crossing the Trust Gap in Medical AI: Building an Abductive Bridge for xAI,” Philos. Technol., vol. 37, no. 3, p. 105, Aug. 2024, doi: 10.1007/s13347-024-00790-4

    2024 doi

  20. [28]

    Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

    G. Team et al., “Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,” arXiv preprint arXiv:2403.05530, 2024

    2024 arXiv

  21. [29]

    The Llama 3 Herd of Models,

    A. Dubey et al., “The Llama 3 Herd of Models,” Aug. 15, 2024, arXiv: arXiv:2407.21783. doi: 10.48550/arXiv.2407.21783

    2024 doi arXiv

  22. [30]

    meta-llama/Llama-3.2-11B-Vision-Instruct · Hugging Face

    “meta-llama/Llama-3.2-11B-Vision-Instruct · Hugging Face.” Accessed: May 14, 2025. [Online]. Available: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct

    2025

  23. [31]

    Gemma 3 technical report,

    G. Team et al. , “Gemma 3 technical report,” arXiv preprint arXiv:2503.19786, 2025

    2025 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.