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REVIEW 4 major objections 5 minor 67 references

Mobilizing Waldo: Evaluating Multimodal AI for Public Mobilization

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Using Where's Waldo images as stand-ins for crowded public scenes, this paper shows that GPT-4o can describe scenes and devise creative persuasion tactics but consistently fails to locate Waldo or correctly identify characters, exposing a…

desk verdict A small empirical paper with a credible negative finding about GPT-4o's spatial localization in dense scenes, but the lack of quantitative criteria makes the headline claim under-supported. read the letter →

arxiv 2412.14210 v1 pith:VAVY4CU4 submitted 2024-12-18 cs.HC cs.CYcs.SI

Manuel Cebrian , Petter Holme , Niccolo Pescetelli This is my paper · ORCID
classification cs.HCcs.CYcs.SI
keywords multimodalLLMspatialreasoningpublicmobilizationWhere'sWaldoGPT-4obenchmarkpersuasionvisualgrounding
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 tries to establish a safe, controlled way to test whether multimodal AI can handle the perceptual and social-analysis steps required for public mobilization. It uses Where's Waldo images as proxies for crowded public gatherings, prompting GPT-4o to locate Waldo, describe the scene, and propose how Waldo could persuade five characters to dress like him. The finding is that GPT-4o produces fluent descriptions and inventive strategies but consistently mislocates Waldo and misidentifies characters in dense scenes. If correct, this means current multimodal LLMs cannot yet be trusted for situational awareness tasks in real crowds, even though their language output looks confident and plausible.

What carries the argument

The central object is the Where's Waldo image set used as a controlled proxy for a crowded public scene, paired with a fixed JSON prompt. The Hey-Waldo dataset supplies 18 labeled, densely populated illustrations with ground-truth Waldo locations, and the five simpler control images provide a performance baseline. The prompt asks the model to describe the scene, give Waldo's coordinates, and identify five persuadable characters with coordinates, reasons, and strategies; the authors then overlay the returned coordinates on the images and rate each response as Good, Fair, or Poor. This machinery isolates spatial reasoning and character identification from language creativity, making the benchmark the actual contribution.

What would settle it

Measure GPT-4o's reported Waldo coordinates against the ground-truth labels in the Hey-Waldo dataset; if a single run locates Waldo within a reasonable tolerance on more than a couple of the 18 images, the paper's 'consistently mislocated' conclusion fails.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that GPT-4o's visual grounding lags far behind its language generation when the scene is dense. Across 18 high-complexity Hey-Waldo images, the model never located Waldo correctly, and its character identifications often pointed to fabricated people or wrong coordinates. The model's scene descriptions were usually thematically accurate, and its persuasion strategies were creative, but these strengths did not compensate for the spatial errors. The paper interprets this as evidence that multimodal LLMs still lack the integration of visual understanding and language reasoning needed for tasks like crowd analysis, surveillance, or situational public mobilization.

Load-bearing premise

The whole test rests on the assumption that a static Where's Waldo illustration behaves like a real crowded public gathering well enough that failing at Waldo means failing at mobilization; the authors themselves note the images lack motion, facial expression, and real human interaction, so the step from cartoon to real crowd is untested.

Editorial extensions

If this is right

  • If GPT-4o cannot consistently locate a distinct target in a static cartoon, it will likely fail in noisier real-world imagery, so current multimodal LLMs should not be relied upon for crowd monitoring or public-safety decisions.
  • The Waldo benchmark can be reused to track whether future models, including test-time-compute models like o1, improve at spatial reasoning; the paper's preliminary o1 test showed no substantial improvement.
  • The gap between fluent descriptions and inaccurate locations means that simply asking an LLM about a scene can produce confidently wrong answers, a risk for any application that depends on factual visual grounding.
  • The method offers a privacy-preserving alternative to testing AI on real people, allowing researchers to probe persuasion and influence capabilities without collecting personal data.

Reading between the lines

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

  • A natural extension would be to measure localization accuracy quantitatively against the Hey-Waldo ground-truth coordinates, such as median pixel distance, rather than qualitative Good/Fair/Poor ratings, which would make the benchmark directly comparable across models.
  • The same prompting strategy could be adapted to video clips or dynamic crowd simulations, testing whether temporal changes, the element the authors note is missing, change the failure pattern.
  • Because the paper finds creative persuasion strategies even when identification fails, a follow-up could deliberately decouple the two by asking the model to plan mobilization for a pre-specified, correctly located target, measuring whether strategy quality is independent of visual grounding.
  • If the benchmark is adopted, one could predict that models trained with explicit spatial supervision, such as hybrid vision-LLM systems, will close the Waldo gap before monolithic multimodal LLMs do, since the paper points to that hybrid direction.
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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

4 major / 5 minor

Summary. The paper proposes a methodology for ethically evaluating multimodal LLMs in public-mobilization scenarios by using Where's Waldo images as proxies for crowded real-world gatherings. The authors prompt GPT-4o to describe each scene, locate Waldo with pixel coordinates, and suggest five characters who could be persuaded to dress like Waldo, along with reasons and strategies. They evaluate the model's outputs on 5 control images and 18 Hey-Waldo images using qualitative Good/Fair/Poor ratings assigned by the authors. The main findings are that GPT-4o produces vivid scene descriptions and creative persuasion strategies, but consistently fails to locate Waldo or accurately identify characters in the complex Hey-Waldo images. The authors conclude that current multimodal LLMs lack the spatial reasoning and contextual grounding needed for situational public mobilization. The paper also reports preliminary, cursory testing of OpenAI's o1 model showing no substantial improvement.

Significance. If supported, the paper's negative result would be a useful cautionary data point for the deployment of multimodal LLMs in tasks that require precise visual grounding, such as crowd monitoring or situational awareness. The use of a standard labeled dataset (Hey-Waldo) as a controlled, privacy-preserving proxy is a sensible design choice, and the paper makes an explicit, falsifiable prediction that GPT-4o cannot reliably locate a target individual in dense scenes. The prompt structure and sample JSON output are clearly presented, which aids replication. However, the central quantitative claim is not operationalized: the 'consistently mislocated' finding rests entirely on the authors' subjective ratings, with no distance threshold, no inter-rater reliability, and no comparison baseline. Likewise, the social-dynamics and persuasion aspects have no ground truth and are evaluated by the same authors who designed the prompts. The paper is therefore better viewed as a qualitative demonstration than a rigorous benchmark, and its strong conclusions about mobilization capability go beyond what the evidence supports.

major comments (4)
  1. [§III, Table II] The headline claim that 'Waldo was consistently mislocated' across the 18 Hey-Waldo images is supported only by the authors' qualitative Good/Fair/Poor ratings. No hit criterion, distance threshold, or accuracy rate is defined, and no inter-rater reliability is reported. A model that identifies Waldo's region correctly but reports coordinates off by a constant scaling factor would still be rated Poor, conflating coordinate-reporting error with identification failure. The authors should define a tolerance-based metric (e.g., Euclidean distance between predicted and ground-truth coordinates, normalized by image dimensions), report the resulting accuracy for both control and complex images, and ideally include a human or computer-vision baseline for comparison.
  2. [§II (Evaluation framework) and §III] The claims that GPT-4o 'cannot reliably assess social dynamics' and that persuasion strategies are 'creative but not feasible' rest on ratings assigned by the same authors who designed the prompts. There is no structured rubric with concrete criteria, no independent raters, and no measure of inter-rater agreement. Since the task has no objective ground truth for 'persuadability' or 'strategy effectiveness,' these conclusions should be reframed as qualitative observations or the authors should provide a scoring protocol with independent annotation and agreement statistics.
  3. [Discussion] The final conclusion that current multimodal LLMs cannot reliably perform the 'perceptual and social-analysis steps needed for situational public mobilization' overstates what the data can support. The authors themselves acknowledge in the Discussion that Where's Waldo images are static and lack temporal changes, facial expressions, and real human interactions. The results can support a claim about performance on this specific benchmark, not a general inability in real-world mobilization contexts; the abstract and conclusion should be tempered accordingly.
  4. [Data and Code Availability] The availability statement says the GitHub repository is private and access is 'restricted to verification purposes.' For an empirical study whose central finding is a negative result, the prompts, raw model responses, overlay code, and rating data should be made openly available to allow independent verification. Without such access, the 'consistently mislocated' claim cannot be checked, and the paper's reproducibility is limited.
minor comments (5)
  1. [Figure II (referenced in §II)] The text refers to 'Figure II' for the overlay visualization, but no such figure appears in the manuscript; the annotated visualizations that are said to support the qualitative ratings are therefore unavailable to the reader.
  2. [Discussion] The first sentence of the Discussion contains a grammatical error: 'an preliminary evaluation' should be 'a preliminary evaluation.'
  3. [Table I] The complexity ratings are described as color-coded, but in the printed text the color coding is not visible; explicit textual labels (Low, Medium, High) should be added to the table so that the ratings are interpretable without color.
  4. [§III] The phrase 'Across the dataset, Waldo was consistently mislocated' is ambiguous because the control images received Fair or Good ratings for Waldo location in Table II; the claim should be qualified as applying to the 18 Hey-Waldo complex images.
  5. [Figure 2] The list of references does not include a URL or access information for the 'Hey-Waldo' dataset beyond the author and year; a direct link would help readers reproduce the study.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper evaluates GPT-4o against an external ground-truth dataset and does not fit any parameter or prediction to its own inputs.

full rationale

The paper contains no derivation chain in which an output is equivalent to an input by construction. The central empirical claim that GPT-4o consistently mislocates Waldo in complex scenes is an evaluation of model outputs against the externally published Hey-Waldo dataset [15], which provides hand-labeled ground truth independent of the present authors. The Good/Fair/Poor ratings in Table II are qualitative author judgments, and the paper explicitly defines them with reference to image content (e.g., 'incorrect coordinates, fabricated descriptions, or a complete failure to identify key elements'); these ratings are not fitted parameters and are not used to construct the model outputs or the ground truth. The absence of a quantitative coordinate-tolerance metric and the lack of inter-rater reliability are correctness or reproducibility concerns, not circularity. Self-citations (e.g., refs [11, 48, 49, 51, 56, 62]) appear only as background motivation and context, and none is load-bearing for the evaluation result. The acknowledged limitation that Where's Waldo images are static proxies lacking real-world dynamics concerns external validity, not circularity. Thus no step reduces to its own inputs, and the paper is self-contained as an empirical benchmark study.

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

No numeric free parameters are fitted; the evaluation uses qualitative categorical ratings rather than fitted constants. The axioms are the dataset ground truth, the proxy validity of Waldo images, the reliability of author ratings, and the representativeness of single prompt runs. No new physical or conceptual entities are introduced.

assumptions (4)
  • domain assumption The Hey-Waldo dataset provides correct ground-truth locations for Waldo.
    The evaluation treats the dataset labels as ground truth for judging coordinate accuracy; introduced in Section II when the dataset is described.
  • ad hoc to paper Where's Waldo images are valid proxies for complex, crowded real-world gatherings for the purpose of testing public mobilization.
    This is the paper's central methodological premise, stated in the Introduction and Methodology, and acknowledged by the authors as limited because the images are static and lack real social dynamics.
  • domain assumption Qualitative Good/Fair/Poor ratings by the authors are reliable measures of the model's capabilities.
    The entire results table is based on author judgments, with no inter-rater reliability or independent annotation, as described in Section II and reported in Table II.
  • domain assumption GPT-4o's responses to one fixed prompt are representative of the model's general capability in these tasks.
    No prompt variation, temperature setting, or repeated trials are reported, so single responses are assumed representative; see the Methodology section.

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Cite this review

Pith. "Pith review of Mobilizing Waldo: Evaluating Multimodal AI for Public Mobilization." pith.science (2026). https://pith.science/paper/VAVY4CU4

@misc{pith2026241214210,
  author       = {Pith},
  title        = {Pith review of: Mobilizing Waldo: Evaluating Multimodal AI for Public Mobilization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VAVY4CU4}},
  note         = {Machine review of arXiv:2412.14210}
}
read the original abstract

Advancements in multimodal Large Language Models (LLMs), such as OpenAI's GPT-4o, offer significant potential for mediating human interactions across various contexts. However, their use in areas such as persuasion, influence, and recruitment raises ethical and security concerns. To evaluate these models ethically in public influence and persuasion scenarios, we developed a prompting strategy using "Where's Waldo?" images as proxies for complex, crowded gatherings. This approach provides a controlled, replicable environment to assess the model's ability to process intricate visual information, interpret social dynamics, and propose engagement strategies while avoiding privacy concerns. By positioning Waldo as a hypothetical agent tasked with face-to-face mobilization, we analyzed the model's performance in identifying key individuals and formulating mobilization tactics. Our results show that while the model generates vivid descriptions and creative strategies, it cannot accurately identify individuals or reliably assess social dynamics in these scenarios. Nevertheless, this methodology provides a valuable framework for testing and benchmarking the evolving capabilities of multimodal LLMs in social contexts.

Figures

Figures reproduced from arXiv: 2412.14210 by the authors.

Figure 1
Figure 1. FIG. 1: Prompt structure used for image analysis [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2: Prompt structure used for analyzing Image 1 of the Hey-Wa [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Reference graph

Works this paper leans on

67 extracted references · 56 canonical work pages

  1. [1]

    A brief description of the scene

    work page

  2. [2]

    • A brief description in words of where Waldo is located

    Waldo’s precise location with: • Coordinates [x, y] , where (0,0) is top-left. • A brief description in words of where Waldo is located

    work page

  3. [3]

    s c e n e _ d e s c r i p t i o n

    Identify 5 characters who could be persuaded to dress like Waldo. For each, provide: • Description. • Precise coordinates [x, y] . • A brief description in words of where the char- acter is located in the scene. • Why they might be easily convinced. • A strategy Waldo could use to persuade them. Please provide your response in JSON format. Note: The image...

    work page

  4. [4]

    Social media and fake news in the 2016 election

    Hunt Allcott and Matthew Gentzkow. Social media and fake news in the 2016 election. Journal of economic per- spectives, 31(2):211–236, 2017

    work page 2016

  5. [5]

    Palm 2 technical report

    Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. Palm 2 technical report. arXiv preprint arXiv:2305.10403, 2023

    arXiv 2023

  6. [6]

    S. Aral. The Hype Machine: How Social Media Disrupts Our Elections, Our Economy and Our Health – and How We Must Adapt . HarperCollins, 2020

    work page 2020

  7. [7]

    Stewardship of global collective behavior

    Joseph B Bak-Coleman, Mark Alfano, Wolfram Barfuss, Carl T Bergstrom, Miguel A Centeno, Iain D Couzin, Jonathan F Donges, Mirta Galesic, Andrew S Gersick, Jennifer Jacquet, et al. Stewardship of global collective behavior. Proceedings of the National Academy of Sci- ences, 118(27):e2025764118, 2021

    work page 2021

  8. [8]

    Bleakley

    P. Bleakley. Panic, pizza and mainstreaming the alt- right: A social media analysis of pizzagate and the rise of the qanon conspiracy. Curr. Sociol., 71:509–525, 2023

    work page 2023

Show all 67 references
  1. [9]

    End to end learning for self-driving cars

    Mariusz Bojarski. End to end learning for self-driving cars. 2016

    2016

  2. [10]

    The ma- licious use of artificial intelligence: Forecasting, preve n- tion, and mitigation

    Miles Brundage, Shahar Avin, Jack Clark, Helen Toner, Peter Eckersley, Ben Garfinkel, Allan Dafoe, Paul Scharre, Thomas Zeitzoff, Bobby Filar, et al. The ma- licious use of artificial intelligence: Forecasting, preve n- tion, and mitigation. arXiv preprint arXiv:1802.07228 , 2018. 10

    2018 arXiv

  3. [11]

    Sparks of artificial general intelligence: Early experiments with gpt-4

    S´ ebastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of artificial general intelligence: Early experiments with gpt-4. In 37th Conference on Neural Information Pro- ces...

    2023

  4. [12]

    Burtell and T

    M. Burtell and T. Woodside. Artificial influence: An analysis of ai-driven persuasion. arXiv, cs.CY, 2023

    2023

  5. [13]

    Cao and M

    X. Cao and M. Kosinski. Large language models know how the personality of public figures is perceived by the general public. Sci. Rep. , 14:6735, 2024

    2024

  6. [14]

    Proof of con- cept for ai-driven alternate and augmented reality games

    Manuel Cebrian and Niccolo Pescetelli. Proof of con- cept for ai-driven alternate and augmented reality games. Available at SSRN 4788741 , 2024

    2024

  7. [15]

    Chatsiou and S

    K. Chatsiou and S. J. Mikhaylov. Deep learning for po- litical science. arXiv, cs.CL, 2020

    2020

  8. [16]

    Deepfakes and the new disinformation war

    Danielle K Citron and Robert Chesney. Deepfakes and the new disinformation war. Foreign Affairs , 2019

    2019

  9. [17]

    K. M. Colby. Computer simulation of a neurotic process. Computer simulation of personality: Frontier of psycho- logical research. 1963

    1963

  10. [18]

    Hey-waldo: Labeled images of the where’s waldo puzzle for use in classification and im- age recognition problems, 2017

    Valentino Constantinou. Hey-waldo: Labeled images of the where’s waldo puzzle for use in classification and im- age recognition problems, 2017. Accessed: 2024-11-19

    2017

  11. [19]

    S. J. Cranmer. Machine learning in political science. https://www.cambridge.org/core/services/aop-file- manager/file/5c348274e401b41903dae11b/PAN-VSI- Intro-0119-Machine-learning.pdf, 2019

    2019

  12. [20]

    A. R. Doshi and O. P. Hauser. Generative AI enhances in- dividual creativity but reduces the collective diversity o f novel content. Science Advances, 10(28):eadn5290, 2024

    2024

  13. [21]

    Palm-e: An embodied multimodal language model

    Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, et al. Palm-e: An embodied multimodal language model. arXiv preprint arXiv:2303.03378 , 2023

    2023 arXiv

  14. [22]

    Growing Artificial Societies: Social Science from the Bottom Up

    Joshua M Epstein. Growing Artificial Societies: Social Science from the Bottom Up . The Brookings Institution Press, 1996

    1996

  15. [23]

    Z. et al. Epstein. Towards a new social laboratory: An experimental study of search through community partic- ipation at burning man. arXiv, cs.SI, 2019

    2019

  16. [24]

    S. et al. Feuerriegel. Research can help to tackle ai- generated disinformation. Nat Hum Behav , 7:1818–1821, 2023

    2023

  17. [25]

    Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommen- dations

    Luciano Floridi, Josh Cowls, Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, et al. Ai4people—an ethical framework for a good ai society: opportunities, risks, principles, and recommen- datio...

    2018

  18. [26]

    On the cre- ativity of large language models

    Giorgio Franceschelli and Mirco Musolesi. On the cre- ativity of large language models. AI & SOCIETY , pages 1–11, 2024

    2024

  19. [27]

    Democracy, Lead- ership and Governance–Application of Artificial Intelli- gence: A Machine-generated Overview

    K Gireesan and Jos Chathukulam. Democracy, Lead- ership and Governance–Application of Artificial Intelli- gence: A Machine-generated Overview . Springer Nature, 2024

    2024

  20. [28]

    Context-guided spatio-temporal video ground- ing

    Xin Gu, Heng Fan, Yan Huang, Tiejian Luo, and Libo Zhang. Context-guided spatio-temporal video ground- ing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 18330– 18339, 2024

    2024

  21. [29]

    Language models hallucinate, but may ex- cel at fact verification

    Jian Guan, Jesse Dodge, David Wadden, Minlie Huang, and Hao Peng. Language models hallucinate, but may ex- cel at fact verification. arXiv preprint arXiv:2310.14564 , 2023

    2023 arXiv

  22. [30]

    K. Harvey. Encyclopedia of Social Media and Politics . SAGE Publications, 2013

    2013

  23. [31]

    Hinds, E

    J. Hinds, E. J. Williams, and A. N. Joinson. ‘it wouldn’t happen to me’: Privacy concerns and perspectives fol- lowing the cambridge analytica scandal. Int. J. Hum. Comput. Stud. , 143:102498, 2020

    2020

  24. [32]

    Computer vision for autonomous vehicles: Prob- lems, datasets and state of the art

    Joel Janai, Fatma G¨ uney, Aseem Behl, Andreas Geiger, et al. Computer vision for autonomous vehicles: Prob- lems, datasets and state of the art. Foundations and Trends® in Computer Graphics and Vision , 12(1–3):1– 308, 2020

    2020

  25. [33]

    Survey of hallucination in natural language generation

    Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. Survey of hallucination in natural language generation. Acm computing surveys , 55(12):1–38, 2023

    2023

  26. [34]

    The global landscape of ai ethics guidelines

    Anna Jobin, Marcello Ienca, and Effy Vayena. The global landscape of ai ethics guidelines. Nature machine intel- ligence, 1(9):389–399, 2019

    2019

  27. [35]

    Chatgpt for good? on opportunities and challenges of large language models for education

    Enkelejda Kasneci, Kathrin Seßler, Stefan K¨ uchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan G¨ unnemann, Eyke H¨ ullermeier, et al. Chatgpt for good? on opportunities and challenges of large language models for education. Learning an...

    2023

  28. [36]

    There’s waldo: Us- ing deep learning to find waldo

    Brad Kenstler. There’s waldo: Us- ing deep learning to find waldo. https://github.com/bckenstler/TheresWaldo, 2017. Accessed: 2024-12-17

    2017

  29. [37]

    Kertysova

    K. Kertysova. Artificial intelligence and disinformation: How ai changes the way disinformation is produced, dis- seminated, and can be countered. Security and Human Rights, 29:55–81, 2018

    2018

  30. [38]

    Kosinski

    M. Kosinski. Evaluating large language models in theory of mind tasks. arXiv e-prints , arXiv:2302.02083, 2023

    2023 arXiv

  31. [39]

    D. et al. Lazer. Computational social science. Science, 323:721–723, 2009

    2009

  32. [40]

    BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models

    Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597 , 2023

    2023 arXiv

  33. [41]

    Towards understanding and mit- igating social biases in language models

    Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. Towards understanding and mit- igating social biases in language models. In Interna- tional Conference on Machine Learning , pages 6565–

  34. [42]

    Survey on deep learning-based 3d object detection in autonomous driv- ing

    Zhenming Liang and Yingping Huang. Survey on deep learning-based 3d object detection in autonomous driv- ing. Transactions of the Institute of Measurement and Control, 45(4):761–776, 2023

    2023

  35. [43]

    A transformer-based multi-modal fusion net- work for semantic segmentation of high-resolution remote sensing imagery

    Yutong Liu, Kun Gao, Hong Wang, Zhijia Yang, Pengyu Wang, Shijing Ji, Yanjun Huang, Zhenyu Zhu, and Xi- aobin Zhao. A transformer-based multi-modal fusion net- work for semantic segmentation of high-resolution remote sensing imagery. International Journal of Applied Earth Obse...

    2024

  36. [44]

    A. Loth, M. Kappes, and M.-O. Pahl. Blessing or curse? a survey on the impact of generative ai on fake news. arXiv, cs.CL, 2024

    2024

  37. [45]

    How to find wally with a neural network

    Tadej Magajna. How to find wally with a neural network. Towards Data Science , 2017. 11

    2017

  38. [46]

    From statistical relational to neurosymbolic artificial intelligence: A survey

    Giuseppe Marra, Sebastijan Dumanˇ ci´ c, Robin Man- haeve, and Luc De Raedt. From statistical relational to neurosymbolic artificial intelligence: A survey. Artificial Intelligence, page 104062, 2024

    2024

  39. [47]

    F. Matos. ECIAIR 2021 3rd European Conference on the Impact of Artificial Intelligence and Robotics . Academic Conferences and publishing limited, 2021

    2021

  40. [48]

    Commonsense reasoning: an event cal- culus based approach

    Erik T Mueller. Commonsense reasoning: an event cal- culus based approach . Morgan Kaufmann, 2014

    2014

  41. [49]

    Gpt-4o system card, 2024

    OpenAI. Gpt-4o system card, 2024. Accessed: 2024-12- 08

    2024

  42. [50]

    Openai o1 system card, 2024

    OpenAI. Openai o1 system card, 2024. Accessed: 2024- 12-08

    2024

  43. [51]

    Pescetelli, M

    N. Pescetelli, M. Cebrian, and I. Rahwan. Beeme: Real- time internet control of situated human agents. Com- puter, 53:49–58, 2020

    2020

  44. [52]

    Time- critical social mobilization

    Galen Pickard, Wei Pan, Iyad Rahwan, Manuel Cebrian, Riley Crane, Anmol Madan, and Alex Pentland. Time- critical social mobilization. Science, 334(6055):509–512, 2011

    2011

  45. [53]

    A sur- vey of hallucination in large foundation models

    Vipula Rawte, Amit Sheth, and Amitava Das. A sur- vey of hallucination in large foundation models. arXiv preprint arXiv:2309.05922 , 2023

    2023 arXiv

  46. [54]

    Targeted social mobilization in a global man- hunt

    Alex Rutherford, Manuel Cebrian, Iyad Rahwan, Sohan Dsouza, James McInerney, Victor Naroditskiy, Matteo Venanzi, Nicholas R Jennings, JR DeLara, Eero Wahlst- edt, et al. Targeted social mobilization in a global man- hunt. PloS one , 8(9):e74628, 2013

    2013

  47. [55]

    Salvi, M

    F. Salvi, M. H. Ribeiro, R. Gallotti, and R. West. On the conversational persuasiveness of large language models: A randomized controlled trial. arXiv, cs.CY, 2024

    2024

  48. [56]

    Deep generative models on 3d rep- resentations: A survey

    Zifan Shi, Sida Peng, Yinghao Xu, Andreas Geiger, Yiyi Liao, and Yujun Shen. Deep generative models on 3d rep- resentations: A survey. arXiv preprint arXiv:2210.15663 , 2022

    2022 arXiv

  49. [57]

    An empirical analy- sis on spatial reasoning capabilities of large multimodal models

    Fatemeh Shiri, Xiao-Yu Guo, Mona Golestan Far, Xin Yu, Reza Haf, and Yuan-Fang Li. An empirical analy- sis on spatial reasoning capabilities of large multimodal models. In Proceedings of the 2024 Conference on Em- pirical Methods in Natural Language Processing , pages 21440–21...

    2024

  50. [58]

    Simchon, M

    A. Simchon, M. Edwards, and S. Lewandowsky. The persuasive effects of political microtar- geting in the age of generative ai. Preprint, https://doi.org/10.1073/pnas.XXXXXXXXXX, 2023

    2023 doi

  51. [59]

    Stefanovitch, A

    N. Stefanovitch, A. Alshamsi, M. Cebrian, and I. Rah- wan. Error and attack tolerance of collective problem solving: The darpa shredder challenge. EPJ Data Sci- ence, 3:13, 2014

    2014

  52. [60]

    Technology, autonomy, and manipulation

    Daniel Susser, Beate Roessler, and Helen Nissenbaum. Technology, autonomy, and manipulation. Internet Pol- icy Review , 8(4), 2019

    2019

  53. [61]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 , 2023

    2023 arXiv

  54. [62]

    A. Unver. Artificial intelligence, authoritarianism and the future of political systems. 2018

    2018

  55. [63]

    Taxonomy of risks posed by language models

    Laura Weidinger, Jonathan Uesato, Madeleine Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, et al. Taxonomy of risks posed by language models. Com- munications of the ACM , 66(10):145–155, 2023

    2023

  56. [64]

    AI now report 2018

    Meredith Whittaker, Kate Crawford, Roel Dobbe, Genevieve Fried, Elizabeth Kaziunas, Varoon Mathur, Sarah Mysers West, Rashida Richardson, Jason Schultz, Oscar Schwartz, et al. AI now report 2018 . AI Now Institute at New York University New York, 2018

    2018

  57. [65]

    Yanardag, M

    P. Yanardag, M. Cebrian, and I. Rahwan. Shelley: A crowd-sourced collaborative horror writer. In Proceedings of the 13th Conference on Creativity and Cognition , pages 1–8. Association for Computing Machinery, New York, NY, USA, 2021

    2021

  58. [66]

    The dawn of lmms: Preliminary explorations with gpt-4v (ision)

    Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang. The dawn of lmms: Preliminary explorations with gpt-4v (ision). arXiv preprint arXiv:2309.17421 , 2023

    2023 arXiv

  59. [67]

    Online po- litical microtargeting: Promises and threats for democ- racy

    Frederik Zuiderveen Borgesius, Judith M¨ oller, Sanne Kruikemeier, Ronan ´O Fathaigh, Kristina Irion, Tom Dobber, Balazs Bodo, and Claes H de Vreese. Online po- litical microtargeting: Promises and threats for democ- racy. Utrecht Law Review , 14(1):82–96, 2018

    2018

Pith tools

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