Pith. sign in

REVIEW 1 major objections 4 minor 135 references

Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report

T0 review · 1 major / 4 minor · reviewed 2026-08-11 · deepseek-v4-flash

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

Pith's one-line read The report's central claim: five deep-learning research lines, from LLM-driven game agents to generative world models from unlabelled video, are the most encouraging near-term directions for AI in digital gaming.

desk verdict A competent, honest exploratory report that maps five AI-in-gaming directions; useful as a briefing, not a research contribution. read the letter →

arxiv 2412.14085 v1 pith:PDILH3ME submitted 2024-12-18 cs.LG cs.AIcs.HC

Markus Dablander This is my paper · ORCID
classification cs.LGcs.AIcs.HC
keywords largelanguagemodelsgameagentmodellingneuralcellularautomataproceduralcontentgenerationdeepsurrogateself-supervisedlearningstateembeddingsgenerativeworld
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 exploratory report argues that five deep-learning research lines are the most promising near-term directions for AI in digital gaming: LLM-based game agents, neural cellular automata for procedural content generation, deep surrogate models for expensive in-game simulations, self-supervised game state embeddings, and generative world models trained from unlabelled video. It offers no new experiments, datasets, or theorems; its contribution is a curated roadmap assembled from existing results and aimed at inspiring more rigorous research. A reader should care because the report converts scattered early demonstrations into a small set of tractable directions, while also listing the technical obstacles that currently block adoption in real game development.

What carries the argument

The organizing mechanism is a three-way taxonomy of AI-for-game applications—agent modelling, procedural content generation, and player modelling—drawn from [20], which the report uses to position its five avenues. Within each avenue, a specific technical object carries the argument: an LLM-based cognitive architecture with perception, memory, thinking, action, role-playing, and learning modules; a neural cellular automaton, meaning a cellular automaton whose local transition rule is a trained neural network; a deep surrogate model, a network $\Phi_\theta$ trained on input/output pairs from an expensive function $f$ to approximate or optimise it at far lower cost; a joint-embedding predictive architecture, in which an encoder maps a current state to an embedding and a predictor forecasts the embedding of a related state given a latent variable; and a tokeniser/latent-action/dynamics-model stack that turns unlabelled video into an interactive world. The report argues that each mechanism addresses a known weakness of prior approaches, such as lack of control in cellular automata or the prohibitive cost of repeated simulation.

What would settle it

A systematic, criteria-driven literature review with a scoring rubric could settle the selection claim: if a direction outside these five, such as quality-diversity optimisation or deep reinforcement learning for game balance, consistently ranks above one of the five on demonstrated results, the roadmap is not representative.

Watch

Extended reading notes

Core claim

On the report's own terms, its central claim is that five research avenues currently offer the most encouraging openings for applying deep learning to digital games: LLMs as the cognitive core of game agents; neural cellular automata as controllable generators of game content; deep surrogate models that approximate expensive in-game simulations; self-supervised learning to produce reusable game state embeddings; and generative interactive-world models trained from unlabelled video. Each avenue is anchored to existing evidence, such as an LLM-based multi-agent society with memory streams, level-generating neural cellular automata, surrogate models that accelerate environment generation, benchmarked self-supervised game state representations, and an 11-billion-parameter world model that learns latent actions without labels. The report does not claim to prove these are the best directions; it presents the list as a curated, necessarily subjective selection intended to inspire more rigorous work, and it pairs the list with an explicit catalog of technical challenges that currently limit deployment.

Load-bearing premise

The value of the report rests on the author's own selection of these five avenues being a fair representation of the most promising research directions, a premise the report explicitly labels as curated and necessarily subjective rather than evidence-based.

Editorial extensions

If this is right

  • LLM-based agents with persistent memory streams would make unscripted dialogue and emergent social interaction among NPCs a default feature rather than a hand-coded exception.
  • Neural cellular automata trained with constraint-aware loss functions could generate playable levels, textures, and regenerative objects while keeping the designer's requirements as part of the optimisation objective.
  • Deep surrogates trained on costly in-game simulations could cut evaluation time by orders of magnitude, making tasks such as level balancing and automated playtesting feasible at design time.
  • Self-supervised embeddings learned from pixels alone could serve as a common perception layer for many downstream tasks, from agent control and player affect prediction to game-state description.
  • Interactive world models trained from unlabelled video could let a user seed a game from a single image and play it, turning video libraries into a renewable source of game content.

Reading between the lines

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

  • The five avenues could be combined rather than pursued separately: an LLM agent could be trained or evaluated inside worlds generated by a latent-action world model, with self-supervised embeddings as the perception layer; the report does not spell out these couplings.
  • The report's own 'curated and necessarily subjective' caveat suggests the natural next step is a systematic, criterion-based survey or a community-ranked map; until then, the practical value of the roadmap is a hypothesis to test, not an established ranking.
  • For game developers, the least risky near-term adoption is likely in offline and pre-production tasks such as content generation, simulation acceleration, and playtest analysis, rather than in real-time player-facing AI, because the report's own challenge list places runtime efficiency and debuggability as unresolved.
  • The latent action model idea points to a concrete testable extension: measuring whether actions inferred from unlabelled gameplay video align with the action vocabulary of a real game engine, which would determine whether world models can be plugged into existing games.
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

1 major / 4 minor

Summary. This exploratory report identifies and discusses five research avenues for applying deep learning to digital gaming: (i) LLM-based game agent modelling, (ii) neural cellular automata for procedural content generation, (iii) deep surrogate models to accelerate in-game simulations, (iv) self-supervised game state representation learning, and (v) generative world models trained from unlabelled video. The paper explicitly frames itself as a curated, non-exhaustive, and subjective collection of ideas rather than a systematic survey or a presentation of new empirical results. Each avenue is described with reference to representative prior work, and a final section summarizes technical challenges such as computational cost, interpretability, data requirements, and integration into game development workflows.

Significance. Judged on its own terms, the report is a competently written and useful roadmap for researchers entering the AI-gaming intersection. Its factual descriptions of cited works are largely accurate, and it is commendably transparent about its scope, its speculative nature, and its commissioned context. The central claim is not a formal result but a curated judgment of promising directions; the paper states this limitation clearly in Sections 1 and 8, so the absence of a systematic selection methodology does not undermine the claim as presented. The main value of the report is as an accessible overview and idea generator; it makes no new empirical or theoretical contribution, so its significance is modest but legitimate for an exploratory report.

major comments (1)
  1. [2] The paper's central claim is explicitly framed as a 'curated and necessarily subjective collection of ideas' (Section 1) and the report does not claim exhaustiveness or ranking. The five selected avenues are each supported by credible representative citations, and the limitations are acknowledged in Section 7. I therefore identified no load-bearing technical error requiring major revision.
minor comments (4)
  1. [2, first paragraph] The text attributes Llama 3 to Google ('Google's Llama 3 [34]'), but the cited reference [34] is the Meta AI 'Llama 3 herd of models'; this should be corrected to Meta.
  2. [5, second paragraph] The statement that 'A literature search for studies that use concepts from self-supervised learning in digital gaming revealed only 12 instances' would benefit from a brief description of the search strategy, databases consulted, and search date, or from softer phrasing such as 'a non-systematic search identified 12 instances'. As written, the count cannot be independently verified and may mislead readers into treating it as a comprehensive enumeration.
  3. [6, 'Genie 2' paragraph] The description of Genie 2 relies on a public announcement rather than a peer-reviewed technical report; the paper does acknowledge this, but the sentence 'It has been stated, however, that Genie 2 is an autoregressive latent diffusion model' could more explicitly attribute the claim to DeepMind's announcement to avoid giving it the same evidentiary weight as the peer-reviewed Genie 1 description.
  4. [References] Reference [113] is malformed: it appears as 'Super Mario as a string: Platformer level generation via LSTMs, author=Summerville, Adam and Mateas, Michael, journal=...' with a visible 'author=' field. This should be reformatted in the standard style used by the other entries.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the report is an explicitly curated, non-derivational survey with no fitted predictions and no load-bearing self-citation.

full rationale

This paper makes no technical derivation that could reduce to its own inputs. It explicitly states in Section 1 that its objective 'is not to provide a comprehensive set of mature research proposals, or to present novel original research findings,' and that the five avenues 'represent a curated and necessarily subjective collection of ideas.' Because the central claim is a subjective, non-exhaustive selection of promising research directions, there is no prediction, fitted parameter, or uniqueness theorem whose content is imported from the authors' prior work. The supporting evidence for each avenue consists of external prior work (e.g., Park et al. for LLM agents, Earle et al. for neural cellular automata, Bhatt et al. for deep surrogates, Anand et al. for self-supervised embeddings, and Bruce et al. for Genie), not self-citations by the author. The formal definitions in Section 4 (the surrogate function f and network Φθ) are expository and are not used to derive a result that is equivalent to an input. The limitations acknowledged in Section 7 temper the report's claims but do not create circularity. The only identified issue, the attribution of Llama 3 to Google rather than Meta, is a factual accuracy concern outside the circularity framework. Overall, the report is self-contained as a survey and does not engage in circular reasoning.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The report introduces no fitted parameters or new entities. Its dependencies are the cited literature and the author's judgement about promising directions.

assumptions (3)
  • ad hoc to paper The five selected research avenues are the most promising directions at the AI-gaming intersection.
    This is the central judgement of the report. It is not derived from a systematic survey or ranking; the abstract states the list is 'curated, non-exhaustive', and Section 1 states it is a 'subjective collection of ideas'.
  • domain assumption The cited prior works accurately represent the state of the art.
    The report's descriptions of existing approaches rest on the cited literature being representative and correctly interpreted. This is a standard assumption for a review.
  • ad hoc to paper Genie 2 capabilities described from a public announcement are trustworthy.
    Section 6 relies on Google DeepMind's blog post (reference 110) for Genie 2, since no technical paper existed at submission time. The report itself flags this as 'early stage' and lacking a research paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report." pith.science (2026). https://pith.science/paper/PDILH3ME

@misc{pith2026241214085,
  author       = {Pith},
  title        = {Pith review of: Future Research Avenues for Artificial Intelligence in Digital Gaming: An Exploratory Report},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PDILH3ME}},
  note         = {Machine review of arXiv:2412.14085}
}
read the original abstract

Video games are a natural and synergistic application domain for artificial intelligence (AI) systems, offering both the potential to enhance player experience and immersion, as well as providing valuable benchmarks and virtual environments to advance AI technologies in general. This report presents a high-level overview of five promising research pathways for applying state-of-the-art AI methods, particularly deep learning, to digital gaming within the context of the current research landscape. The objective of this work is to outline a curated, non-exhaustive list of encouraging research directions at the intersection of AI and video games that may serve to inspire more rigorous and comprehensive research efforts in the future. We discuss (i) investigating large language models as core engines for game agent modelling, (ii) using neural cellular automata for procedural game content generation, (iii) accelerating computationally expensive in-game simulations via deep surrogate modelling, (iv) leveraging self-supervised learning to obtain useful video game state embeddings, and (v) training generative models of interactive worlds using unlabelled video data. We also briefly address current technical challenges associated with the integration of advanced deep learning systems into video game development, and indicate key areas where further progress is likely to be beneficial.

Figures

Figures reproduced from arXiv: 2412.14085 by the authors.

Figure 1
Figure 1. Simple, high-level overview of a conceivable LLM-based cognitive architecture for a video [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Conceptual diagram of how a neural cellular automaton (NCA), once trained, could [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Illustration of the elementary idea behind deep surrogate modelling: A computationally [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Schematic overview of a prototypical joint-embedding predictive architecture (JEPA) [ [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Schematic diagram illustrating the inference process of the trained Genie model [ [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

135 extracted references · 46 canonical work pages

  1. [1]

    Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 2012

    2012

  2. [2]

    Very deep convolutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014

    arXiv 2014

  3. [3]

    Zeiler and Rob Fergus

    Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional networks. Proceedings of the European Conference on Computer Vision, pages 818–833, 2014

    2014

  4. [4]

    Going deeper with convolutions

    Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1–9, 2015

    2015

  5. [5]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 770–778, 2016

    2016

  6. [6]

    Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al

    Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V . Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016

    arXiv 2016

  7. [7]

    Gomez, Łukasz Kaiser, and Illia Polosukhin

    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

  8. [8]

    Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165, 2020

    arXiv 2005

Show all 135 references
  1. [9]

    Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140):1–67, 2020

    2020

  2. [10]

    Kingma and Max Welling

    Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114, 2013

    2013 arXiv

  3. [11]

    Generative adversarial nets

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2014

    2014

  4. [12]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022

    2022

  5. [13]

    Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33, 2020

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33, 2020

    2020

  6. [14]

    Schoenholz, Patrick F

    Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neu- ral message passing for quantum chemistry. International Conference on Machine Learning, 70, 2017. 5/eHomepage | X 13

    2017

  7. [15]

    Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D

    Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, and Alán Aspuru-Guzik. Automatic chemical design using a data- driven continuous ...

    2018

  8. [16]

    Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P

    David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams. Convolutional networks on graphs for learning molecular fingerprints. Advances in Neural Information Processing Systems, 28, 2015

    2015

  9. [17]

    How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018

    Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018

    2018 arXiv

  10. [18]

    Highly accurate protein structure prediction with AlphaFold

    John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ron- neberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873):583–589, 2021

    2021

  11. [19]

    Games for artificial intelligence research: A review and perspectives

    Chengpeng Hu, Yunlong Zhao, Ziqi Wang, Haocheng Du, and Jialin Liu. Games for artificial intelligence research: A review and perspectives. IEEE Transactions on Artificial Intelligence, 2024

    2024

  12. [20]

    Yannakakis and Julian Togelius.Artificial intelligence and games

    Georgios N. Yannakakis and Julian Togelius.Artificial intelligence and games. Springer, 2018. https://gameaibook.org

    2018

  13. [21]

    Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al

    David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587): 484–489, 2016

    2016

  14. [22]

    Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H

    Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H. Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al. Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782): 350–354, 2019

    2019

  15. [23]

    Dota 2 with large scale deep reinforcement learning

    Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemysław D˛ ebiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, et al. Dota 2 with large scale deep reinforcement learning. arXiv preprint arXiv:1912.06680, 2019

    1912 arXiv

  16. [24]

    Multi-game decision transformers

    Kuang-Huei Lee, Ofir Nachum, Mengjiao Sherry Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Winnie Xu, Eric Jang, Henryk Michalewski, et al. Multi-game decision transformers. Advances in Neural Information Processing Systems, 35, 2022

    2022

  17. [25]

    A generalist agent

    Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al. A generalist agent. arXiv preprint arXiv:2205.06175, 2022

    2022 arXiv

  18. [26]

    Rusu, Joel Veness, Marc G

    V olodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015

    2015

  19. [27]

    Bryant, Ryan Cornelius, Igor V

    Risto Miikkulainen, Bobby D. Bryant, Ryan Cornelius, Igor V . Karpov, Kenneth O. Stanley, and Chern Han Yong. Computational intelligence in games. Computational Intelligence: Principles and Practice, pages 155–191, 2006

    2006

  20. [28]

    Three states and a plan: The AI of FEAR

    Jeff Orkin. Three states and a plan: The AI of FEAR. Game Developers Conference, 2006:4, 2006

    2006

  21. [29]

    Action-based character AI in video-games with CogBots architecture: A preliminary report

    Davide Aversa and Stavros Vassos. Action-based character AI in video-games with CogBots architecture: A preliminary report. arXiv preprint arXiv:1307.3195, 2013

    2013 arXiv

  22. [30]

    The AI systems of Left 4 Dead

    Michael Booth. The AI systems of Left 4 Dead. Artificial Intelligence and Interactive Digital Entertainment Conference at Stanford, 2009. 14

    2009

  23. [31]

    Noor Shaker, Julian Togelius, and Mark J. Nelson. Procedural content generation in games. Springer, 2016

    2016

  24. [32]

    Yannakakis, Pieter Spronck, Daniele Loiacono, and Elisabeth André

    Georgios N. Yannakakis, Pieter Spronck, Daniele Loiacono, and Elisabeth André. Player modeling. Dagstuhl Publishing, 2013

    2013

  25. [33]

    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

  26. [34]

    The Llama 3 herd of models

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. The Llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  27. [35]

    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

  28. [36]

    A survey of LLM datasets: From autoregressive model to AI chatbot

    Fei Du, Xin-Jian Ma, Jing-Ru Yang, Yi Liu, Chao-Ran Luo, Xue-Bin Wang, Hai-Ou Jiang, and Xiang Jing. A survey of LLM datasets: From autoregressive model to AI chatbot. Journal of Computer Science and Technology, 39(3):542–566, 2024

    2024

  29. [37]

    Yannakakis

    Roberto Gallotta, Graham Todd, Marvin Zammit, Sam Earle, Antonios Liapis, Julian Togelius, and Georgios N. Yannakakis. Large language models and games: A survey and roadmap. arXiv preprint arXiv:2402.18659, 2024

    2024 arXiv

  30. [38]

    Large language models and video games: A preliminary scoping review

    Penny Sweetser. Large language models and video games: A preliminary scoping review. Proceedings of the 6th ACM Conference on Conversational User Interfaces, pages 1–8, 2024

    2024

  31. [39]

    MarioGPT: Open-ended text2level generation through large language models

    Shyam Sudhakaran, Miguel González-Duque, Matthias Freiberger, Claire Glanois, Elias Najarro, and Sebastian Risi. MarioGPT: Open-ended text2level generation through large language models. Advances in Neural Information Processing Systems, 36, 2024

    2024

  32. [40]

    V oyager: An open-ended embodied agent with large language models

    Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. V oyager: An open-ended embodied agent with large language models. arXiv preprint arXiv:2305.16291, 2023

    2023 arXiv

  33. [41]

    Leveraging the OPT large language model for sentiment analysis of game reviews

    Markos Viggiato and Cor-Paul Bezemer. Leveraging the OPT large language model for sentiment analysis of game reviews. IEEE Transactions on Games, 2023

    2023

  34. [42]

    Towards automated video game commentary using generative AI

    Noah Ranella and Markus Eger. Towards automated video game commentary using generative AI. EXAG@ AIIDE, 2023

    2023

  35. [43]

    Chatter generation through language models

    Matthias Müller-Brockhausen, Giulio Barbero, and Mike Preuss. Chatter generation through language models. IEEE Conference on Games, pages 1–6, 2023

    2023

  36. [44]

    Bernstein

    Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior.Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pages 1–22, 2023

    2023

  37. [45]

    What if Red can talk? Dynamic dialogue generation using large language models

    Navapat Nananukul and Wichayaporn Wongkamjan. What if Red can talk? Dynamic dialogue generation using large language models. arXiv preprint arXiv:2407.20382, 2024

    2024 arXiv

  38. [46]

    A survey on large language model-based game agents

    Sihao Hu, Tiansheng Huang, Fatih Ilhan, Selim Tekin, Gaowen Liu, Ramana Kompella, and Ling Liu. A survey on large language model-based game agents. arXiv preprint arXiv:2404.02039, 2024

    2024 arXiv

  39. [47]

    Generative AI vs

    Ben Goertzel. Generative AI vs. AGI: The cognitive strengths and weaknesses of modern LLMs. arXiv preprint arXiv:2309.10371, 2023

    2023 arXiv

  40. [48]

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

    Sébastien 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. arXiv preprint arXiv:2303.12712, 2023. 15

    2023 arXiv

  41. [49]

    University of Illinois Press Urbana, 1966

    John V on Neumann and Arthur Walter Burks.Theory of self-reproducing automata. University of Illinois Press Urbana, 1966

    1966

  42. [50]

    A brief history of cellular automata

    Palash Sarkar. A brief history of cellular automata. ACM Computing Surveys, 32(1):80–107, 2000

    2000

  43. [51]

    Mathematical games

    Martin Gardner. Mathematical games. Scientific American, 222(6):132–140, 1970

    1970

  44. [52]

    Yannakakis, and Julian Togelius

    Lawrence Johnson, Georgios N. Yannakakis, and Julian Togelius. Cellular automata for real- time generation of infinite cave levels. Proceedings of the Workshop on Procedural Content Generation in Games, pages 1–4, 2010

    2010

  45. [53]

    Procedural maze level generation with evolutionary cellular automata

    Chad Adams and Sushil Louis. Procedural maze level generation with evolutionary cellular automata. IEEE Symposium Series on Computational Intelligence, pages 1–8, 2017

    2017

  46. [54]

    Schuster

    Jonathan Devlin and Micah D. Schuster. Probabilistic cellular automata for granular media in video games. The Computer Games Journal, 10(1):111–120, 2021

    2021

  47. [55]

    Realtime procedural terrain generation

    Jacob Olsen. Realtime procedural terrain generation. 2004

    2004

  48. [56]

    Sensitive dependence on initial conditions for cellular automata

    Jesús Urıas, Raúl Rechtman, and Agustın Enciso. Sensitive dependence on initial conditions for cellular automata. Chaos: An Interdisciplinary Journal of Nonlinear Science, 7(4):688–693, 1997

    1997

  49. [57]

    Wulff and J

    N. Wulff and J. A. Hertz. Learning cellular automaton dynamics with neural networks. Advances in Neural Information Processing Systems, 5, 1992

    1992

  50. [58]

    Ca-NEAT: Evolved compositional pattern producing networks for cellular automata morphogenesis and replication

    Stefano Nichele, Mathias Berild Ose, Sebastian Risi, and Gunnar Tufte. Ca-NEAT: Evolved compositional pattern producing networks for cellular automata morphogenesis and replication. IEEE Transactions on Cognitive and Developmental Systems, 10(3):687–700, 2017

    2017

  51. [59]

    Growing neural cellular automata

    Alexander Mordvintsev, Ettore Randazzo, Eyvind Niklasson, and Michael Levin. Growing neural cellular automata. Distill, 2020. doi: 10.23915/distill.00023. https://distill.pub/ 2020/growing-ca

    2020 doi

  52. [60]

    Fontaine, Stefanos Nikolaidis, and Julian Togelius

    Sam Earle, Justin Snider, Matthew C. Fontaine, Stefanos Nikolaidis, and Julian Togelius. Illuminating diverse neural cellular automata for level generation. Proceedings of the Genetic and Evolutionary Computation Conference, pages 68–76, 2022

    2022

  53. [61]

    µNCA: Texture generation with ultra-compact neural cellular automata

    Alexander Mordvintsev and Eyvind Niklasson. µNCA: Texture generation with ultra-compact neural cellular automata. arXiv preprint arXiv:2111.13545, 2021

    2021 arXiv

  54. [62]

    DyNCA: Real-time dynamic texture synthesis using neural cellular automata

    Ehsan Pajouheshgar, Yitao Xu, Tong Zhang, and Sabine Süsstrunk. DyNCA: Real-time dynamic texture synthesis using neural cellular automata. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 20742–20751, 2023

    2023

  55. [63]

    Growing 3D artefacts and functional machines with neural cellular automata

    Shyam Sudhakaran, Djordje Grbic, Siyan Li, Adam Katona, Elias Najarro, Claire Glanois, and Sebastian Risi. Growing 3D artefacts and functional machines with neural cellular automata. Artificial Life Conference Proceedings 33, 2021(1):108, 2021

    2021

  56. [64]

    Mesh neural cellular automata

    Ehsan Pajouheshgar, Yitao Xu, Alexander Mordvintsev, Eyvind Niklasson, Tong Zhang, and Sabine Süsstrunk. Mesh neural cellular automata. ACM Transactions on Graphics, 43(4): 1–16, 2024

    2024

  57. [65]

    Automata quest: NCAs as a video game life mechanic

    Hiroki Sato, Tanner Lund, Takahide Yoshida, and Atsushi Masumori. Automata quest: NCAs as a video game life mechanic. arXiv preprint arXiv:2309.14364, 2023

    2023 arXiv

  58. [66]

    Yannakakis, and Julian Togelius

    Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, and Julian Togelius. Deep learning for procedural content generation. Neural Computing and Applications, 33(1):19–37, 2021

    2021

  59. [67]

    Dral, Matthias Rupp, and O

    Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole V on Lilienfeld. Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 1(1):1–7, 2014. 16

    2014

  60. [68]

    Axel D. Becke. Density-functional thermochemistry. I. The effect of the exchange-only gradient correction. The Journal of Chemical Physics, 96(3):2155–2160, 1992

    1992

  61. [69]

    Surrogate models for uncertainty quantification: An overview

    Bruno Sudret, Stefano Marelli, and Joe Wiart. Surrogate models for uncertainty quantification: An overview. 11th European Conference on Antennas and Propagation, pages 793–797, 2017

    2017

  62. [70]

    Physics- constrained deep learning for high-dimensional surrogate modeling and uncertainty quantifica- tion without labeled data

    Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, and Paris Perdikaris. Physics- constrained deep learning for high-dimensional surrogate modeling and uncertainty quantifica- tion without labeled data. Journal of Computational Physics, 394:56–81, 2019

    2019

  63. [71]

    Durlofsky

    Meng Tang, Yimin Liu, and Louis J. Durlofsky. A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. Journal of Computational Physics, 413:109456, 2020

    2020

  64. [72]

    Radaideh and Tomasz Kozlowski

    Majdi I. Radaideh and Tomasz Kozlowski. Surrogate modeling of advanced computer simula- tions using deep Gaussian processes. Reliability Engineering & System Safety, 195:106731, 2020

    2020

  65. [73]

    Yannakakis

    Daniel Karavolos, Antonios Liapis, and Georgios N. Yannakakis. A multifaceted surrogate model for search-based procedural content generation. IEEE Transactions on Games, 13(1): 11–22, 2019

    2019

  66. [74]

    Deep surrogate assisted generation of environments

    Varun Bhatt, Bryon Tjanaka, Matthew Fontaine, and Stefanos Nikolaidis. Deep surrogate assisted generation of environments. Advances in Neural Information Processing Systems, 35: 37762–37777, 2022

    2022

  67. [75]

    Yannakakis

    Daniel Karavolos, Antonios Liapis, and Georgios N. Yannakakis. Pairing character classes in a deathmatch shooter game via a deep-learning surrogate model. Proceedings of the 13th International Conference on the Foundations of Digital Games, pages 1–10, 2018

    2018

  68. [76]

    Yannakakis

    Daniel Karavolos, Antonios Liapis, and Georgios N. Yannakakis. Using a surrogate model of gameplay for automated level design. IEEE Conference on Computational Intelligence and Games, pages 1–8, 2018

    2018

  69. [77]

    Fontaine, Amy K

    Yulun Zhang, Matthew C. Fontaine, Amy K. Hoover, and Stefanos Nikolaidis. Deep surrogate assisted map-elites for automated hearthstone deckbuilding. Proceedings of the Genetic and Evolutionary Computation Conference, pages 158–167, 2022

    2022

  70. [78]

    SuSketch: Surrogate models of gameplay as a design assistant

    Panagiotis Migkotzidis and Antonios Liapis. SuSketch: Surrogate models of gameplay as a design assistant. IEEE Transactions on Games, 14(2):273–283, 2021

    2021

  71. [79]

    Yannakakis

    Daniel Karavolos, Antonios Liapis, and Georgios N. Yannakakis. Learning the patterns of balance in a multi-player shooter game. Proceedings of the 12th International Conference on the Foundations of Digital Games, pages 1–10, 2017

    2017

  72. [80]

    De- von Hjelm

    Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, and R. De- von Hjelm. Unsupervised state representation learning in Atari. Advances in Neural Informa- tion Processing Systems, 32, 2019

    2019

  73. [81]

    Yannakakis

    Konstantinos Makantasis, Antonios Liapis, and Georgios N. Yannakakis. From pixels to affect: A study on games and player experience. 8th International Conference on Affective Computing and Intelligent Interaction, pages 1–7, 2019

    2019

  74. [82]

    Lewis, and Satinder Singh

    Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L. Lewis, and Satinder Singh. Action- conditional video prediction using deep networks in atari games. Advances in Neural Informa- tion Processing Systems, 28, 2015

    2015

  75. [83]

    Generative music in video games: State of the art, challenges, and prospects

    Cale Plut and Philippe Pasquier. Generative music in video games: State of the art, challenges, and prospects. Entertainment Computing, 33:100337, 2020

    2020

  76. [84]

    Self-supervised learning: Generative or contrastive

    Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1):857–876, 2021. 17

    2021

  77. [85]

    Saquib Sarfraz, and Rainer Stiefelhagen

    Vivek Sharma, Makarand Tapaswi, M. Saquib Sarfraz, and Rainer Stiefelhagen. Self- supervised learning of face representations for video face clustering. 14th IEEE International Conference on Automatic Face & Gesture Recognition, pages 1–8, 2019

    2019

  78. [86]

    Seungdong Yoa, Seungjun Lee, Chiyoon Kim, and Hyunwoo J. Kim. Self-supervised learning for anomaly detection with dynamic local augmentation. IEEE Access, 9:147201–147211, 2021

    2021

  79. [87]

    Hin- ton

    Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hin- ton. Big self-supervised models are strong semi-supervised learners. Advances in Neural Information Processing Systems, 33:22243–22255, 2020

    2020

  80. [88]

    A survey on contrastive self-supervised learning

    Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Debapriya Banerjee, and Fillia Makedon. A survey on contrastive self-supervised learning. Technologies, 9(1):2, 2020

    2020

  81. [89]

    Yannakakis

    Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis, and Georgios N. Yannakakis. Learning task-independent game state representations from unlabeled images. IEEE Confer- ence on Games, pages 88–95, 2022

    2022

  82. [90]

    Yannakakis

    Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis, and Georgios N. Yannakakis. Towards general game representations: Decomposing games pixels into content and style. arXiv preprint arXiv:2307.11141, 2023

    2023 arXiv

  83. [91]

    Yannakakis

    Chintan Trivedi, Antonios Liapis, and Georgios N. Yannakakis. Contrastive learning of generalized game representations. IEEE Conference on Games, pages 1–8, 2021

    2021

  84. [92]

    Self-supervised contrastive learning for predicting game strategies

    Young Jae Lee, Insung Baek, Uk Jo, Jaehoon Kim, Jinsoo Bae, Keewon Jeong, and Seoung Bum Kim. Self-supervised contrastive learning for predicting game strategies. Pro- ceedings of SAI Intelligent Systems Conference, pages 136–147, 2022

    2022

  85. [93]

    Comparing learning methodologies for self-supervised audio-visual representation learning

    Hacene Terbouche, Liam Schoneveld, Oisin Benson, and Alice Othmani. Comparing learning methodologies for self-supervised audio-visual representation learning. IEEE Access, 10: 41622–41638, 2022

    2022

  86. [94]

    Yannakakis

    Nemanja Rašajski, Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis, and Geor- gios N. Yannakakis. BehA VE: Behaviour alignment of video game encodings.arXiv preprint arXiv:2402.01335, 2024

    2024 arXiv

  87. [95]

    Yannakakis

    Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis, and Georgios N. Yannakakis. Game state learning via game scene augmentation. Proceedings of the 17th International Conference on the Foundations of Digital Games, pages 1–4, 2022

    2022

  88. [96]

    Creating multimodal interactive agents with imitation and self-supervised learning

    DeepMind Interactive Agents Team, Josh Abramson, Arun Ahuja, Arthur Brussee, Federico Carnevale, Mary Cassin, Felix Fischer, Petko Georgiev, Alex Goldin, Mansi Gupta, et al. Creating multimodal interactive agents with imitation and self-supervised learning. arXiv preprint arXi...

    2021 arXiv

  89. [97]

    STARLING: Self-supervised training of text-based reinforcement learning agent with large language models

    Shreyas Basavatia, Keerthiram Murugesan, and Shivam Ratnakar. STARLING: Self-supervised training of text-based reinforcement learning agent with large language models. arXiv preprint arXiv:2406.05872, 2024

    2024 arXiv

  90. [98]

    Entity embedding as game represen- tation

    Nazanin Yousefzadeh Khameneh and Matthew Guzdial. Entity embedding as game represen- tation. arXiv preprint arXiv:2010.01685, 2020

    2010 arXiv

  91. [99]

    CURL: Contrastive unsupervised representations for reinforcement learning

    Michael Laskin, Aravind Srinivas, and Pieter Abbeel. CURL: Contrastive unsupervised representations for reinforcement learning. International Conference on Machine Learning, pages 5639–5650, 2020

    2020

  92. [100]

    A path towards autonomous machine intelligence version 0.9

    Yann LeCun. A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27. Open Review, 62(1):1–62, 2022

    2022

  93. [101]

    Introduction to latent variable energy-based models: A path toward autonomous machine intelligence

    Anna Dawid and Yann LeCun. Introduction to latent variable energy-based models: A path toward autonomous machine intelligence. Journal of Statistical Mechanics: Theory and Experiment, 2024(10):104011, 2024. 18

    2024

  94. [102]

    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. Proceedings of the IEEE/CVF Conference on Computer Vision and Pat...

    2023

  95. [103]

    Bootstrap your own latent - a new approach to self-supervised learning

    Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent - a new approach to self-supervised learning. Advances in Neural...

    2020

  96. [104]

    Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al

    Jake Bruce, Michael D. Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, et al. Genie: Generative interactive environments. International Conference on Machine Learning, 2024

    2024

  97. [105]

    Spatial-temporal transformer networks for traffic flow forecasting

    Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, and Hongkai Xiong. Spatial-temporal transformer networks for traffic flow forecasting. arXiv preprint arXiv:2001.02908, 2020

    2001 arXiv

  98. [106]

    Neural discrete representation learning

    Aaron Van Den Oord, Oriol Vinyals, and Koray Kavukcuoglu. Neural discrete representation learning. Advances in Neural Information Processing Systems, 30, 2017

    2017

  99. [107]

    Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T. Freeman. Maskgit: Masked generative image transformer. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11315–11325, 2022

    2022

  100. [108]

    Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Informatio...

    2022

  101. [109]

    World models

    David Ha and Jürgen Schmidhuber. World models. arXiv preprint arXiv:1803.10122, 2018

    2018 arXiv

  102. [110]

    Genie 2: A large-scale foundation world model

    Google DeepMind. Genie 2: A large-scale foundation world model. https://deepmind. google/discover/blog/genie-2-a-large-scale-foundation-world-model . Ac- cessed: 2024-12-11

    2024

  103. [111]

    Scaling instructable agents across many simulated worlds

    Maria Abi Raad, Arun Ahuja, Catarina Barros, Frederic Besse, Andrew Bolt, Adrian Bolton, Bethanie Brownfield, Gavin Buttimore, Max Cant, Sarah Chakera, et al. Scaling instructable agents across many simulated worlds. arXiv preprint arXiv:2404.10179, 2024

    2024 arXiv

  104. [112]

    A survey on simulators for testing self-driving cars

    Prabhjot Kaur, Samira Taghavi, Zhaofeng Tian, and Weisong Shi. A survey on simulators for testing self-driving cars. Fourth International Conference on Connected and Autonomous Driving, pages 62–70, 2021

    2021

  105. [113]

    Super Mario as a string: Platformer level generation via LSTMs, author=Summerville, Adam and Mateas, Michael, journal=arXiv preprint arXiv:1603.00930, year=2016

    2016 arXiv

  106. [114]

    Ruthenbeck and Karen J

    Greg S. Ruthenbeck and Karen J. Reynolds. Virtual reality for medical training: The state-of- the-art. Journal of Simulation, 9(1):16–26, 2015

    2015

  107. [115]

    Alfred T. Lee. Flight simulation: Virtual environments in aviation. Routledge, 2017

    2017

  108. [116]

    Augmenting LLMs with knowledge: A survey on hallucination prevention

    Konstantinos Andriopoulos and Johan Pouwelse. Augmenting LLMs with knowledge: A survey on hallucination prevention. arXiv preprint arXiv:2309.16459, 2023

    2023 arXiv

  109. [117]

    Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S. M. Tonmoy, Aman Chadha, Amit P. Sheth, and Amitava Das. The troubling emergence of hallucination in large language models–an extensive definition, quantification, and prescriptive remediations. arXiv preprint...

    arXiv

  110. [118]

    Efficient deep learning: A survey on making deep learning models smaller, faster, and better

    Gaurav Menghani. Efficient deep learning: A survey on making deep learning models smaller, faster, and better. ACM Computing Surveys, 55(12):1–37, 2023. 19

    2023

  111. [119]

    Latent action pretraining from videos

    Seonghyeon Ye, Joel Jang, Byeongguk Jeon, Sejune Joo, Jianwei Yang, Baolin Peng, Ajay Mandlekar, Reuben Tan, Yu-Wei Chao, Bill Yuchen Lin, et al. Latent action pretraining from videos. arXiv preprint arXiv:2410.11758, 2024

    2024 arXiv

  112. [120]

    LLM4GEN: Leveraging semantic representation of LLMs for text-to-image generation

    Mushui Liu, Yuhang Ma, Yang Zhen, Jun Dan, Yunlong Yu, Zeng Zhao, Zhipeng Hu, Bai Liu, and Changjie Fan. LLM4GEN: Leveraging semantic representation of LLMs for text-to-image generation. arXiv preprint arXiv:2407.00737, 2024

    2024 arXiv

  113. [121]

    LayoutLLM-T2I: Eliciting layout guidance from LLM for text-to-image generation

    Leigang Qu, Shengqiong Wu, Hao Fei, Liqiang Nie, and Tat-Seng Chua. LayoutLLM-T2I: Eliciting layout guidance from LLM for text-to-image generation. Proceedings of the 31st ACM International Conference on Multimedia, pages 643–654, 2023

    2023

  114. [122]

    LLM-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models

    Long Lian, Boyi Li, Adam Yala, and Trevor Darrell. LLM-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models. arXiv preprint arXiv:2305.13655, 2023

    2023 arXiv

  115. [123]

    Deep learning for video game playing

    Niels Justesen, Philip Bontrager, Julian Togelius, and Sebastian Risi. Deep learning for video game playing. IEEE Transactions on Games, 12(1):1–20, 2019

    2019

  116. [124]

    Data management challenges for deep learning

    Aiswarya Munappy, Jan Bosch, Helena Holmström Olsson, Anders Arpteg, and Björn Brinne. Data management challenges for deep learning. 45th Euromicro Conference on Software Engineering and Advanced Applications, pages 140–147, 2019

    2019

  117. [125]

    Understanding intermediate layers using linear classifier probes

    Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. arXiv preprint arXiv:1610.01644, 2016

    2016 arXiv

  118. [126]

    Opening the black box of deep neural networks via information

    Ravid Shwartz-Ziv and Naftali Tishby. Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810, 2017

    2017 arXiv

  119. [127]

    Misbehaviour prediction for autonomous driving systems

    Andrea Stocco, Michael Weiss, Marco Calzana, and Paolo Tonella. Misbehaviour prediction for autonomous driving systems. Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering, pages 359–371, 2020

    2020

  120. [128]

    Nikolenko

    Sergey I. Nikolenko. Synthetic data for deep learning, volume 174. Springer, 2021

    2021

  121. [129]

    Distilling the knowledge in a neural network

    Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  122. [130]

    What is the state of neural network pruning? Proceedings of Machine Learning and Systems, 2:129–146, 2020

    Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag. What is the state of neural network pruning? Proceedings of Machine Learning and Systems, 2:129–146, 2020

    2020

  123. [131]

    The super weight in large language models

    Mengxia Yu, De Wang, Qi Shan, and Alvin Wan. The super weight in large language models. arXiv preprint arXiv:2411.07191, 2024

    2024 arXiv

  124. [132]

    Opportunities and challenges in explainable artificial intelligence (XAI): A survey

    Arun Das and Paul Rad. Opportunities and challenges in explainable artificial intelligence (XAI): A survey. arXiv preprint arXiv:2006.11371, 2020

    2006 arXiv

  125. [133]

    Opportunities and challenges in deep learning adversarial robustness: A survey

    Samuel Henrique Silva and Peyman Najafirad. Opportunities and challenges in deep learning adversarial robustness: A survey. arXiv preprint arXiv:2007.00753, 2020

    2007 arXiv

  126. [134]

    Procedural content generation through quality diversity.IEEE Conference on Games, pages 1–8, 2019

    Gravina, Daniele and Khalifa, Ahmed and Liapis, Antonios and Togelius, Julian and Yan- nakakis, Georgios N. Procedural content generation through quality diversity.IEEE Conference on Games, pages 1–8, 2019

    2019

  127. [135]

    Differentiable quality diversity

    Matthew Fontaine and Stefanos Nikolaidis. Differentiable quality diversity. Advances in Neural Information Processing Systems, 34:10040–10052, 2021. 20

    2021

Pith tools

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