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risks from frontier models some examples of dangerous capabilities include designing and synthesizing new biological or chemical weapons 15 enabling novel offensive cyber attacks 16 evading human control through deceptive means 17 it may be difficult to regulate the development and deployment of frontier models due to limited interpretability 18 unforeseen capability improvements ease of misuse and rapid proliferation 19 if a frontier model is open weight and publicly available online the model can spread more rapidly while the combination of greater customizability and non revocable access exacerbates problems of accountability and regulatory oversight 20 mitigating all harms that might arise from already deployed frontier models becomes more difficult due to post release modifications and attacks such as api fine tuning 21 and prompt injection as well as capabilities enhancements such as the agent harness access to external resources data tools systems and orchestration among ai systems 22 the industry led frontier model forum was founded in july 2023 by openai anthropic google deepmind and microsoft with a stated goal of supporting safety research best practice standards and information sharing about advanced foundation models 23 non primary source needed a network of national artificial intelligence safety institutes aisis have formed to assess risk from frontier models 24 general purpose ai edit foundation models adaptable to a wide range of use cases are sometimes referred to as general purpose ai in designing the eu ai act the european parliament has stated that a new wave of general purpose ai technologies shapes the overall ai ecosystem 25 world models edit main article world model artificial intelligence see also digital twin world models are sometimes described as foundation models 26 27 world models are a representation of an environment intended to predict the state of that environment after taking a set of actions 28 29 as well as to implicitly model physical concepts such as gravity 29 input prompts for world models can include text or images 30 31 as well as videos or 3d scenes 32 and the resulting 3d environments can be exported 32 world models alongside embodied ai multi agent models and neuroscience models of the brain are seen as alternatives to large language models for achieving general artificial intelligence 33 world models do not have a fully agreed definition but have been divided into two scopes one for representing and understanding the current environment and another for predicting the future state of that environment in the former view world models are developed using model based reinforcement learning and a markov decision process using model predictive control or monte carlo tree search to create policies with the latter multimodal large language models or video generation models can be used in addition these environments can be immersive simulations for training ai agents that can interact in the real world 34 history edit quanta magazine traced world models back to a 1943 publication by kenneth craik on mental models and the blocks world of shrdlu in the 1960s 35 business insider traced world models to a 1971 paper by jay wright forrester 33 a related idea of organizing world knowledge the frame representation was proposed by marvin minsky in 1974 34 in 2018 researchers david ha and jürgen schmidhuber defined world models in the context of reinforcement learning an agent with a variational autoencoder model v for representing visual observations a recurrent neural network model m for representing memory and a linear model c for making decisions they suggested that agents trained on world models in environments that simulate reality could be applied to real world settings 36 in 2022 yann lecun saw a world model defined by him as a neural network that acts as a mental model for aspects of the world that are seen as relevant as part of a larger system of cognitive architecture other neural networks that are analogous to different regions of the brain in his view this framework could lead to commonsense reasoning 37 38 lecun has estimated that world models would be fully functional by the late 2020s 39 to mid 2030s 40 training edit world models are trained on a variety of data modalities including text images audio and video and have been applied to video generation 41 one open source dataset for world models includes 1 billion data points across multiple modalities text images audio video and point clouds including 1 million manual annotations 29 examples edit techcrunch saw sora as an example of a world model 41 while in january 2025 nvidia released its own set of world models 42 27 the south china morning post wrote that manycore tech was another example of companies aiming to build a world model viewing their work as an example of spatial intelligence 43 in may 2025 mohamed bin zayed university of artificial intelligence released a world model for building simulations to test ai agents 44 google deepmind has also released two world models in two dimensional space and three dimensional space respectively that were trained on video data with google claiming that the latter can be a training environment for ai agents 45 46 meta released a world model in june 2025 47 tencent released an open source world model in july 2025 48 niantic inc spinoff niantic spatial is developing a world model using anonymized player scans from pokémon go 49 50 other companies that are planning as of 2025 to build world models include bytedance 48 and xai 51 applications edit computer scientist researcher fei fei li views world models as applying to robotics and creative works due to the complexity of these models she advocates for more complex strategies in data acquisition data engineering data processing and synthesizing data 52 she co founded a startup on building world models which as of 2024 planned to do so in three phases incorporating an understanding of three dimensional space along with time support for augmented reality and support for robotics 53 her startup world labs released its commercial world model marble in november 2025 54 world models are intended for use in interactive media such as video games and movies 55 and environment simulation 56 proposed use cases for world models include action planning and outcome prediction 54 other applications include social simulacra to simulate social systems 34 wired compared world models to the metaverse 53 while business insider noted possible military applications 52 in 2025 world models are being applied to drone warfare robotics and self driving vehicles the wall street journal speculated that world models could improve spatial reasoning of artificial intelligence models and successfully automate both blue collar and white collar jobs 57 as of october 2025 research has shown mixed results in the spatial reasoning capabilities of text to video models in particular veo 3 58 in ophthalmology foundation models have been applied to retinal imaging including volumetric optical coherence tomography 59 concerns edit techcrunch noted that world models could use more data than large language models and would require significantly more computational power including the use of thousands of gpus for training and inference 38 41 it also noted the risk of hallucinations coverage bias and algorithmic bias 41 similarly the financial times noted the difficulty and expense in collecting data to simulate the world and training models to use that data 51 creative professionals have expressed concern that world models could disrupt jobs in their industries 56 other concerns include data privacy 34 simulation of harmful situations 34 misinformation and disinformation 34 emergent behaviors 60 and copyright 55 technical details edit model architecture edit for a foundation model to effectively generalize it must acquire rich representations of the training data as a result expressive model architectures that efficiently process large scale data are often preferred in building foundation models 5 currently the transformer architecture is the de facto choice for building foundation models across a range of modalities 61 training edit foundation models are built by optimizing a loss function which is a mathematical function that determines how model parameters are updated based on model predictions on training data 62 language models are often trained with a next tokens prediction objective which refers to the extent at which the model is able to predict the next token in a sequence image models are commonly trained with contrastive learning or diffusion training objectives for contrastive learning images are randomly augmented before being evaluated on the resulting similarity of the model s representations for diffusion models images are noised and the model learns to gradually de noise via the objective multimodal training objectives also exist with some separating images and text during training while others examine them concurrently 63 in general the training objectives for foundation models promote the learning of broadly useful representations of data with the rise of foundation models and the larger datasets that power them a training objective must be able to parse through internet scale data for meaningful data points additionally since foundation models are designed to solve a general range of tasks training objectives ought to be domain complete or able to solve a broad set of downstream capabilities within the given domain lastly foundation model training objectives should seek to scale well and be computationally efficient with model size and compute power both being relevant constraints a training objective must be able to overcome such bottlenecks data edit foundation models are trained on a large quantity of data working under the maxim the more data the better 64 performance evaluation does show that more data generally leads to better performance but other issues arise as data quantity grows tasks like managing the dataset integrating data across new applications ensuring adherence to data licenses and maintaining data quality all become more difficult as data size grows the specific demands of foundation models have only exacerbated such issues as it remains the norm for large foundation models to use public web scraped data foundation models include also search engines data and seo meta tags data public web data remains a plentiful resource but it also demands stringent moderation and data processing from foundation model developers before it can be successfully integrated into the training pipeline 65 training foundation models often runs the risk of violating user privacy as private data can be disclosed collected or used in ways beyond the stated scope even if no private data is leaked models can still inadvertently compromise security through learned behavior in the resulting foundation model 66 data quality is another key point as web scraped data frequently contains biased duplicate and toxic material once foundation models are deployed ensuring high quality data is still an issue as undesirable behavior can still emerge from small subsets of data systems edit the size of foundation models also brings about issues with the computer systems they run on the average foundation model is too large to be run within a single accelerator s memory and the initial training process requires an expensive amount of resources 67 such issues are predicted to further exacerbate in future as foundation models grow to new heights due to this constraint researchers have begun looking into compressing model size through tight model inference gpus are the most common choice of compute hardware for machine learning due to high memory storage and strong power typical foundation model training requires many gpus in a distributed computing setup connected in parallel with fast interconnects acquiring a sufficient amount of gpus of requisite compute efficiency is a challenge for many foundation model developers one that has led to an increasing dilemma in the field larger models require greater compute power but often at the cost of improved compute efficiency since training remains time consuming and expensive the tradeoff between compute power and compute efficiency has led only a few select companies to afford the production costs for large state of the art foundation models some techniques like compression and distillation can make inference more affordable but they fail to completely shore up this weakness scaling edit the accuracy and capabilities of foundation models often scale predictably with the size of the model and the amount of the training data specifically scaling laws have been discovered which are data based empirical trends that relate resources data model size compute usage to model capabilities particularly a model s scale is defined by compute dataset size and the number of parameters all of which exhibit a power law relationship with end performance however broken scaling laws 68 have been discovered in which this relationship smoothly transitions at points referred to as break s from a power law with one exponent to a power law with another different exponent when one does not collect any points near or after the break s it can be difficult to obtain an accurate extrapolation adaptation edit foundation models are inherently multi purpose to use these models for a specific use case requires some form of adaptation at a minimum models need to be adapted to perform the task of interest task specification but often better performance can be achieved by more extensive adaptation to the domain of interest domain specialization a variety of methods e g prompting in context learning fine tuning lora provide different tradeoffs between the costs of adaptation and the extent to which models are specialized some major facets to consider when adapting a foundation model are compute budget and data availability foundation models can be very large up to trillions of parameters in size so adapting the entirety of a foundation model can be computationally expensive therefore developers sometimes adapt only the last neural layer or only the bias vectors to save time and space 69 for particularly niche applications specific data may also not be available to adapt the foundation model sufficiently in such circumstances data must be manually labeled which is costly and can demand expert knowledge evaluation edit evaluation is a key part of developing foundation models not only does evaluation allow for tracking progress of high performance models it also creates benchmarks for future model development stakeholders rely on evaluations to understand model behaviors and gain insight into their various attributes traditionally foundation models are evaluated relative to each other through standardized task benchmarks like mmlu 70 mmmu 71 humaneval 72 and gsm8k 73 given...