PrismaX is empowering visual generative AI by addressing the data limitations hindering both multimodal foundation models and their applications. While text-based AI has thrived on vast datasets like the Common Crawl, visual AI struggles due to insufficient, biased data sources. PrismaX introduces a first-person view (PoV) mechanism to build a comprehensive, high-quality visual dataset collection infrastructure that captures authentic first-person perspectives - the most natural way humans perceive and interact with the world - while supporting AI pre-launch testing and continuous improvement.

This decentralized infrastructure ensures data diversity and authenticity by leveraging a global community of contributors, incentivized through PIX Coin. The platform's robust filtering and verification mechanisms maintain data quality, supporting advancements in AI and robotics — from full-body motion understanding to personalized AI systems. PrismaX's vision is to create an ecosystem where AI and robots seamlessly interact with the world, driving forward human-like understanding and collaboration.

The revolution in generative AI, powered by large-scale statistical distribution modeling, owes much of its success to the Common Crawl—a comprehensive archive of humanity's digital textual output spanning two decades. This vast dataset has enabled models to reason across diverse knowledge domains, generate code, compose poetry, and exhibit human-like cognitive patterns. However, this success remains largely confined to the textual domain.

In contrast, visual AI—covering images and videos—faces significant performance limitations. Current models struggle with basic tasks, producing only generic image captions or brief, often inconsistent 4-second video clips. This performance gap stems from a fundamental data problem: the absence of a dataset that truly represents visual reality. While we have access to images from e-commerce platforms and video clips from YouTube, these sources offer a narrow, commercially-biased view of the world. Unlike the Common Crawl, which emerged from humanity's collective digital communication, existing visual datasets fail to capture the richness and complexity of the natural world.

The key to bridging this gap lies in capturing ground truth data from daily human experiences. This approach promises to create a visual equivalent of the Common Crawl—a comprehensive dataset that truly represents the world. The foundation for our solution is PoV - Proof-of-View - a highly scalable protocol enabling the collection of internet-scale multimodal datasets without the need for extensive, per-submission human validation.

By solving the fundamental data problem in visual AI, we enable breakthrough advances in robotics, world understanding, and multimodal prediction and generation. This gives models focused on vision, motion, images, and sound the same level of reasoning capability and generalization that large language models (LLMs) have in the text domain.

Our collection process is governed by protocols that ensure data truthfulness, quality, and diversity. Our supply-side capture system leverages on-device AI combined with auxiliary data such as geolocation and motion to ensure baseline truthfulness of data, while our protocol layer incorporates state-of-the-art deep learning to filter, validate, and fairly score every piece of data, all with zero human validators required. This approach ensures that every piece of collected data contributes meaningfully to the dataset's overall value, while allowing the network to scale efficiently by eliminating the high burden and trust issues associated with decentralized human curators.

The crux of our methodology is the observation that capturing real-life experiences is relatively low friction for the supply side. Therefore, systematically submitting bad data to the network is only worthwhile if it is easier than simply recording a video with a phone. Our protocol incorporates existing AI models to verify the plausibility of each submission; bad actors would have to carefully craft diverse data (in order to not be penalized by the diversity engine) that passes our automated filters.

We reward incentive to supply-side participants as follows:

Where k is a fixed constant, S0 is the current threshold, and s is the data's aesthetic score. S0 starts at 0 and is updated by the network such that the rate of high aesthetic data evolves according to some schedule.

Where D is the diameter of the smallest ball which contains at least (1−z)·a fraction of the data points, z is the outlier rejection ratio, and α is a fixed constant. u is clamped from above to 1. The geometric mean in the numerator has the convenient feature that if V = Vi for some i, then u = 0; namely, the dissimilarity score of a strict duplicate is zero. Then we can compute the token yield as proportional to:

Where once again k is a fixed constant and u0 starts at 0 and is updated by the network. Finally, the net token yield is proportional to the product of Rs and Ra. The network updates s0 and u0 to ensure a constant rate of token issuance across a sliding window.

Demand side customers are used to performing the bulk of postprocessing and data transformation tasks, but the PoV protocol also offers a number of scalable post processing services which are applied to each accepted data submission, including:

Many demand-side customers need domain- or task-specific data that are sparsely present in the master dataset. In order to connect data users to the correct community members, we can leverage the data contributed by each community member to build a representation of each user’s habits, daily activities, and skills. Matching data requests with the right miners not only makes data collection more efficient, it increases baseline data quality.

We leverage text as a representation of user personality. The network already generates text captions for each submitted item; we first temporarily compress captions so that data submitted at nearby times have a single caption. We then concatenate captions, adding their times and locations, and take advantage of the strong reasoning capabilities of open-source text-only language models to build rich (~2000 tokens) text descriptions of each user. To avoid having to re-ingest context every time, as new data are submitted by users the engine updates the personalities in a rolling fashion, concatenating the previous personality description with compressed captions of new data to generate updated descriptions.

To route data requests, we rewrite requests for retrieval, once again taking advantage of the rich knowledge of text-only models, then use semantic retrieval and clustering to find the best-matching community members.

PoV bridges the gap in the value chain between well-funded robotics and AI tooling companies that currently overpay for data or collect it through inefficient and costly methods. On the supply side, while Web3 presents a powerful avenue for data collection, simply relying on token incentives without a sustainable economic model can destabilize tokenomics and ultimately jeopardize a project's viability. Take most of the x-to-earn project for example- the fundamental issue is the token's inability to regenerate value due to an imbalance in its economic structure. PoV solves this problem efficiently through a closed-loop model, where a clearly defined demand side funds the incentivization of the network, ensuring the social aspect of the network scalability.

By building state-of-the-art datasets for state-of-the-art multimodal foundation models, PoV establishes a baseline whereby the transactional unit for all interactions on the protocol - the $PIX token - gains inherent value beyond just as a speculative medium, granting stability to the token not found in many other projects. This in turn establishes supply-side trust, incentivizing community participants to further invest their time, money, and data in $PIX, something which closes the loop and accelerates continuous improvement of the dataset.

Because AI companies are quickly becoming differentiated not by their algorithms or model architectures, but by their training data and data access, multimodal training data has immense value and will continue to do so for the foreseeable future.

PoV demand-side customers are AI companies and research teams who are training foundation models for real world use cases such as robotics, content creation, and video analysis. These companies are looking at investing hundreds of thousands to hundreds of millions of dollars per model run, depending on the scale of the training and the size of the model. Out of this, a large portion (30%+) of the budget will be devoted to data collection and generation. This is especially true for models trained on video data, where no readily available baseline dataset exists. A quick estimate indicates that for typical video pricing of between 10 cents and 1 dollar (US currency) per short (several seconds) clip, a fine tuning run on 1M rows of data costs $100,000 and a large-scale pretraining run on 1B rows costs $100,000,000 in data costs alone. These data costs are amplified to intractable levels if they are custom data sourced from traditional centralized sources such as Scale AI, which carry significant overhead.

PoV supply side is designed to capture customers with varieties of personas. Tasks are published on the platform in epoch-based data challenges. Reward hunters and speculators favor simple, reward-driven tasks requiring minimal effort. Meanwhile, a tight-knit community of researchers and AI enthusiasts—already passionate about data collection and often doing it for free—is drawn to novel challenges and their potential impact. For the broader crypto community, certain challenges incorporate gamification elements to encourage engagement and retain recurring participants. With this method, we enforce the data diversity from a social perspective.

Initially, our data community will collect data via the mobile app, providing a low-friction and cost-effective solution. As the network scales, we will introduce hardware aspects through partnerships such as action cameras and smart glasses for targeted datasets. This phased approach aligns with our go-to-market strategy, starting with broad, general datasets before transitioning to more specialized, high-value data acquisition.

$PIX token is the tie that channels both sides. PoV demand-side customers stake $PIX tokens through a third-party intermediary in order to access data, ensuring fiat accessibility for Web2 companies. The intermediate clears the payments by using them to stake tokens, accesses the data, and provides it to the customer. This mechanism creates a positive feedback loop:

The core of PrismaX's offering data - both data that has been collected by the network already as well as the ability to rapidly collect new data. Around this data, we're able to build powerful enterprise tooling to make it easier for demand-side customers to access our network.

PoV data forms a diverse and robust visual dataset, capturing real-world, complex activities in authentic settings:

Real world data contains a variety of scenarios which AI models can learn from. These scenarios provide unique motion patterns, interactions, and contextual challenges—essential for developing robots and systems that can perform tasks accurately, navigate dynamic spaces, and engage naturally with humans. For example:

Demand side customers can initiate data challenges in order to use the network to collect domain-specific data they find the most useful. Key to a successful data challenge platform is preventing sabotage, especially systematic sabotage, through a robust verification mechanism. To that end, we propose:

Combined, these techniques make it extremely challenging to sabotage the verification mechanism - an actor would have to engineer a system that passes engagement verification, yields enough data that changes over time to earn tokens while evading the filter engine and ensure that the bad data so generated has captions which match the data request. In order for the effort to be worthwhile, they would need to craft an attack that works across multiple data requests. Such a system is unlikely to be able to collect data more efficiently than simply working with real humans to collect real data, which incentivizes the network to provide truthful responses to data requests.

PoV data and tooling empower vastly improved performance across broad verticals in multimodal AI, including robotics, world understanding, and more.

First-person view datasets transform robotics by enabling spatial understanding, robust interactions, and synthetic data generation.

Third-person-view data of humans completing tasks yields valuable full-body pose data, which is critical for advancing robotics. Such data can either be explicitly incorporated into training pipelines by explicitly extracting pose with a dedicated pose model during preprocessing, or implicitly through a model architecture that learns to track major human body features as part of the training process. Exposure to a wide variety of poses during training enhances model generalization, improving human-robot collaboration and enabling downstream use-cases such as object handling and environmental interaction. Applications span industrial automation, service robotics, and collaborative environments, leading to more efficient performance in assembly, sorting, and various interactive tasks.

Complex interactions, such as assembly processes requiring precise orientation and placement, become feasible through a detailed understanding of hand-object interaction (HOI). HOI encompasses not only the physical aspects of manipulation but also the temporal sequences and contextual understanding necessary for natural interaction. This knowledge enables robots to learn from human demonstrations and transfer skills effectively.

Traditionally, industrial robots have been programmed by having them repeat a series of precise, deterministic steps; a more modern approach might incorporate basic machine vision using object detection and simple rules, but nevertheless, remains limited in its ability to handle unexpected situations. This manual approach also leads to long and expensive development cycles, with dedicated automation engineers having to redo work for every new model of robot and every new task, even for minor changes.

In contrast, foundation robotic models trained on large datasets of videos learn powerful internal representations of physical interactions. These models can be adapted to, for example, combine natural language instructions from robot users with vision input from cameras on the robots to complete tasks much more autonomously than with traditional means. Such adapted models are far more agnostic to exact environmental or hardware configurations than rules-based pipelines, making it much easier for robotics users to design and modify their workflows.

FPV data also empowers personal AI by enabling sophisticated episodic memory and diarization techniques. By analyzing human perspective data, personal AI learns to recall specific experiences vividly, recognize individual movement patterns, and adapt interactions accordingly. This integration allows AI to tailor interactions based on past behaviors and develop personalized patterns, significantly enhancing user comfort and effectiveness. Through FPV data and diarization, personal AI not only assists more adeptly in daily tasks but also interacts in a more human-like, empathetic manner, improving the overall human-AI collaboration experience.

We present a few more concrete case studies showcasing the above fields.

Traditional autonomous navigation models are trained on dashcam-like sensor footage from fleets of instrumented cars piloted by human drivers. Thanks to the huge number of cars in the world, these models exhibit robust generalization and reliability. However, for emerging robotics applications which need to navigate small indoor environments at 3 m/s, not freeways at 30 m/s, no such models exist because no such training data exists. PoV is able to provide extensive such datasets through phones or wearable cameras. By simply recording themselves moving through their environment and interacting with doors and elevators, community participants can passively collect valuable data to help robots learn end-to-end navigation in everyday environments. This makes possible the next generation of consumer facing robotics experiences such as concierges, restaurant servers, hospital robotics, and more.

Domestic and light commercial robotics is an emerging field which seeks to replicate human tasks using modern, advanced robotics. For example, this space encompasses food service, shelf restocking, ‘backend’ hospitality such as maid service, light outdoor work, and more. Robots in this field need to be intuitively controllable by non-technical staff, often small businesses who do not have access to AI or industrial automation engineers.

In order to enable these use cases, robots need to be trained on a broad compendium of tasks so that during deployment, natural language or other simple controls can be used to prompt and adapt their behavior to tasks at hand.

Large third-person-view datasets of task videos can be used to train robots to complete tasks through, for example, ego-exo transfer approaches. Here, the higher level video data (which teaches the model the steps and spatial reasoning needed to complete a task) is combined with a lower level motion controller trained on a much smaller and hardware-specific corpus of joint and pose data to create a robust controller. PoV is well suited for this approach because participants can passively collect data of themselves or others completing tasks, and the diversity of the community provides a basis for a varied and highly generalizable dataset.

AI models such as Stable Diffusion are very capable at generating new content, but fail to perform content-preserving edits on existing content. Even state-of-the-art approaches such as Adobe's Firefly Generative AI frequently encounter edge cases and fail to follow user instructions.

One of the earliest approaches for AI driven image editing was Instruct-Pix2Pix, a fine-tuning process for Stable Diffusion 1.5 which resulted in an instruction-following editing model. However, due to severe data limitations (all data was synthetically generated through a primitive pipeline), real-world performance was extremely lacking.

An improved dataset would greatly improve Instruct-Pix2Pix and similar techniques. Rather than paying skilled workers millions of dollars to photoshop images, PoV can take a different approach thanks to the diversity of its participants: they can capture before-after pairs of images with real-life changes, write a short caption about what changed, and submit it to the network. A few minutes of participant time here replaces tens of dollars of skilled image editing labor, and the technique seamlessly extends to video, which would require hours of work per clip to edit.

Our approach prioritizes scalability by first collecting a broad, general dataset, distinguishing us from other networks that focus on niche, specific data collection such as 3D and motion. Our ultimate goal is to achieve a dataset at the scale of Common Crawl for images and videos, unlocking vast opportunities beyond just robotics. While robotics serves as our initial beachhead market, our broader vision extends far beyond.

In our blue sky scenario, PrismaX PoV becomes the foundational data layer for all generative AI companies, powering advancements in AI foundation models, personal AI, retail and e-commerce, entertainment, and media. By building a scalable, high-quality dataset, we aim to shape the next generation of AI-driven innovation across industries.