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Accept Decline Featured Content Home Genius Blog News Company Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube   AI for Finance Previous Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii Next 1 2 3 4 Our technology: Inspired by the brain Humans learn by prioritizing what is both important and uncertain as our brains model the world and continuously improve. For instance, when drivers see a ball roll into the road, they sense possible danger (inference), and change their behavior (active) by slowing down and scanning the area. Like the brain, VERSES Genius™ has been designed to tackle problems with this process of active inference. Genius interacts with the world and continually updates, allowing it to adapt to a volatile, uncertain, complex, and ambiguous world. Like humans, it can solve problems by sensing, planning, acting, and sharing. Our breakthroughs Beating OpenAI and DeepSeek In winter 2024/2025 we showed that Genius could learn quickly - and that it could beat competitors playing Mastermind and Atari computer games. This proved that Genius could achieve better results at lower costs that leading AI models such as Iris (a transformer model), OpenAI’s o1-preview and DeepSeek R1. The world’s first digital brain beats Google DeepMind In June 2025 we unveiled a modular digital brain, which played classic computer games 60% better and 97% more efficiently than Google’s leading AI models. This performance is driven by models that quickly adapt to new information without retraining.   2025/6 Finance Over 2025/6 we worked with a large global asset manager (>$100bn AUM) to show how Genius can help them achieve higher returns while managing risk in compliance with their governance policies. Up next... ARC-AGI-3 VERSES is developing further modules for Genius which will allow us to solve complex problems, such as those tested by the ARC-AGI-3 challenge. ARC-AGI 3 is a test of human-like intelligence. It does this by setting tasks that are deliberately designed to show the gap between cutting edge AI systems and human intelligence. 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VERSES is committed to protecting and respecting your privacy, and we’ll only use your personal information to administer your account and to provide the products and services you requested from us. From time to time, we would like to contact you about our products and services, as well as other content that may be of interest to you. If you consent to us contacting you for this purpose, please tick below to say how you would like us to contact you: I agree to receive other communications from VERSES. In order to provide you the content requested, we need to store and process your personal data. If you consent to us storing your personal data for this purpose, please tick the checkbox below. I agree to allow VERSES to store and process my personal data. * You may unsubscribe from these communications at any time. For more information on how to unsubscribe, our privacy practices, and how we are committed to protecting and respecting your privacy, please review our Privacy Policy. Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. ◈ Interior Pages — 21 pages crawledGenius: AI for finance Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube   AI for Finance Genius solves financial services problems   Fund managers can't rely on today's AI Today’s LLMs can’t be explained and aren’t reliable.   Fund managers need an AI Genius Genius™ helps fund managers to identify and quantify risks and quickly act on new data.   Inspired by the human brain Active inference is superior to LLMs and learns on its own, with models that can always be explained. At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii Our technology: Inspired by the brain Humans learn by prioritizing what is both important and uncertain as our brains model the world and continuously improve . For instance, when drivers see a ball roll into the road, they sense possible danger (inference), and change their behavior (active) by slowing down and scanning the area. Like the brain, VERSES Genius™ has been designed to tackle problems with this process of active inference . Genius interacts with the world and continually updates, allowing it to adapt to a volatile, uncertain, complex, and ambiguous world. Like humans, it can solve problems by sensing, planning, acting, and sharing. Our breakthroughs Beating OpenAI and DeepSeek In winter 2024/2025 we showed that Genius could learn quickly - and that it could beat competitors playing Mastermind and Atari computer games. This proved that Genius could achieve better results at lower costs that leading AI m odels such as Iris (a transformer model), OpenAI’s o1-preview and DeepSeek R1. The world’s first digital brain beats Google DeepMind In June 2025 we unveiled a modular digital brain , which played classic computer games 60% better and 97% more efficiently than Google’s leading AI models. This performance is driven by models that quickly adapt to new information without retraining.   2025/6 Finance Over 2025/6 we worked with a large global asset manager (>$100bn AUM) to show how Genius can help them achieve higher returns while managing risk in compliance with their governance policies. Up next... ARC-AGI-3 VERSES is developing further modules for Genius which will allow us to solve complex problems, such as those tested by the ARC-AGI-3 challenge . ARC-AGI 3 is a test of human-like intelligence. It does this by setting tasks that are deliberately designed to show the gap between cutting edge AI systems and human intelligence. The underlying philosophy behind it is that intelligence is simply “how quickly can you learn new skills?” Contact us Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Genius: AI for finance Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube   AI for Finance Genius solves financial services problems   Fund managers can't rely on today's AI Today’s LLMs can’t be explained and aren’t reliable.   Fund managers need an AI Genius Genius™ helps fund managers to identify and quantify risks and quickly act on new data.   Inspired by the human brain Active inference is superior to LLMs and learns on its own, with models that can always be explained. At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii Our technology: Inspired by the brain Humans learn by prioritizing what is both important and uncertain as our brains model the world and continuously improve . For instance, when drivers see a ball roll into the road, they sense possible danger (inference), and change their behavior (active) by slowing down and scanning the area. Like the brain, VERSES Genius™ has been designed to tackle problems with this process of active inference . Genius interacts with the world and continually updates, allowing it to adapt to a volatile, uncertain, complex, and ambiguous world. Like humans, it can solve problems by sensing, planning, acting, and sharing. Our breakthroughs Beating OpenAI and DeepSeek In winter 2024/2025 we showed that Genius could learn quickly - and that it could beat competitors playing Mastermind and Atari computer games. This proved that Genius could achieve better results at lower costs that leading AI m odels such as Iris (a transformer model), OpenAI’s o1-preview and DeepSeek R1. The world’s first digital brain beats Google DeepMind In June 2025 we unveiled a modular digital brain , which played classic computer games 60% better and 97% more efficiently than Google’s leading AI models. This performance is driven by models that quickly adapt to new information without retraining.   2025/6 Finance Over 2025/6 we worked with a large global asset manager (>$100bn AUM) to show how Genius can help them achieve higher returns while managing risk in compliance with their governance policies. Up next... ARC-AGI-3 VERSES is developing further modules for Genius which will allow us to solve complex problems, such as those tested by the ARC-AGI-3 challenge . ARC-AGI 3 is a test of human-like intelligence. It does this by setting tasks that are deliberately designed to show the gap between cutting edge AI systems and human intelligence. The underlying philosophy behind it is that intelligence is simply “how quickly can you learn new skills?” Contact us Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Investors Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube Investor Resources VERSES is a cognitive computing company building next-generation agentic software systems modeled after the Wisdom and Genius of Nature. Our What Agentic Enterprise Intelligence.™ Smarter Predictions and Decision Automation for Business. . Our How Smarter by Nature.™ Genius is based on first principles found in physics and biology. Our Why Imagine a Smarter World.™ Where people and technology work in greater harmony to generate shared prosperity. VERSES is now trading on OTCQB: VRSSF  and  CBOE: VERS Investor Presentation Genius Q&A with CTO & CFO Hari Thiruvengada and James Christodoulou answer some common questions about Genius. Investor Presentation Two Page Overview Board of Directors Michael Blum Jon De Vos David T. Scott VERSES Monthly Newsletter April 2026 Prodigii Licenses VERSES® Technology April 2026 call transcript and recording Financials VERSES AI Inc. fiscal year begins April 1st and ends March 31st Financials 2026 2025 2024 2023 Q4 Q3 Q2 Q1 Management Discussion & Analysis (VHI) Management Discussion & Analysis (VTU) Financial Statements (VTU)FINANCIAL STATEMENTS for the years ended MARCH 31, 2021, 2020 and 2019 Q4 CONSOLIDATED FINANCIAL STATEMENTS for the period from incorporation on November 19, 2020 to March 31, 2021 Financial Statements Q4 Q3 Q2 Q1 Financial Statements Management's Discussion & Anallysis Q1 Q2 Q3 Q4 Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Q1 Q2 Q3 Q4 Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Q1 Q2 Q3 Q4 Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Financial Statements Management's Discussion & Analysis Q1 Q2 Form 10-Q Management's Discussion & Analysis Form 10-Q Management's Discussion & Analysis Annual Reports 2025 10-K Corporate Governance Articles of the Company Audit Committee Charter Mandate of the Board of Directors Certificate of Incorporation Certificate of Change of Name Compensation Committee Charter Majority Voting Policy Nominating & Corporate Governance Committee Charter Notice of Articles   Life Offering LIFE Offering Document - Nov 6th, 2024 LIFE Offering Document - Sep 20th, 2023   For all financial and other information filed with the SEC see EDGAR Questions? Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Get in touch Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube Canada 205 - 810 Quayside Drive New Westminster, BC V3M 6B9 Canada   Europe High Tech Campus 6a 5656 AE Eindhoven, The Netherlands   North America 2121 Avenue of the Stars, 8th Floor Los Angeles, CA 90067 Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Genius Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube More Reliable Predictions and Smarter Decisions for Business. Problem Gartner predicts 50% of AI models will be domain-specific* by 2027 yet t oday’s general purpose AI models fail to reliably solve domain-specific problems . Solution Genius is a cutting edge agentic enterprise intelligence platform for rapidly building reliable domain-specific predictions and decisions and is particularly useful for problems that involve volatility, uncertainty, complexity  or ambiguity .  Who is Genius for? Genius is designed from the ground up to accelerate time-to-value for  machine learning researchers, engineers and data scientists working on  enterprise-class challenges. Signup Genius is designed to help machine learning professionals and data scientists Build causal models that learn continuously in real time Generate predictions that are more reliable , more explainable and more efficient Accelerate time from data to decisions from weeks to days "50% of AI models will be domain-specific" Gartner Genius Features Drag & Drop Interface* An intuitive no-code graphical user interface designed to help you model the causal relationships between factors and variables. *Bayes Nets only Guided Workflows User guidance to help you build models and run inference fast. Build Domain-Specific Models Upload CSV datasets to extract variables, factors and parameters and export as JSON to perform inference. Continual Learning & Explainability Train, measure and update explainable models continuously Visualize Inference Quantify uncertainty and express probabilistic predictions of likely outcomes. Automated Binning Group and categorize data in order to simplify analysis and optimize inference. Graceful Exception Handling Troubleshoot and debug errors, build resilient models and recover faster for a smoother development experience. Genius Capabilities Model & Validation Tooling Intuitive low-code user interface designed for modeling domain-specific causal models on which to perform reasoning, planning and learning using Active Inference. Enhanced Inference & Learning Advanced reasoning and planning using Bayesian inference mechanisms. Customer Success Model & Validation Tooling Intuitive low-code user interface designed for modeling domain-specific causal models on which to perform reasoning, planning and learning using Active Inference. Enhanced Inference and Learning Advanced reasoning and planning using Bayesian inference mechanisms. Lifecycle User Enablement Simplified deployment, tutorials and reference examples. Enterprise Ready Manage users, licenses, agents and support tickets. Developer Friendly Well-documented SDK and APIs along with customer success support. Lifecycle User Enablement Simplified deployment, tutorials and reference examples. Enterprise Ready Manage users, licenses, agents and support tickets. Developer Friendly Well-documented SDK and APIs along with customer success support. Modeling & Validation Tooling Intuitive low-code user interface for modeling and structuring cause and effect in data. Performance Characterization Command tool for monitoring model and inference performance in real time. Enhanced Inference and Learning Advanced Reasoning and planning using Bayesian inference mechanisms. Lifecycle User Enablement Simplified deployment, tutorials and examples for enabling users get time to value. Easy to Install & Use Kubernetes containers for easy deployments. Analytics & Telemetry Monitor user activity to enable data-driven decision-making. Intelligence Powered by Genius Agents powered by Genius have agency and autonomy can act as the intelligent interface to knowledge repositories, systems, devices, other AI and ML models and even other agents. Unlimited Potential Transcend the limitations and shortcomings of conventional machine learning. Hallucinations Drift Static models Sample inefficiency Sensitivity to noise Black box Hardware inflexibility Lack of uncertainty quantification Goodbye bots, pre-training, black box, fragile, oceans of data, energy intensive, tedious rework Hello agents, continual learning, explainable, flexible, sample efficiency, sustainable, rapid prototyping Instant Insight Make sense of your data in minutes not weeks. Rapidly ideate and validate Bayesian models for inference that explicitly map causal relationships. Build agents that respond to dynamic environments with online learning and planning based on real time observations and explainable decisions. Streamline complex integrations and deployments. ML Researchers can spend less time implementing and more time experimenting. ML Engineers can spend less time adjusting models that fail to perform in the face of complexity and uncertainty. Adaptable Continual and online learning Autonomous Self-directed goal setting & decision making Composable Modular and reusable knowledge models Efficient Requires few samples and minimal compute Explainable Transparency into how predictions and decisions are made Flexible Powerful specialized GPUs are not required but optional Interoperable Shared knowledge means better decision making Reliable Predictions have confidence score qualifiers Resilient Fault tolerance and able to recover from failure Scalable Run in the cloud or at the edge Sustainable Less compute and less retraining means less energy Quantify Uncertainty Thrive in spite of of noisy, sparse or missing data At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii Beyond smart.  Genius. Genius enables agents to generate predictions that are highly reliable, explainable and sustainable. Reliable Predictions have confidence score qualifiers Explainable Transparency into how predictions and decisions are made Sustainable Less comp News Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube News Filter by Tag Show All Benchmarks Beta Davos Event Financial Services In The News Leadership Newsletter Press Release Research Robotics news VERSES Monthly Newsletter April 2026 Newsletter Read More Prodigii Licenses VERSES® Technology news Financial Services Read More April 2026 call transcript and recording Leadership Event Read More VERSES® Expands Commercial Relationship with Major Global Investment Firm news Financial Services Read More VERSES® Announces Closing of Second Tranche of Private Placement Offering of Units news Read More VERSES Monthly Newsletter March 2026 Newsletter Read More VERSES® Announces Closing of Private Placement Offering of Units news Read More VERSES in IEEE Spectrum In The News Robotics Read More March 2026 call transcript and recording Leadership Event Read More February 2026 Earnings call transcript and recording Leadership Event Read More 1 2 3 4 5 Next Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. 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Return to top In the Media Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube In the Media Newsletter Signup Financial Times AI models must adapt or die   Devansh Substack How a Tiny AI Startup is beating OpenAI by Redefining Intelligence New York Times The Fever Dream of Imminent ‘Superintelligence’ Is Finally Breaking Diginomica Why Karl Friston is betting on cultivating curiosity for sustainable AGI The Robot Report VERSES multi-agent robotics model works without pre-training IEEE Spectrum Here Comes the World Wide Web of Everything The Spatial Web standard connects devices, robots, and AI agents Psychology Today Designing Artificial Consciousness from Natural Intelligence Psychology Today The Singularity Is Here Diginomica How the Spatial Web could guide de-centralized and trustworthy agentic AI to like us more Psychology Today AI Isn’t a Brain but Soon It Could Be WIRED A Deep Learning Alternative Can Help AI Agents Gameplay the Real World Popular Mechanics This AI Model Can Mimic Human Thought—And May Even Be Capable of Reading Your Mind, Scientists Say Diginomica What is the Spatial Web, and why should the enterprise care? Here's why Cboe Spotlight VERSES Analyst Mentions Jan 2025 Emerging Tech Impact Radar: 2025 Emerging Tech Impact Radar: Smart Home Feb 2025 Emerging Tech Impact Radar: Generative AI April 2025 Emerging Tech Impact Radar: Edge Artificial Intelligence May 2025 Emerging Tech: Techscape for Startups in Intelligent Simulation June 2025 Emerging Tech Impact Radar: Disruptive Technologies in the Far Horizon Hype Cycle for Artificial Intelligence, 2025 Hype Cycle for Deep Technologies, 2025 Hype Cycle for Oil and Gas, 2025 Hype Cycle for User Experience, 2025 July 2025 Hype Cycle for Data Science and Machine Learning, 2025 Hype Cycle for Generative AI, 2025 Hype Cycle for Smart City and Sustainability in China, 2025 August 2025 Hype Cycle for Emerging Technologies, 2025 Innovation Insight: World Models Are Set to Empower AI Agents With Imagination October 2025 Emerging Tech Impact Radar: Spatial AI November 2025 Emerging Tech Impact Radar: Physical AI   Gartner and Hype Cycle are a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Mastering Atari Games with Natural Intelligence Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 11 min read Mastering Atari Games with Natural Intelligence Steven Swanson : Jan 20, 2025 1:07:25 AM Active Inference Benchmarks Agents RESEARCH PREVIEW Genius™-Powered Agents Outperform World’s Leading AI Algorithms with New Approach in Industry Benchmark   Highlights Atari 100k benchmark tests the ability of software agents to efficiently learn to meet or exceed human performance on a variety of classic video games VERSES’ agent, powered by Genius, trained for 2 hours on 90% less data than Atari 100k (thus Atari 10k) on several games Genius Agent matched or exceeded performance of top ranking models that were trained on 10x the data and many times more compute Genius Agent exceeded human-level performance and achieved perfect play (scoring all 20 points) at Pong multiple times Genius Agent outperformed a leading model, IRIS with 96% smaller model size The same Genius Agent framework trained on 2 other Atari Games exhibited superior competency Our results are preliminary and unoptimized but mark a milestone demonstration of the first hyper-efficient Bayesian-based agent successfully solving high-dimensional games in a generalized way   Introduction The quest to create systems that exceed human-level intelligence dates back to ancient-mythology and in the modern era games have become a useful benchmark for machine intelligence.  In 1996 IBM’s Deep Blue defeated Garry Kasparov at chess, a game with well-defined rules and discrete state space.  Systems like Deep Blue excel at brute-force computation, evaluating millions of possible moves to find the best one.  In 2016 Google’s AlphaGo defeated Lee Sedol at the game of Go, a game with more possible board configurations than there are atoms in the universe.  AlphaGo and its predecessor AlphaZero demonstrated the power of deep reinforcement learning and Monte Carlo tree search and marked a leap from brute-force computation to AI capable of pattern recognition and strategic planning. Conquering Chess and Go are milestone achievements, that demonstrated a form of complex decision-making environments; however, they don’t emulate the complex dynamics of the real-world where change is constant and one must adapt to shifting conditions and rules.  In order to raise the bar, video games emerged as a critical test for intelligence as they offer interactive environments with dynamic rules.  Success must be learned through direct interaction and successful gameplay requires effective planning.  Thus, Atari games emerged as the gold standard for assessing an agent’s ability to model and navigate complex dynamic systems across multiple environments. In 2013, Deepmind released a paper outlining how their reinforcement learning based model, DQN, could play Atari games at human or super human levels, however it required hundreds of millions of environment steps to train (Google acquired Deepmind for $500 million in 2014).  In 2020, Google Deepmind published a paper on how its Agent57, based on deep reinforcement learning, surpassed the human baseline gameplay across multiple Atari games; however it required training on nearly 80 billion frames, the equivalent of thousands of years of human training.  This kind of brute-force "data-inflation" motivated the AI community to seek a new benchmark that required AI models to attempt to learn more like humans and within realistic human time frames.  Thus, the Atari 100k challenge was created as a more sample-efficient benchmark with the aim of achieving better than human level gameplay competency using just 100,000 data samples, the rough equivalent to 2 hours of human gameplay training. In early 2024, we stated our goal of demonstrating how agents, powered by Genius, could match or exceed performance of state-of-the-art methods on the Atari 100k Challenge using 1/10th of the amount of training data (thus Atari 10k) and dramatically less compute while generating a model a fraction of the size of top ranked models powered by deep reinforcement learning (DRL) or transformers. If, metaphorically, the methods used by DQN and Agent57 are gas-guzzling Hummers and those used to tackle the Atari 100k challenge are like a fuel-efficient Prius, then our approach used on Atari 10k is like a Tesla, a hyper-efficient alternative architecture.  Before diving into the details, let’s take a moment to understand why Atari 10k is such a big deal.   Why Atari 10k Matter The original Atari 100k benchmark was established to test an agent’s ability to excel, with limited training data, in three critical areas: interactivity, generalization, and efficiency. Interactivity measures how well an agent learns and adapts in dynamic environments where its actions directly influence outcomes. In Atari games, agents must act in real time, respond to feedback, and adjust their behavior to succeed. This mirrors real-world scenarios, where adaptability is essential. Generalization evaluates an agent’s ability to apply learned strategies across diverse games with different rules and challenges. It ensures the agent isn’t overfitting to a single task and can perform well across various domains, demonstrating true adaptability. Efficiency focuses on how quickly an agent can learn effective strategies with limited data and compute. The 100k step constraint highlights the importance of learning efficiently—critical for real-world applications where data is often scarce. Any competent and experienced developer can write a custom program to solve games and logic puzzles.  As evidenced by Deepmind’s original DQN approach and even the more efficient Atari 100k, with enough human intervention and brute force data and compute, conventional machine learning can be tuned and fitted to master games like Atari’s.  In contrast, Genius Agents demonstrate the same capabilities but, crucially, by figuring out how to play games on their own, and with 90% less data than Atari 100k, which is more directly relevant to real-world problems where data can be sparse, incomplete, noisy, and change in real time.   What Were the Results? In order to provide an apples-to-apples comparison against state-of-the-art (SOTA) machine learning, for these initial tests, we selected model-based, IRIS, which is based on the breakthrough transformer architecture which in turn is the foundation of Generative AI and LLMs like GPT, Claude, Gemini, Llama, Grok and others.  It was the fastest for us to deploy so that we can focus energy on advancing our own research rather than reproducing others.  The top 2 performers on Atari 100K, EfficientZero and BBF, are based on Deep Reinforcement Learning, the same approach that underpins work such as Deepmind’s AlphaZero, AlphaGo and AlphaFold. We trained both our Genius Agent and IRIS on 10,000 steps of gameplay for 2 hours (10k/ 2h ).  We benchmarked the performance of Genius Agent 10k/2h against IRIS, which trained on the same 10k steps – but over 2 days (10k/ 2d ).  We compared Genius Agent 10k/ 2h performance steps as measured by Human Normalized Score (HNS) against the publicly available results for BBF and EfficientZero that were trained on the full 100k.  HNS 1.0 is considered human-level performance and equivalent to a human player scoring, for example, an average of 14.6 points in Pong against the computer after 2 hours of practice time (roughly the equivalent of 100k samples). Unlike transformers and DR Active inference explained Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 1 min read Active inference explained Rob Blackie : Nov 27, 2025 2:12:39 AM Active Inference World Model Predictive Coding Models Our brains constantly make predictions about the world around us. We assess what we’re uncertain about, and what we need to resolve. Once our brains model the world, they learn over time and continuously improve. When you walk toward a crosswalk, your brain predicts whether cars will stop. If you’re unsure, you look both ways, wait, or step back. Your brain updates its understanding every moment until you feel safe to walk. That cycle—sense, predict, update, act—is active inference in motion, a continuous predict-and-act loop that is deeply coded into humans, allowing us to adapt and improve. Our brains are highly efficient, operating on only 20 watts, just enough to power a light bulb. Most AI systems today fail to retain feedback, adjust to context, or improve over time. What's missing, as a recent MIT study pointed out , are systems that adapt, remember, and evolve.   Active inference can fill this role. It’s how all natural systems —humans, animals, plants—address the volatile, uncertain, complex, and ambiguous world around them. But these approaches were long thought impossible to scale in computing.   This is what we have tackled with Genius™ : Agents that adapt, learn, and get smarter in real time. Visit our models to find out more.    This prediction-action update cycle is how people as well as Genius-powered agents learn and adapt in real time. Agents experiment, data comes in, relevant information is absorbed, while noise is ignored.   Tweet Our models World Model Predictive Coding Models AI in Finance: Addressing the Failure of Current Models (Uncertainty, Risk, and Trust) Active Inference World Model Predictive Coding Models Genius can sense, think, act, and share intelligently Active Inference Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Be yourself, with us. Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube Be yourself, with us. You're invited to do your best work on important problems with exceptional people . Vision A Smarter World where people and technology work in greater harmony to generate shared prosperity . Mission Elevate human potential through intelligent tools that radically improve our mutual understanding .   Our Culture We are a globally distributed, fully-remote collective of individuals from a wide spectrum of backgrounds and disciplines that are passionate about empowering ecosystems of intelligence through the products we imagine and create. Taking care of our people is priority number one and we place an emphasis on fostering an inclusive and safe environment where people flourish.  We feel a strong sense of purpose in our mission, belonging, and job satisfaction.  We believe that great company cultures – of the type we are cultivating – organically lead to a stimulating and diverse environment.   Our Culture We are a globally distributed, fully remote, and autonomous collective of individuals from a wide spectrum of backgrounds and disciplines that are passionate about empowering ecosystems of intelligence through the products we imagine and create. Taking care of our people is priority number one and we place an emphasis on fostering an inclusive and safe environment where people flourish.  We feel a strong sense of purpose in our mission, belonging, and job satisfaction.  We believe that great company cultures – of the type we are cultivating – organically lead to a stimulating and diverse environment. The Total Package Work-life integration.   Flexible schedules, adequate vacation time, and support for personal needs. Professional development.   Training programs, mentorship opportunities, and career advancement paths. Compensation and benefits.  Competitive salaries, comprehensive health insurance and other perks. Positive work culture.  Open communication, mutual respect, recognition for achievements, and opportunities for collaboration.  Employee wellbeing.   Addressing mental health concerns, promoting physical health initiatives, and creating a supportive environment. Competitive total reward program Health, dental, and vision insurance Life and AD&D insurance Stock options Responsible time off Parental leave Remote work culture Stock options Competitive total reward program Health, Dental, and Vision insurance Life and AD&D insurance Stock options Responsible time off Parental leave Remote work culture Career growth and development Our Values Audacity Fortune favors the bold. LATIN PROVERB Assertiveness Taking initiative pays off. It is hard to visualize someone as a leader if she is always waiting to be told what to do. SHERYL SANDBERG Alignment The task of leadership is to create an alignment of strengths in ways that make weaknesses irrelevant. PETER DRUCKER Accountability A sense of ownership is the most powerful weapon a team or organization can have. PAT SUMMITT Abundance The needs of the many outweigh the needs of the few. SPOCK Awesomeness The only secret of magic is that I'm willing to work harder on it than you think it's worth. PENN JILLETTE Recruitment Agencies & Firms We do not accept agency resumes.  Please do not forward resumes to our employees.  We are not responsible for any fees or overhead related to unsolicited resumes.   Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top VERSES® Genius™ Outperforms DeepSeek R1 Model in Code-Breaking “Mastermind” Challenge Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 5 min read VERSES® Genius™ Outperforms DeepSeek R1 Model in Code-Breaking “Mastermind” Challenge VERSES : Feb 4, 2025 6:12:41 AM Press Release Demonstration of Multi-Step Reasoning by VERSES Genius Agent Beats China’s Top AI Model in Accuracy, Speed, and Cost Efficiency Vancouver, BC – 4 February   2025– VERSES AI Inc. (CBOE:VERS) (OTCQB:VRSSF) ("VERSES'' or the "Company”), a cognitive computing company specializing in next-generation intelligent agent systems, provides an update to its previously announced “Mastermind” challenge where the Company’s flagship product, Genius outperformed OpenAI’s o1-preview model.  In this latest demonstration, VERSES demonstrates Genius, winning the code-breaking game Mastermind in a side-by-side comparison with China’s leading AI model, DeepSeek’s R1, which has been positioned as a disruptive, more cost-efficient large language model (LLM). Genius significantly outperformed DeepSeek’s R1 model while performing two hundred and forty-five ( 245 ) times faster and more than seven hundred and seventy-nine ( 779 ) times cheaper. In the challenge, VERSES compared the DeepSeek-R1 model to Genius. Each model attempted to crack the Mastermind code on 100 games within up to ten guesses. Each model was given a hint for each guess and must reason about the missing part of the correct answer, requiring all six code colors to be correct to crack the code. For perspective, you can play the game at mastermindgame.org. The highlighted results from VERSES demonstration are below: Metric Genius R1 Success Rate 100% 45% Total Compute Time (100 Games) 5m 18s (Avg 3.1s per game) 26 hours (Avg 334s per game) Total Cost for 100 Games $0.05 (est) $38.94 Hardware Requirements Mac M1 Pro GPU Cloud $ are USD, (s) are seconds Genius Performance Highlights Accuracy and Reliability. Genius solved the code 100% of the time in a consistent number of steps. DeepSeek solved the game only 45% of the time. Speed. Genius consistently solved games in 1.1–4.5 seconds , while DeepSeek’s solve times averaged 934 seconds (approximately 15.5 mins) Efficiency. Genius’ total compute time for 100 games was just over 5 minutes , compared to DeepSeek’s 26 hours . Cost. Genius’ compute cost was estimated at $0.05 USD for all 100 games, compared to DeepSeek’s R1 model at $38.94 USD. In summary, Genius consistently outperformed DeepSeek’s model two hundred and forty-five (245) times faster and more than seven hundred and seventy-nine (779) times cheaper. A more detailed description and methodology of the test is on the Company’s blog at verses.ai . “This is a good showcase of Genius’ domain-specific model’s advanced performance in a multi-step reasoning problem,” said Hari Thiruvengada, VERSES Chief Technology Officer. “Mastermind was the perfect choice for this test because it requires multi-step logical reasoning, predictive cause-and-effect understanding, and dynamic adaptation to crack the code. This exercise highlights how Genius excels in step-by-step reasoning by leveraging domain agents using a Bayesian approach and Active Inference.” “Many AI models struggle to efficiently deliver results and as demonstrated in this challenge, Genius’ ability to perform multi-step reasoning and dynamically adjust to feedback is crucial for enabling agents that are not only more efficient but, more importantly, accurate and reliable enough to operate in dynamic real-world scenarios,” said Gabriel René, Founder and CEO of VERSES. “While this test highlights the competitive advantages of our technology, we believe Genius is also highly complementary to LLMs—enhancing their capabilities by providing the additional ‘brainpower’ needed to make AI agents smarter and more trustworthy within their domains. If DeepSeek’s R1 signals the commoditization phase of LLMs, demonstrating that general-purpose, open-source models can now be made much cheaper, we believe that this latest Mastermind test is the signal that the domain-specific models—uniquely enabled by Genius—are the missing links needed to make AI agents truly reliable. We believe this ”last mile” challenge of AI—accuracy— is the key to unlocking adoption across the Fortune 500 market and beyond.” concluded Mr. René. Mastermind™ is a registered trademark of Pressman Inc.   About VERSES VERSES is a cognitive computing company building next-generation agentic software systems modeled after the wisdom and genius of Nature.  Designed around first principles found in science, physics and biology, our flagship product, Genius ™, is an agentic enterprise intelligence platform designed to generate reliable domain-specific predictions and decisions under uncertainty.  Imagine a Smarter World that elevates human potential through technology inspired by Nature.    Learn more at  verses.ai ,  LinkedIn  and  X . On behalf of the Company  Gabriel René, Founder & CEO Press Inquiries: [email protected]  Investor Relations Inquiries  James Christodoulou, Chief Financial Officer [email protected]   (212) 970-8889 Cautionary Note Regarding Forward-Looking Statements When used in this press release, the words "estimate", "project", "belief", "anticipate", "intend", "expect", "plan", "predict", "may" or "should" and the negative of these words or such variations thereon or comparable terminology are intended to identify forward-looking statements and information. Although VERSES believes, in light of the experience of their respective officers and directors, current conditions and expected future developments and other factors that have been considered appropriate, that the expectations reflected in the forward-looking statements and information in this press release are reasonable, undue reliance should not be placed on them because the parties can give no assurance that such statements will prove to be correct. The forward-looking statements and information in this press release include, among other things, statements regarding the potential impacts of Deepseek R1 on the AI technology market, the expected future development of Genius and AI technology and potential adoption of AI technology. There are risks and uncertainties that may cause actual results to differ materially from those contemplated in those forward-looking statements and information. In making the forward-looking statements in this news release, the Company has applied various material assumptions. By their nature, forward-looking statements involve known and unknown risks, uncertainties and other factors which may cause our actual results, performance or achievements, or other future events, to be materially different from any future results, performance or achievements expressed or implied by such forward-looking statements. There are a number of important factors that could cause VERSES' actual results to differ materially from those indicated or implied by forward-looking statements and information. Such factors may include, among other things, that Deepseek R1 will continue to impact the AI technology market; that Genius and AI technology generally will continue to development as currently anticipated by management; and that developments in AI accuracy will increase adoption. The Company undertakes no obligation to comment on analyses, expectations or statements made by third parties in respect of its securities or its financial or operating results (as applicable Genius Outperforms OpenAI Model in Code-Breaking Challenge, Mastermind Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 4 min read Genius Outperforms OpenAI Model in Code-Breaking Challenge, Mastermind Steven Swanson : Dec 18, 2024 6:00:00 AM Active Inference Benchmarks Research Agents In a head to head test of reasoning abilities using the code breaking game, Mastermind, against OpenAI’s o1-preview, an agent powered by Genius solved 100% of the games, 140 times faster and 5260 times cheaper. The term reasoning is being used by many AI companies as a catch-all way to reference the processing under the hood.  OpenAI promotes its new o1 model as having advanced reasoning, where it (and other LLM-based models) use a new kind of “inference-time-compute” whose improved results over GPT models alone are the basis for an argument that these reasoning AI models have a form of language-based reasoning.  However, research from Apple and others suggest that this form of reasoning may struggle to reliably model cause and effect relationships thus leading to issues with accuracy.  Regardless, there are many problem spaces that require a class of reasoning where language based reasoning is not enough and where reliability, accuracy and auditability are critical. ...current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data. - Apple Researchers Speaking at the 2024 K-Science and Technology Global Forum in Seoul, Meta Chief Scientist, Yann LeCun said, “LLMs can deal with language because it is simple and discrete, but it cannot deal with the complexity of the real world,” he explained.  “These systems lack the ability to reason, plan, and understand the physical world in the same way humans do.” There are in fact 7 types of reasoning but in the context of agentic software we define reasoning as: The ability for an agent to run inference on a generative model in order to deduce the likelihood of a cause or effect based on past data. Cybersecurity, fraud detection, stock markets, and weather forecasting among others are complex dynamic systems with complicated cause-effect relationships, surprises, and hidden factors and variables where the “right” answer must be probabilistic in nature rather than absolute because the available information is incomplete, unclear, unprecedented, or otherwise fuzzy.  Predicting future effects within these systems requires learning causality and quantifying uncertainty as opposed to reconstructing predictions based on correlations in past data.  As the saying goes, correlation does not imply causation. With that in mind, we decided to compare how o1-preview* and Genius fare when reasoning a fuzzy problem.  Mastermind is a code breaking game where one player creates a secret code using colored pegs, and the other player must guess the code by making logical deductions based on feedback about the color and position of their guesses.  Learn more about the game or play it at mastermindgame.org . We let o1-preview and Genius each play 100 games with up to 10 guesses using a pool of 6 colors for the four-code secret. Our findings are as follows: Metric Genius o1-preview Success Rate 100% 71% Avg Compute Time per Game 3.1 seconds 345 seconds Avg Guesses per Game 5.6 6.1 Total Compute Time (100 Games) 5m 18s 12.5 hours Avg Cost per Game $0.0005 (estimate) $1.96 Total Cost for 100 Games $0.05 (estimate) $263 USD** Hardware Requirements Mac M1 Pro laptop OpenAI Servers o1-preview failed to guess the secret code on 29% of the games within 10 guesses and of the 71 that it succeeded on, the average compute time was 345 seconds and the average number of guesses was 6.1 Genius solved all 100 games with an average of 3.1 seconds and with 5.6 guesses on average. o1-preview’s solve time on successful games varied from 7.9 to 889 seconds while Genius solve time ranged from 1.1 to 4.5 seconds The combined total time that o1-preview spent solving all 100 games was more than 12.5 hours while Genius spent 5m 18s Based on the number of tokens o1-preview sent back and forth over the API for each guess and the cost per token, we calculated the average cost of each successful game at $1.96 **  Failed games cost more on average because they used up all 10 guesses so we excluded them, however, it should be noted that the 29 failures incurred 47% of the total spend. We estimate the total cost for Genius to run all 100 games was nominal, perhaps $0.05 , given the hardware (see further down) and minimal energy and compute time compared to a total of $263 USD for o1-preview. One observation is that Genius always started with the exact same guess on all 100 games (RRBB) and there is a consistent pattern in subsequent guesses when guessing the same secret code multiple times which demonstrates a principled and systematic approach to reasoning. In contrast, in 25 of the games using the same secret code (GYPO), o1-preview started with a "one of each color" strategy (RBGY) 19 times and the "two of each color" strategy (RRGG) 6 times.  However, in subsequent guesses over the 25 games, there is no apparent pattern and instead the guesses appear random and unpredictable.  o1-preview did not solve 7 of the 25 games within 10 guesses and in the 18 games where it did solve the code, the number of guesses varied from 3 to 10 and solve times ranged from 50 sec to 660 sec. Following is a side by side time-lapse comparison between the two methods.   For the exercise Genius ran on a commodity 2021 Apple Mac with an M1 Pro with 64 gb RAM from 2021 whereas the hardware that OpenAI runs o1-preview on is unknown.  Research and OpenAI’s pricing suggest it’s likely to be running on powerful A100 or H100 GPUs, possibly even a cluster of them.  Given the orders of magnitude difference in compute and power between an M1 and an A100, an apples to apples comparison could significantly widen the gap in effectiveness and efficiency with which Genius outperformed o1-preview. The advanced reasoning required to crack the code in Mastermind is representative of the capabilities available in Genius today and that underpin the Atari 10k challenge that we are eager to share.  Outperforming o1-preview by such a wide margin exposes inherent limitations with the correlational language-based reasoning of LLMs when tackling tasks that require logical cause-effect reasoning and planning. Notes *At the time of the test and this writing, even though the full version of o1 was released for chatGPT Pro subscriber’s interface ($200/month) only o1-preview was available through OpenAI’s API/SDK. **o1-preview cost calculations were arrived at by multiplying the number of tokens by the cost per token. We removed o1-preview’s failed games from our calculations of time, cost, and number of guesses to avoid skewing the numbers. If o1-preview were allowed to exceed the 10 guess limit it likely would have solved all 100 games but the time and cost associated would have been higher. Detailed results are available here . Tweet Whitepaper: Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Active Inference Benchmarks Research Agents Mastering Atari Games with Natural Intelligence Active Inference Benchmarks Agents Real World Intelligence: These Are the Droids You’re Looking For Active Inference Benchmarks Agents Robotics Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights res AI in Finance: Addressing the Failure of Current Models (Uncertainty, Risk, and Trust) Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 2 min read AI in Finance: Addressing the Failure of Current Models (Uncertainty, Risk, and Trust) Rob Blackie : Feb 10, 2026 4:45:58 AM Active Inference World Model Predictive Coding Models Investment teams don’t struggle to find models. They struggle to trust them—especially when market regimes shift (e.g. during a deep recession or time of high inflation). This blog summarizes what we have developed, what we learned from the evidence, and why an uncertainty-aware approach can help teams evaluate portfolio decisions without jumping to automation. This work uses backtests on customer provided data. The recommendations have been developed as an input to human review by fund and risk management teams.   The problem: why adoption stalls AI adoption in portfolio decisions usually breaks for practical reasons: Risk constraints are real. A strategy that looks good without guardrails can fail once you apply volatility, drawdown, and turnover constraints. Trust requires repeatability. Teams need to rerun the same experiment, compare against the same baselines, and see stable behavior across stress windows. Markets change behavior. Correlations shift and uncertainty increases, and models that assume “normal conditions” can become brittle. Explainability. Multi-billion-dollar investment decisions cannot be made using a black-box system without understanding why it makes those recommendations.   The challenge: uncertainty and unexpected events Markets don’t just move—they change how they move. During calm periods, many approaches look stable. During times of market stress, relationships can shift quickly, and that’s where decision support is really tested. A second challenge is signal quality. It’s easy to add indicators that help explain history. It’s harder to find signals that consistently help decisions going forward—especially once you apply constraints. In our work, some added indicators improved fit to historical data but did not consistently improve portfolio outcomes—an important negative result that helps narrow what’s worth testing next.   How Genius TM works The goal of VERSES Genius isn’t perfect prediction. It’s to estimate a range of plausible outcomes, update those estimates as data changes, and propose human-reviewable portfolio weights that can be evaluated under guardrails. While the exact path of asset prices can’t be fully predicted, just knowing when the market is becoming riskier, or less risky, opens up opportunities. For instance, our model can quantify a risk as “equities are 80% likely to increase in price in the next month, and in the 20% scenario they are 90% likely to fall by less than 5%” and provide evidentiary support for these predictions based on market and macro indicators. By doing so, asset managers can make better judgments about the trade-offs between risk and return. Explicit quantification of risk is particularly valuable when it allows managers the confidence to quickly respond to major market changes, such as the “Global Covid Pandemic“ and “Russia-Ukraine War” crisis that triggered market shifts in 2020 and 2022. In situations like these, the initial warning signs will be low-probability events occurring more frequently than expected. This will then trigger our models to examine which, previously unlikely, hypotheses are now looking more probable, for instance, a relationship between two variables has reversed. It first alerts managers that risk has increased, then recalibrates as new data arrives, allowing predictions to become more confident over time.   Uncertainty quantification for asset classes Our models constantly update their estimates of uncertainty for three classes of assets over time, and the relative likelihood of each outcome. Asset 2 is a short term government debt class, so it exhibits low uncertainty, while asset 0 is a equity instrument exhibiting higher uncertainty, which can be seen especially in March to May 2020 as the COVID pandemic took hold.   What this means for investment teams The near-term value is not automation. It’s a disciplined way to evaluate decisions under uncertainty—using repeatable runs, clear baselines, and explicit guardrails. Instead of debating models in the abstract, teams can review evidence artifacts and agree on what holds up across stress windows. In our work with a large global asset manager, we have demonstrated that Genius can help them achieve higher returns while managing risk in compliance with their governance policies.   Book a Demo A short demo is the fastest way to walk through these models —based on evidence, not enthusiasm. Disclosure: This page is for informational purposes only.   Tweet Active inference explained Active Inference World Model Predictive Coding Models Our models World Model Predictive Coding Models Real World Intelligence: These Are the Droids You’re Looking For Active Inference Benchmarks Agents Robotics Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top VERSES Blog Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube VERSES Blog On agents, agentic systems and Genius Filter by Tag Show All AI Governance Active Inference Agents Artificial Intelligence Autonomous Vehicles Benchmarks Data Event Interview Leadership Models Predictive Coding Research Robotics Standards Use Case World Model spatial web VERSES featured in Gartner research 2026 Gartner reports When it comes to AI, Gartner is an indispensable resource for C-Level executives. Investor webinar February 2026 Leadership AI in Finance: Addressing the Failure of Current Models (Uncertainty, Risk, and Trust) Investment teams don’t struggle to find models.... Active Inference World Model Predictive Coding Models Use case: Robotics In Pixar’s WALL-E, a lonely robot evolves beyond... Robotics Use case: Spatial web The Spatial Web represents the next evolution of... Use Case spatial web Genius can sense, think, act, and share intelligently Genius™ has four modules: SENSE perceives the... Active Inference Active inference explained Our brains constantly make predictions about the... Active Inference World Model Predictive Coding Models Our models Inspired by the human brain, we scale active... World Model Predictive Coding Models Reflections on International Workshop on Active Inference 2025 The International Workshop on Active Inference... 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Filter by Tag Show All Active Inference Autonomous Vehicles Benchmarks Event Featured Research Robotics Sentience 4 min read Behavior Prediction under Occlusion Research Autonomous Vehicles 7 min read Why Learn if You Can Infer: Active Inference for Robot Planning & Control Active Inference Benchmarks Featured Robotics 11 min read AXIOM: Mastering Arcade Games in Minutes with Active Inference and Structure Learning Benchmarks Research 2 min read Beyond Imitation Games: A Falsifiable Emergent Sentience Framework Research Sentience 6 min read Benchmarking Predictive Coding Networks Made Simple Research 2 min read NeurIPS 2024 Retrospective Event 3 min read Variational Bayes Gaussian Splatting: A Bayesian Approach for Continual 3D Learning Research Featured 3 min read Gradient-free variational learning with conditional mixture networks Research Featured 4 min read Achieving Human-Level Atari Gameplay with Object-Centric Priors and Active Inference Active Inference Benchmarks Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Privacy Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube PRIVACY NOTICE Last Updated: June 1, 2024 VERSES, INC. (“VERSES”) recognizes the need to protect its users’ privacy rights. 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However, we may, from time-time, share PII with some affiliates that are wholly owned by, or under common control of, VERSES, in accordance with the restrictions outlined in this Privacy No Genius can sense, think, act, and share intelligently Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 5 min read Genius can sense, think, act, and share intelligently Rob Blackie : Nov 27, 2025 3:26:27 AM Active Inference Genius™ has four modules: SENSE perceives the world more like we do, integrating sensory information that gives computers the ability to truly “see,” process, and understand.  THINK serves as its digital brain, with modules for memory, prediction, and reasoning that work together to continuously refine an internal model of the world. ACT allows robots and agents to learn new tasks quickly in physical and digital worlds, without the extensive pre-training that conventional systems require. SHARE enables our agents, from traffic signals to drones to lunar rovers, to collaborate securely and share not just knowledge but also skills—what one learns can be distributed instantly. Together, SENSE , THINK , ACT , and SHARE form the sight, brain, body, and ecosystem of an AI that perceives, learns, adapts, and improves with experience.   SENSE SENSE makes live vision feasible on edge devices—from drones and autonomous vehicles to robotics —where real-time perception is critical. It treats perception more like our own five senses, integrating vision, motion, touching, and acceleration into a picture of what’s happening. The power isn’t just in collecting signals but in making sense of them. This ability to “see” comes from inferring what’s behind sensor readings—what in the world is generating the signals. Any modality can provide evidence: A camera gives you color and shape, a microphone gives you timing and echoes, a depth sensor gives you structure. The core task is to integrate incoming signals and update the understanding about what objects are present and how they are arranged. Variational Bayes Gaussian Splatting (VBGS ) is one concrete implementation of this idea. Instead of training a model through endless passes over data, VBGS updates its internal beliefs sequentially with each new observation, sharpening its 3D representation on the fly. Every signal—color, shape, position—sharpens the existing model and enables SENSE to maintain a map of the world—in other words, a world model that gets more accurate with every glimpse. But SENSE also needs a way to interpret, prioritize, and act on what it perceives, which is where hierarchical active inference comes in. Hierarchical active inference unifies two functions of the human brain that are usually treated separately: control, which pushes goals downward into concrete actions, and planning, which adjusts how strongly each goal should be pursued as conditions shift. These systems work together constantly, balancing competing demands rather than following rigid rules. Because SENSE is coupled to the agent’s control and decision-making, its signals immediately shape behavior. If it detects an obstacle, movement adjusts automatically; if something unexpected appears, higher-level processes reassess goals. At every level, sensing and acting form a continuous feedback loop. Picture an autonomous car. Traditional sensors only fuse what they can currently “see,” so if a pedestrian is hidden behind a van, the system has no data to work with. SENSE behaves differently: it maintains hypotheses about what might be present, predicting possible trajectories while integrating every new signal to update its beliefs. It fuses not just sensor readings but expectations, uncertainty, and goals. Instead of treating sensors as isolated modules, hierarchical active inference provides a shared goal engine. Goals guide what each sensor should attend to. Sensory signals update the system’s expectations. The result is a kind of sensor fusion: a unified, context-aware model of the world that adapts in real time and continuously shapes action.  VERSES has developed a solution called Variational Bayes Gaussian Splatting (VBGS), which builds a 3D world from billions of tiny probabilistic Gaussians—each storing color, shape, and position. As new evidence arrives, these blobs shift and refine, letting the system update its map in real time without overwriting what it knows.   THINK We recently unveiled what we believe is the world's first digital brain, AXIOM . Designed to mirror the modular structure of the human brain, AXIOM develops an understanding of its world and how to operate within it. Learning is central to its design. Over time, AXIOM’s models grow more efficient, pruning what they no longer need while continuously adapting. The result is a system that becomes smarter, more efficient, and more reliable with experience. More reliable because our models learn over time. More efficient because they can learn from fewer interactions, the way humans only have to see something new a few times to recognize it. Or how a toddler repeatedly dropping a spoon on the ground might be refining their understanding of gravity. This capacity to simplify mirrors natural intelligence. Recent benchmarking results showed AXIOM to be up to 60% more reliable, 97% more efficient, and 39 times faster at learning than Google DeepMind’s DreamerV3.  AXIOM MODULE AXIOM FUNCTION BRAIN REGION Slot Mixture Model (sMM) Vision Converts raw pixels into separate objects. Occipital Lobe Identity Mixture Model (iMM) Memory & Identity Recognizes and tracks objects over time. Temporal Lobe Transition Mixture Model (tMM) Prediction & Planning Forecasts how things move. Frontal Lobe Recurrent Mixture Model (rMM)  Reasoning Links cause to effect (if this, then that). Frontal/Parietal Lobe   AXIOM mirrors the brain with mixture models that uncover hidden structure, enabling perception, identity, memory, motion, and planning to work together in a continuous loop of reasoning, action, and learning.   ACT  In August 2025, we published results of our robotics model , which outperformed other models on Meta’s Habitat benchmark simulation without any pre-training. Across three tasks—tidying a room, preparing groceries, and setting a table—the VERSES model achieved a 67% success rate, surpassing the previous best alternative of 55%. Unlike a deep-learning robotics model that required imitation-based pre-training with more than 1.3 billion steps to acquire these skills, the VERSES model adapted and learned in real time. With ACT, it thinks on its feet. Just as a human might enter a room, assess its architecture, and map where items in a kitchen might be located based on models of how the world is typically arranged, the VERSES model tidied a room and set the dining table without pre-training. This breakthrough is “exciting…offering an alternative approach, ” said Sean Wallingford, former CEO and President of Swisslog, a leading logistics automation company. “If we can deploy robots without training, they will be viable in a wide range of activities, from factories and warehouses to domestic and commercial applications.” The VERSES robot recognizes that objects exist, that one object can be inside another (food in a fridge), and that bumping into things (a couch, a ball on the floor) is bad. It updates its 3D model of the world, and figures out what actions will get it closer to its goal.    SHARE Brains don’t just build models—they test them in the real world through action. That connection between perception and movement is essential as more AI and robotics systems operate in physical space and teamwork will require skill-sharing. To facilitate this, VERSES helped lead the creation of the Spatial Whitepaper: Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube 10 min read Whitepaper: Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Steven Swanson : Jun 2, 2025 7:18:54 AM Active Inference Benchmarks Research Agents From Arcade Games to Real World Domains, How Generalized Adaptive Intelligence Learns to Win   Editorial Note This whitepaper provides the background and results from our latest scientific paper “ AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models ” published on ArXiv on May 30, 2025. It is intended to provide context to a non-technical audience and serve as a broad translation of the paper’s importance and its implications for AI.   Highlights A New Class of AI Agent with Active Inference AXIOM (Active eXpanding Inference with Object-centric Models) based on Active Inference, is an important innovation in AI, a modular, “digital brain”, that learns arcade-style gameplay the way humans do, by modeling objects and cause-and-effect dynamics, instead of memorizing raw pixels.   A New Benchmark For Generalized Adaptive Intelligence In our new research paper, we examine how AXIOM outperforms Google DeepMind’s top AI models in the Gameworld 10k benchmark (10 physics-consistent games, 10,000-step cap); results were validated by an independent third-party lab. KEY METRIC DREAMER V3 AXIOM ADVANTAGE Normalized score 0.48 0.77 +60 % better gameplay Steps to competence 24,207 3,175 7 times faster learning GPU time to competence 6.23 hr 0.16 hr 39 times (~97%) greater compute-efficient Model size (Params)  420 M 0.95 M ~440 times (-99%) smaller   Real World Implications AXIOM’s adaptive architecture could provide increased reliability and accuracy, making it potentially superior and more affordable. Its tiny footprint and energy savings could make high-performance intelligence practical on virtually any device, including low-power edge appliances. Its object-centric reasoning delivers reliability, adaptability, and full interpretability that black-box neural nets lack, aligning it with regulatory requirements. It’s AI for 100 billion devices rather than from $100 billion data centers.   Next Steps with AXIOM The full paper with Gameworld 10K benchmarks and AXIOM code have been released on ArXiv and GitHub.  AXIOM is expected to be shipped in upcoming releases of Genius™; early-access sign-up is now open.   Introduction When a ball rolls behind a couch, we still know it exists and expect it to keep moving until something stops it. We grasp this not because we have seen every trajectory but because our brains encode core principles—gravity, inertia, cause and effect—that give the world structure. From these principles we form common sense , testing and refining it through experience and carrying it from one context to the next. In effect, the brain builds a world model —an internal simulation of objects, including their size, color, weight, texture, and purpose, as well as how they interact, allowing us to predict outcomes and act accordingly. We begin with an early set of working assumptions— mental axioms —and continually update them as new evidence emerges. That self-correcting world model is the essence of natural intelligence. But this is not how current state-of-the-art artificial intelligence (generally based on deep reinforcement learning) works.  AI can certainly learn statistical patterns about how pixels and words are frequently grouped, but this isn’t the same as understanding the general cause-and-effect dynamics of the world and how to transfer them to other contexts.  This inability to generalize across domains is why the full potential of AI, Artificial General Intelligence (AGI), remains elusive. The human brain is our best example of general intelligence so how might we apply the principles of cognition in software? A 2018 Wired cover story, “ The Genius Neuroscientist Who Might Hold the Key to True AI ,” cast Professor Karl Friston’s free-energy principle as “the most all-encompassing idea since the theory of natural selection.”  Inspired by that vision, we began working with Professor Friston, VERSES Chief Scientist who assisted us in assembling a unique, multidisciplinary team of neuroscientists, computer engineers, and machine learning researchers to reimagine intelligence from first principles.  Over the past several years—and through more than 130 research publications in collaboration with dozens of universities worldwide—we’ve been developing and testing a new class of AI, internally codenamed AXIOM.  A culmination of those efforts, AXIOM (Active eXpanding Inference with Object-centric Models) is a modular, biomimetic digital brain designed to interact with the world more like a human than a machine.  AXIOM is not a modification of mainstream machine learning—it’s a new biologically grounded re-architecting of artificial intelligence itself. “AXIOM has been developed as a ‘digital brain’, designed to mirror the modular structure and dynamic processes of our own brains; It develops an understanding of its world and how it operates within that world, enabling it to seek out experiences that massively enhance learning.  - VERSES Chief Scientist, Professor Karl Friston For decades, the perceptron —a simplified model of a neuron—has been treated as the fundamental unit of intelligence in artificial neural networks.  This assumption underpins nearly all modern AI, from deep reinforcement learning agents to large language models (LLMs).  The field's core advancement has been scaling: stacking more perceptrons into larger architectures, increasing training data, and adding compute.  But despite the scale, the core limitations persist—brittle generalization, data inefficiency, and black-box behavior.  This reductionist, brute-force approach to AI with neurons as the fundamental unit is an important branch in the evolutionary tree of computer science, but in our view, it's not the trunk. From Neuroscience to a Digital Brain Based on Professor Friston’s neuroscience research—and demonstrated here—we believe that the brain offers a superior model for intelligence compared to current state-of-the-art machine learning methods.  Unlike artificial neural networks, the brain is modular, dynamic, and adaptive.  It doesn't just passively absorb patterns—it builds a model of the world, reasons about causes, and learns by interacting.  Using neuroplasticity, it continuously learns and updates its neuronal connections dynamically. AXIOM is built on this principle: a digital brain that mirrors the brain's architecture, processes, and dynamic interactions, not just its smallest component. AXIOM’s modular architecture is built on a new class of mixture models—probabilistic models that assume data comes from several underlying groups, even without knowing in advance what those groups are.  Rather than relying on labeled data, these models uncover “hidden categories” on their own, making them ideal for identifying structure in messy or unlabeled data, such as we encounter from complex, real-world inputs.  In AXIOM, different mixture models handle distinct cognitive functions—perception, identity, memory, motion, and planning—mirroring how specialized regions of the brain work together to produce what we experience as cognition.  These modules work in concert to actively rea Genius: AI for finance Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube VERSES Research Summary   VERSES technology is on the cutting edge. This can make it hard to know where to start. This page provides an overview of our work. VERSES’ research program is centered on a single premise: intelligence emerges from agents that continuously infer, learn, and act within uncertain environments. The lab’s work builds a coherent stack—from theoretical foundations to real-world applications—that demonstrates how this approach can scale from individual agents to networks of intelligent systems. Intelligence is a process of active inference The foundation of the research is the position paper Designing Ecosystems of Intelligence from First Principles , which formalizes intelligence as a process of Active Inference . In this framework, agents continuously update probabilistic models of the world and coordinate with other agents to minimize uncertainty. This work establishes the conceptual basis for intelligence as a distributed system of interacting agents rather than an isolated model. Active inference can tackle real-world problems by using approaches that scale efficiently Building on this foundation, the paper From Pixels to Planning: Scale-Free Active Inference introduces Renormalized Generative Models (RGM). These models allow agents to learn hierarchical world representations that connect perception directly to planning. The result is a unified generative framework in which sensing, reasoning, and action emerge from a single probabilistic model of the environment. VERSES models can efficiently learn and update in real time The next step addresses how agents learn structured environments efficiently. AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models demonstrates that agents can construct object-centric world models that dynamically expand as environments become more complex. This approach allows agents to learn new environments rapidly and with far greater data efficiency than conventional reinforcement learning systems. Gameworld 10K is a challenge created to demonstrate these capabilities and has a direct, strong relevance to solving the ARC-AGI-3 challenge . The results of Gameworld 10K were independently validated by Soothsayer Analytics TM   and are documented in this report. VERSES models are well suited to real-world challenges such as robotics For real-world deployment, perception of complex physical environments is critical. Variational Bayes Gaussian Splatting introduces a probabilistic method for real-time 3D scene reconstruction with uncertainty modeling, enabling agents to maintain continuously updated spatial representations of their surroundings. These capabilities are then applied to embodied systems. The robotics work Mobile Manipulation with Active Inference for Long-Horizon Rearrangement Tasks demonstrates how perception, generative world models, and planning can be integrated into a single framework that enables robots to execute complex, long-horizon tasks in dynamic environments. VERSES technology provides a suite of solutions that solve complex problems that span foundational research to real products Taken together, these papers describe a progression from theory → world models → perception → planning → embodied intelligence , ultimately supporting VERSES’ broader vision of ecosystems of intelligent agents that coordinate through shared probabilistic models of the world . This architecture is designed to enable scalable intelligence across domains such as financial services, robotics, spatial computing, infrastructure systems, and autonomous decision environments. In essence, the research demonstrates how Active Inference can serve as a unifying framework for building adaptive, world-model-driven AI systems capable of operating in complex real-world environments. These capabilities are now embodied in VERSES’ product, Genius, launched commercially in April 2025. Read more about our science We've published 100+ papers in the last few years based on the Active Inference research performed by the VERSES research team, which is of major significance. The full list can be found on Google Scholar. Here is an independent blog written by AI expert Devansh about VERSES. How a Tiny AI Startup is beating OpenAI by Redefining Intelligence [Breakdowns] Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Genius: AI for finance Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube   AI for Finance Genius solves financial services problems   Fund managers can't rely on today's AI Today’s LLMs can’t be explained and aren’t reliable.   Fund managers need an AI Genius Genius™ helps fund managers to identify and quantify risks and quickly act on new data.   Inspired by the human brain Active inference is superior to LLMs and learns on its own, with models that can always be explained. At Analog, we believe intelligence is about adapting, evolving, and seamlessly integrating into the fabric of everyday life. Genius didn’t just prove it could handle complexity—it showed us it could anticipate and creatively respond to real-world challenges. By embedding Genius Enterprise as a component of our solutions, we’re crafting the nervous system of tomorrow’s cities: intelligent, intuitive, and human-centric. Alex Kipman Founder and CEO Analog Our Fortune 500 clients want AI that speaks their language, respects their rules, and delivers results they can trust. Off-the-shelf platforms rarely meet those standards. We believe that Genius lets us generate reliable, domain-specific predictions in weeks instead of months, and our team provides the know-how to take those models live. Becoming the first Certified Genius Reseller allows us to bring VERSES’ next-generation capability to enterprises worldwide. Gaurav Agrawal Founder and CEO Soothsayer We're excited to join VERSES in building trustworthy, explainable intelligence designed to enhance rather than replace human capabilities. Together, we will help organizations transition from unreliable, generic AI to domain-specific intelligence that adapts and grows alongside the businesses it serves. We feel that we are breaking significant new ground with our Enterprise program with VERSES while enabling our clients to be at the forefront of using cognitive AI as a catalyst for reinvention. Ken Rastin Founder and CEO Kalosys Being able to explicitly model the cause-effect relationships of complex systems and quantify uncertainty means we can generate something not possible with traditional ML tools – results that are reliable, explainable, and assurance ready. Andy Tasker Founder and Chief Strategist Prodigii Our technology: Inspired by the brain Humans learn by prioritizing what is both important and uncertain as our brains model the world and continuously improve . For instance, when drivers see a ball roll into the road, they sense possible danger (inference), and change their behavior (active) by slowing down and scanning the area. Like the brain, VERSES Genius™ has been designed to tackle problems with this process of active inference . Genius interacts with the world and continually updates, allowing it to adapt to a volatile, uncertain, complex, and ambiguous world. Like humans, it can solve problems by sensing, planning, acting, and sharing. Our breakthroughs Beating OpenAI and DeepSeek In winter 2024/2025 we showed that Genius could learn quickly - and that it could beat competitors playing Mastermind and Atari computer games. This proved that Genius could achieve better results at lower costs that leading AI m odels such as Iris (a transformer model), OpenAI’s o1-preview and DeepSeek R1. The world’s first digital brain beats Google DeepMind In June 2025 we unveiled a modular digital brain , which played classic computer games 60% better and 97% more efficiently than Google’s leading AI models. This performance is driven by models that quickly adapt to new information without retraining.   2025/6 Finance Over 2025/6 we worked with a large global asset manager (>$100bn AUM) to show how Genius can help them achieve higher returns while managing risk in compliance with their governance policies. Up next... ARC-AGI-3 VERSES is developing further modules for Genius which will allow us to solve complex problems, such as those tested by the ARC-AGI-3 challenge . ARC-AGI 3 is a test of human-like intelligence. It does this by setting tasks that are deliberately designed to show the gap between cutting edge AI systems and human intelligence. The underlying philosophy behind it is that intelligence is simply “how quickly can you learn new skills?” Contact us Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top Meet the Team Genius: AI for finance Featured Content Home Genius Blog Research News News In the media Company Investors About Search Toggle Menu Search Toggle Menu Home Genius Blog Research News News In the media Company Investors About Featured Whitepaper | Mastering Gameworld 10k in Minutes with the AXIOM ‘Digital Brain’ Mastering Atari Games with Natural Intelligence Genius Outperforms DeepSeek R1 in Code-Breaking Challenge, Mastermind Linkedin X YouTube About VERSES is a cognitive computing company building next-generation intelligent software systems modeled after the Wisdom and Genius of Nature. Vision A smarter world where people and technology work in greater harmony to generate shared prosperity. Mission Elevate human potential through intelligent tools that radically improve our mutual understanding. Distributed Intelligence. Leadership David T. Scott Interim CEO Linkedin Hari Thiruvengada CTO Linkedin Twitter Karl Friston Chief Scientist James Hendrickson COO Linkedin Twitter James Christodoulou CFO Linkedin Don Moody Chief Legal Officer Linkedin Lynda Ryan Senior Manager, People Operations Linkedin John Carenbauer VP ENTERPRISE OPERATIONS Linkedin Board of Directors Gabriel René Dan Mapes Jon De Vos Scott Paterson Michael Blum Join our Team! We are looking for exceptional people. See Open Positions Company About Careers Investors Contact Resources Research blog News Blog In the media Privacy Notice © 2026 VERSES All rights reserved. X Linkedin YouTube VERSES®, Genius™, Agentic Enterprise Intelligence™, Imagine a Smarter World™, Smarter by Nature™, Brainpower for Agents™ Use of third party names, trademarks, brands and/or logos should not be deemed to imply any sponsorship, affiliation or endorsement. Return to top