Seth Dobrin

#AI #NOW

ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture Seth Dobrin, Lukasz Chmiel This paper presents ARYA, a composable, physics-constrained, deterministic world model architecture built on five foundational principles: nano models, composability, causal reasoning, determinism, and architectural AI safety. We demonstrate that ARYA satisfies all canonical world model requirements, including state representation, dynamic prediction, causal and physical awareness, temporal consistency, generalization, learnability, and planning and control. Unlike monolithic foundation models, the ARYA foundation model implements these capabilities through a hierarchical system-of-system-of-systems of specialized nano models, orchestrated by AARA (ARYA Autonomous Research Agent), an always-on cognitive daemon that executes a continuous sense-decide-act-learn loop. The nano model architecture provides linear scaling, sparse activation, selective untraining, and sub-20-second training cycles, resolving the traditional tension between capability and computational efficiency. A central contribution is the Unfireable Safety Kernel: an architecturally immutable safety boundary that cannot be disabled or circumvented by any system component, including its own self-improvement engine. This is not a social or ethical alignment statement; it is a technical framework ensuring human control persists as autonomy increases. Safety is an architectural constraint governing every operation, not a policy layer applied after the fact. We present formal alignment between ARYA's architecture and canonical world model requirements, and report summarizing its state-of-the-art performance across 6 of 9 competitive benchmarks head-to-head with GPT-5.2, Opus 4.6, and V-JEPA-2. All with zero neural network parameters, across seven active industry domain nodes spanning aerospace, pharma manufacturing, oil and gas, smart cities, biotech, defense, and medical devices. arxiv.org/abs/2603.21340

SMART launches new Wearable Imaging for Transforming Elderly Care research group #AI #ArtificialIntelligence @MIT sbee.link/ncgv43j87d

SMART launches new Wearable Imaging for Transforming Elderly Care research group #AI #ArtificialIntelligence @MIT sbee.link/jh9yakn7ub

Dr. Dobrin was quoted today by CNBC. Thank you to Senior Media & Tech Correspondent, Julia Boorstin. Here is this link. sbee.link/397kedy8q6 #AI #CNBC #ARYA

Counter intelligence #AI #ArtificialIntelligence @MIT sbee.link/htu76fv3nb

Improving AI models’ ability to explain their predictions #AI #ArtificialIntelligence @MIT sbee.link/w8uheq4xap

ARYA Labs' world model is powered by Constrained Deterministic AI™ ✅ Deterministic + mathematically constrained ✅ Physics-informed (not just pattern-matched) ✅ Formally verifiable & certifiable ✅ Efficiency over brute-force compute ✅ Certainty over probability ✅ 100% compliant with hard constraints ✓ Scalable within constraint family Deterministic AI engine, autonomous systems, physics simulation, safety-critical inference, constrained optimization. This adherence to physical laws ensures a reliable and efficient AI system. sbee.link/v84kjaqbhf

𝑩𝒖𝒃𝒃𝒍𝒆 𝒐𝒓 $𝟯𝟴𝟬𝑩 𝑨𝒓𝒃𝒊𝒕𝒓𝒂𝒈𝒆: 𝑻𝒖𝒓𝒏 “𝑶𝒃𝒔𝒐𝒍𝒆𝒕𝒆” 𝑮𝑷𝑼𝒔 𝑰𝒏𝒕𝒐 𝑷𝒓𝒐𝒇𝒊𝒕 𝑴𝒂𝒄𝒉𝒊𝒏𝒆𝒔. The economic longevity of these chips is rooted in the fundamental split in AI workloads: training versus inference. Training is the computationally brutal process of teaching a model like GPT-4, which happens once and requires massive parallel processing power. Inference is the process of using the trained model to generate text, images, or predictions, which happens billions of times a day but is far less demanding per query. Research from Epoch AI suggests that, for optimal efficiency, AI labs should spend roughly equal resources on training and inference, approximately a 50/50 split 5. The logic is elegant: if you spend 10 times more on inference than training, you can reduce training compute by 10 times and increase inference compute by 10 times without degrading model performance, achieving massive cost savings. This means that as NVIDIA releases its new Blackwell and Rubin-series GPUs for frontier training, the massive installed base of Hopper (H100) and Ampere (A100) GPUs becomes a highly valuable, fully paid-for asset for running the ever-growing volume of inference workloads. @RicursiveAI @CoreWeave @GroqInc @TensorFlow @aws @google @OpenAI @Oracle @Microsoft @EpochAIResearch @GoldmanSachs #hyperscale #AI #NVIDIA #ASIC #Trainium3 #GPU #H100

ARYA Labs' world model is driven by Constrained Deterministic AI™, ensuring 100% explainability and full auditability. sbee.link/tk7xqwcmgd Every decision is rooted in explicit rules, allowing for transparent reasoning paths that can be traced and verified. With no neural network inference overhead, the execution speed is very fast. This adherence to the laws of physics ensures a reliable and efficient #AI system. #ARYALabs #TransparentAI

BOOK DR. SETH DOBRIN FOR YOUR NEXT EVENT. ✨ Topics: • World Models • AI Strategy & Transformation • Responsible & Ethical AI • Generative AI Applications • Building AI-Ready Organizations Contact: info@qantm.ai or go towww.drsethdobrin.com #KeynoteSpeaker #AI #ArtificialIntelligence

Why Titans Are Leaving LLMs for World Models. Bezos, LeCun, Li, and Dobrin Redefine the Next Frontier of Intelligence. The market should pay attention. open.substack.com/pub/siliconsan…

Katie Spivakovsky wins 2026 Churchill Scholarship #AI #ArtificialIntelligence @MIT sbee.link/bw3kn9qgd7

ARYA Labs' World Model, powered by Constrained Deterministic AI™, operates with 100% transparency, ensuring that every decision is traceable to explicit rules. aryalabs.io The system is fully auditable, allowing for the tracing and verification of all reasoning paths. With no neural network inference overhead, the execution speed is very fast, enabling efficient and reliable performance. #ARYALabs #Transparency #Auditability #AI

3 Questions: Using AI to accelerate the discovery and design of therapeutic drugs #AI #ArtificialIntelligence @MIT sbee.link/gnvwhbajru

Antonio Torralba, three MIT alumni named 2025 ACM fellows #AI #ArtificialIntelligence @MIT sbee.link/wfqjb3gxpa

FRESH SILICON SANDS NEWS: Space Tech, Earth Profits: $1.8 Trillion Space Boom. Space Data Centers--Really? open.substack.com/pub/siliconsan…

Brian Hedden named co-associate dean of Social and Ethical Responsibilities of Computing #AI #ArtificialIntelligence @MIT sbee.link/cfdu7tqa6w

Helping AI agents search to get the best results out of large language models #AI #ArtificialIntelligence @MIT sbee.link/43gbq78f6v

Honoured to be attending the 4th Edition — PIF Private Sector Forum, scheduled for 09–10 February 2026 at (KAICC) and the Gartner event the same week. Please, reach out if you would like to schedule a meeting. Dr. Dobrin was quoted by CNBC. Thank you to Senior Media & Tech Correspondent, Julia Boorstin. Here is this link. sbee.link/3u4pagv76m #AI #PIF #GARTNER #KSA #CNBC #ARYA

BRAND NEW: Why Titans Are Leaving LLMs for World Models. Bezos, LeCun, Li, and Dobrin Redefine the Next Frontier of Intelligence. The market should pay attention. open.substack.com/pub/siliconsan…