ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture

• URL: http://arxiv.org/abs/2603.21340v1
• Date: Sun, 22 Mar 2026 17:46:04 GMT
• Title: ARYA: A Physics-Constrained Composable & Deterministic World Model Architecture
• Abstract summary: ARYA is a composable, physics-constrained, deterministic world model architecture built on five foundational principles.<n>ARYA satisfies all canonical world model requirements, including state representation, dynamic prediction, causal and physical awareness, temporal consistency, generalization, learnability, and planning and control.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Trustworthy Agentic AI: A Comprehensive Cybersecurity and Systems Survey on Threat Landscapes, Defense Architectures, and Open Challenges [0.0]
  This survey delivers a comprehensive systems-security reference framework for trustworthy agentic AI.<n>We formalize the general agent architecture as a stateful 5-tuple and establish a 6-dimensional trustworthiness taxonomy covering security, safety, privacy, explainability, fairness, and accountability.
  arXiv  Detail & Related papers  (2026-09-12T05:51:27Z)
• Beyond Feedforward Networks: Reentry Neural Systems as the Fundamental Basis of Subjecthood and Intrinsic Safety of Next-Generation AGI [0.0]
  We propose a complete blueprint for safe artificial general intelligence based on a closed reentry loop (D -> I cycle)<n>In contrast to feedforward networks, which are directed acyclic graphs, the proposed architecture contains a structural cycle with self-sustaining amplification.<n>We present the S-measure -- a Lean-time [O(N3)] computable alternative to Tononi's NP-hard Phi -- with machine-verified 4 proof that S>0 implies positive integrated information.
  arXiv  Detail & Related papers  (2026-06-24T21:54:02Z)
• Position: Weight Space Should Be a First-Class Generative AI Modality [67.10702320971893]
  This paper argues that model checkpoints should be treated as a first-class data modality.<n>It argues that generative modeling in weight space should be standardized as a core machine learning primitive.<n>Our goal is to shift the community's default mindset from optimizing models per task to sampling models from learned weight distributions.
  arXiv  Detail & Related papers  (2026-05-18T16:38:26Z)
• Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond [209.35045331678043]
  We introduce a "levels x laws" taxonomy organized along two axes.<n>The first defines three capability levels: L1 Predictor, which learns one-step local transition operators; L2 Simulator, which composes them into multi-step, action-conditioned rollouts that respect domain laws; and L3 Evolver, which autonomously revises its own model when predictions fail against new evidence.<n>We synthesize over 400 works and summarize more than 100 representative systems spanning model-based reinforcement learning, video generation, web and GUI agents, multi-agent social simulation, and AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2026-04-24T17:48:47Z)
• The Trinity of Consistency as a Defining Principle for General World Models [106.16462830681452]
  General World Models are capable of learning, simulating, and reasoning about objective physical laws.<n>We propose a principled theoretical framework that defines the essential properties requisite for a General World Model.<n>Our work establishes a principled pathway toward general world models, clarifying both the limitations of current systems and the architectural requirements for future progress.
  arXiv  Detail & Related papers  (2026-02-26T16:15:55Z)
• RISE: Self-Improving Robot Policy with Compositional World Model [52.227523057681786]
  We present RISE, a scalable framework of robotic reinforcement learning via imagination.<n>At its core is a Compositional World Model that predicts multi-view future via a controllable dynamics model, and (ii) evaluates imagined outcomes with a progress value model.<n>These components are integrated into a closed-loop self-improving pipeline that continuously generates imaginary rollouts, estimates advantages, and updates the policy in imaginary space without costly physical interaction.
  arXiv  Detail & Related papers  (2026-02-11T17:43:36Z)
• Engineering AI Agents for Clinical Workflows: A Case Study in Architecture,MLOps, and Governance [0.21748200848556345]
  We show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement.<n>We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.
  arXiv  Detail & Related papers  (2026-01-31T14:33:57Z)
• From Accuracy to Impact: The Impact-Driven AI Framework (IDAIF) for Aligning Engineering Architecture with Theory of Change [0.40611352512781873]
  Impact-Driven AI Framework (IDAIF) is a novel architectural methodology that integrates Theory of Change (ToC) principles with modern artificial intelligence system design.<n>IDAIF addresses the gap by establishing a systematic mapping between ToC's five-stage model and corresponding AI architectural layers.<n>Three case studies demonstrate IDAIF application across healthcare, cybersecurity, and software engineering domains.
  arXiv  Detail & Related papers  (2025-12-09T10:21:02Z)
• CRACI: A Cloud-Native Reference Architecture for the Industrial Compute Continuum [0.0]
  This paper introduces CRACI, a Cloud-native Reference Architecture for the Industrial Compute Continuum.<n>It promotes a decoupled and event-driven model to enable flexible, non-hierarchical data flows across the continuum.
  arXiv  Detail & Related papers  (2025-09-09T08:24:08Z)
• ANNIE: Be Careful of Your Robots [48.89876809734855]
  We present the first systematic study of adversarial safety attacks on embodied AI systems.<n>We show attack success rates exceeding 50% across all safety categories.<n>Results expose a previously underexplored but highly consequential attack surface in embodied AI systems.
  arXiv  Detail & Related papers  (2025-09-03T15:00:28Z)
• World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks [55.90051810762702]
  We present a comprehensive overview of world models, highlighting their architecture, training paradigms, and applications across prediction, generation, planning, and causal reasoning.<n>We propose Wireless Dreamer, a novel world model-based reinforcement learning framework tailored for wireless edge intelligence optimization.
  arXiv  Detail & Related papers  (2025-05-31T06:43:00Z)
• AI in a vat: Fundamental limits of efficient world modelling for agent sandboxing and interpretability [84.52205243353761]
  Recent work proposes using world models to generate controlled virtual environments in which AI agents can be tested before deployment.<n>We investigate ways of simplifying world models that remain agnostic to the AI agent under evaluation.
  arXiv  Detail & Related papers  (2025-04-06T20:35:44Z)
• Conceptual Framework for Autonomous Cognitive Entities [0.9285295512807729]
  This paper introduces the Autonomous Cognitive Entity model, a novel framework for a cognitive architecture. The model is designed to harness the capabilities of the latest generative AI technologies, including large language models (LLMs) and multimodal generative models (MMMs) The ACE framework also incorporates mechanisms for handling failures and adapting actions, thereby enhancing the robustness and flexibility of autonomous agents.
  arXiv  Detail & Related papers  (2023-10-03T15:53:55Z)
• Neural Dynamic Policies for End-to-End Sensorimotor Learning [51.24542903398335]
  The current dominant paradigm in sensorimotor control, whether imitation or reinforcement learning, is to train policies directly in raw action spaces. We propose Neural Dynamic Policies (NDPs) that make predictions in trajectory distribution space. NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks.
  arXiv  Detail & Related papers  (2020-12-04T18:59:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.