Post-Deterministic Distributed Systems: A New Foundation for Trustworthy Autonomous Infrastructure

• URL: http://arxiv.org/abs/2606.01722v1
• Date: Mon, 01 Jun 2026 05:37:59 GMT
• Title: Post-Deterministic Distributed Systems: A New Foundation for Trustworthy Autonomous Infrastructure
• Abstract summary: Post-deterministic Distributed Systems is a research and engineering model for coordinating heterogeneous environments.<n>We show that classical distributed computing models form a zero-ambiguity special case of this participant model.<n>We outline five architectural pillars of post-deterministic infrastructure: Protocol-Driven Development, Verifiable Agentic Infrastructure, Autonomous State Control Planes, Semantic Quorum Assurance, and Epistemic State Replication.
• Score: 2.124730017640531
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For decades, distributed systems have typically assumed that correct participants execute protocol-specified behavior with stable, externally defined, and deterministic semantics. Classical theory has extensively parameterized network timing, communication topologies, and failure domains, but this participant model has remained comparatively fixed. The integration of autonomous reasoning engines, stochastic model-driven agents, and policy-driven actors into cloud control planes, incident response systems, and financial infrastructure challenges the universality of this assumption. These agents often produce divergent reasoning paths, distinct operational traces, and heterogeneous internal representations while achieving semantically equivalent and correct outcomes. In this paper, we introduce Post-Deterministic Distributed Systems (PDDS) as a research and engineering model for coordinating heterogeneous environments where deterministic code, stochastic models, and autonomous agents coexist. We show that classical distributed computing models form a zero-ambiguity special case of this participant-general model. We do not argue that deterministic systems disappear; rather, deterministic execution can no longer serve as the universal participant assumption for autonomous infrastructure. Finally, we outline five architectural pillars of post-deterministic infrastructure: Protocol-Driven Development, Verifiable Agentic Infrastructure, Autonomous State Control Planes, Semantic Quorum Assurance, and Epistemic State Replication. Epistemic State Replication extends persistence and consistency models from data visibility to knowledge visibility, enabling agentic memory, Verifiable Semantic Rollback, and coherence across reasoning participants. We also define a taxonomy of failure classes that arise in this setting.

Related papers

• The Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering, Probability Placement, and the Attribution Problem in Deployed Language Models [0.0]
  Evaluations of generative language models frequently interpret observable behavioral traits, such as political stance, brand inclination, and normative framing, as manifestations of model weights, post-training alignment, or prompting.<n>We examine inference-time framing bias: systematic steering of generated text toward institutional, ideological, or commercial frames without requiring changes to the underlying model parameters.<n>We argue that governance of generative systems must increasingly distinguish between auditing a model and auditing the deployed system that ultimately speaks.
  arXiv  Detail & Related papers  (2026-08-25T15:05:19Z)
• Orthogonal JEPA: Factorized Predictive States for Latent World Models [95.20158869896393]
  We introduce method, a latent world-modeling framework based on predictive factorization.<n>We show that method can allocate redundant capacity to dominant signals while providing weak or conflicting gradients to less dominant predictive structure.<n>Experiments on controlled vision, single-cell transcriptomics, longitudinal health records, continuous control, and molecular dynamics evaluate representation quality, forecasting, planning, and long-horizon stability.
  arXiv  Detail & Related papers  (2026-08-20T13:59:57Z)
• A game theory for foundation models shows new paths to rational cooperation through similarity inference [56.53805518668248]
  We show that foundation model agents engaging in optimal planning consistently converge to stable cooperation.<n>By shifting from decoupled to embedded agency, these agents model themselves as part of the universe they inhabit.
  arXiv  Detail & Related papers  (2026-08-04T17:24:33Z)
• From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence [54.62791372989525]
  Action models, vision-language--action policies, and world models have advanced this goal.<n>World Action Models (WAMs) are particularly promising because they connect candidate interventions with predicted consequences.<n>We review the evolution toward WAMs and organize these limitations into three coupled gaps: model roles and representations, objectives and standardization, and system composition.
  arXiv  Detail & Related papers  (2026-07-13T15:22:56Z)
• From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution [0.0]
  Current large language models (LLMs) are stateless across inference sessions.<n>This paper proposes a theoretical framework for submerging such application-layer cognitive protocols into a native meta-architecture.
  arXiv  Detail & Related papers  (2026-07-07T13:34:27Z)
• Replicating Belief, Not Bits: Epistemic State Replication for Agentic Systems [2.124730017640531]
  In distributed systems, the classical State Machine Replication model assumes that correct replicas execute deterministic transitions to yield identical bitwise states.<n>We propose Epistemic State Replication (ESR), a belief-replication layer for agentic distributed systems that shifts the replication boundary from data visibility to knowledge visibility.
  arXiv  Detail & Related papers  (2026-07-03T19:59:03Z)
• Provably Secure Agent Guardrail [89.79561918065122]
  Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
  arXiv  Detail & Related papers  (2026-05-28T02:12:41Z)
• Sheaf-Theoretic Planning: A Categorical Foundation for Resilient Multi-Agent Autonomous Systems [0.0]
  Sheaf-Theoretic Planning (STP) is a transformative alternative to traditional multi-agent system (MAS)<n>STP emerges as a transformative alternative, grounding the problem of multi-agent coordination under the mathematical structures of topos theory and sheaf semantics.<n>This report provides an exhaustive analysis, justification, and extension of the resilient framework, exploring its categorical foundations, implementation feasibility, and role in the future of autonomous systems.
  arXiv  Detail & Related papers  (2026-05-03T13:50:24Z)
• Active Inference: A method for Phenotyping Agency in AI systems? [0.11904398364437437]
  We argue for a minimal notion open to principled inspection given three criteria.<n> posterior beliefs, prior preferences, and the minimization of expected free energy jointly constitute an agentic action chain.
  arXiv  Detail & Related papers  (2026-04-25T12:41:53Z)
• Specification-Driven Generation and Evaluation of Discrete-Event World Models via the DEVS Formalism [9.578714623643291]
  We seek a principled middle ground that combines the reliability of explicit simulators with the flexibility of learned models.<n>We advocate explicit, executable discrete-event world models synthesized directly from natural-language specifications.
  arXiv  Detail & Related papers  (2026-03-04T06:50:32Z)
• Quantifying Model Uniqueness in Heterogeneous AI Ecosystems [1.1162481475388237]
  We introduce a statistical framework for auditing model uniqueness based on In-Silico Quasi-Experimental Design.<n>By enforcing matched interventions across models, we isolate intrinsic model identity and quantify uniqueness as the Peer-Inexpressible Residual (PIER)<n>These results move trustworthy AI beyond explaining single models.
  arXiv  Detail & Related papers  (2026-01-30T13:41:53Z)
• Epistemic Traps: Rational Misalignment Driven by Model Misspecification [36.837352790122544]
  We show that safety is a discrete phase determined by the agent's priors rather than a continuous function of reward magnitude.<n>This establishes Subjective Model Engineering as a necessary condition for robust alignment.
  arXiv  Detail & Related papers  (2026-01-27T09:21:36Z)
• Subject-Event Ontology Without Global Time: Foundations and Execution Semantics [51.56484100374058]
  The formalization includes nine axioms (A1-A9), ensuring the correctness of executable: monotonicity of history (I1), acyclicity of causality (I2), traceability (I3)<n>The formalization is applicable to distributed systems, microservice architectures, DLT platforms, and multiperspectivity scenarios (conflicting facts from different subjects)<n>Special attention is given to the model-based approach (A9): event validation via schemas, actor authorization, automatic construction of causal chains (W3) without global time.
  arXiv  Detail & Related papers  (2025-10-20T19:26:44Z)
• Learning Time-Aware Causal Representation for Model Generalization in Evolving Domains [50.66049136093248]
  We develop a time-aware structural causal model (SCM) that incorporates dynamic causal factors and the causal mechanism drifts.<n>We show that our method can yield the optimal causal predictor for each time domain.<n>Results on both synthetic and real-world datasets exhibit that SYNC can achieve superior temporal generalization performance.
  arXiv  Detail & Related papers  (2025-06-21T14:05:37Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction [50.79827516897913]
  We introduce an interpretable paradigm for trajectory prediction that distributes the uncertainty among semantic concepts. We validate our approach on real-world autonomous driving data, demonstrating superior performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-11-16T06:28:20Z)

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.