Grounded Inference: Principles for Deterministically Encapsulated Generative Models

2026-06-19 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

"Grounded Inference" proposes a foundational framework for safely integrating generative models, such as LLMs, into traditional computational systems, addressing their inherent non-determinism. The framework defines four primitives: the Probabilistic Engine (the generative model itself), the Model Encapsulator (validating model output against schemas), the State Registry (deterministic data storage), and the Deterministic Orchestrator (managing state flow and business logic). The paper identifies two critical anti-patterns: treating generative models as data workers or as stateful machines, which lead to issues like loss of auditability, silent degradation, and context collapse. It introduces the "Adaptive Resolution Agent" as a reference architecture, demonstrating how to encapsulate probabilistic engines within deterministic logic for tasks like resolving schema drift, ensuring models remain stateless and their outputs are externally validated. The author used Google Gemini 3 models during manuscript development.

Key takeaway

For AI Architects and MLOps Engineers integrating generative models into production, you must prioritize deterministic encapsulation and external validation. Treat probabilistic engines as stateless functions, never as data workers or stateful machines. Implement Model Encapsulators to enforce schema contracts and manage retry logic, isolating the generative model from core data planes. This approach mitigates risks like silent data corruption and ensures auditability, transforming volatile outputs into predictable enterprise structures.

Key insights

Encapsulate probabilistic generative models within deterministic systems using strict boundaries and external validation.

Principles

• Generative models are stateless, probabilistic engines.
• Isolate probabilistic variance to semantic reasoning.
• Outputs require external, deterministic validation.

Method

The Encapsulation Micro-Pattern uses a Model Encapsulator to validate probabilistic engine outputs against schemas, manage retries, and isolate it from state and orchestration.

In practice

• Apply Entropy Minimization in data ingestion.
• Use Model Encapsulators for schema validation.
• Reset model session context for statelessness.

Topics

• Grounded Inference
• Generative Model Encapsulation
• Deterministic AI Systems
• LLM Anti-Patterns
• Adaptive Resolution Agent
• AI System Reproducibility

Best for: CTO, Director of AI/ML, VP of Engineering/Data, AI Architect, AI Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.