Stateful Reasoning Runtimes: A Reference Architecture for Dispositional Continuity in LLM Systems
Description
Version 2 updates this work into a technical note paired with a deterministic reference implementation. The paper distinguishes conversational state, associative state, and dispositional state in LLM applications, arguing that replay and retrieval reconstruct context but do not provide persistent identity governance.
The accompanying artifact evaluates three runtime patterns: session replay, vector memory, and dispositional runtime. Replay preserves recent chronological context, vector memory retrieves related historical records, and the dispositional runtime maintains persistent governance state through bounded updates, drift detection, constraint persistence, and reset resistance.
The benchmark separates capability probes from governance probes and supports a narrow architectural claim: identity continuity requires explicit dispositional governance state outside the stateless LLM call. The work does not claim clinical validity, therapeutic efficacy, general intelligence, complete identity preservation, or production security certification.
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This paper introduces the architectural concept of stateful reasoning runtimes for LLM applications. Modern LLM APIs operate as fully stateless inference engines. Each call is independent and retains no memory of prior interactions. Current industry solutions externalize context using session replay, vector memory, and retrieval systems. These approaches reconstruct history, but they do not preserve identity continuity. This paper proposes a three-layer architectural taxonomy for AI state: conversational state, associative state, and dispositional state. We demonstrate that dispositional state is required for identity-preserving applications such as therapeutic AI, tutoring systems, and autonomous agents. We present architectural patterns, runtime design principles, and failure-mode mitigations for implementing dispositional state without modifying the stateless foundation of LLM APIs.# The contribution is a reference framework and technical implementation pattern for persistent identity governance in AI agents. This shifts the focus from context reconstruction to continuity of reasoning, behavior, and ethical constraints across sessions. |
Canonical claim: Stateless model plus memory retrieval is not identity continuity. Identity continuity requires a
dispositional governance layer.
Keywords: stateful AI, identity persistence, LLM architecture, cognitive runtime, dispositional continuity, memory
systems, agent orchestration
Files
Stateful_Reasoning_Runtimes_v2.pdf
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Additional details
Additional titles
- Alternative title (English)
- Stateful Reasoning Runtimes: Architectural Patterns for Identity Persistence Over Stateless LLM APIs
Identifiers
Related works
- Cites
- Thesis: 10.5281/zenodo.17280692 (DOI)
Dates
- Copyrighted
-
2025-11-07© Tionne Smith, All Rights Reserved
- Submitted
-
2025-12-02Research
- Updated
-
2026-07-02Implementation
Software
- Repository URL
- https://github.com/electricwolfemarshmallowhypertext/stateful-reasoning-runtime-reference/releases/tag/v0.3.0
- Programming language
- Python
- Development Status
- Active
References
- Smith, T. (2025). Stateful Reasoning Runtimes: Architectural Patterns for Identity Persistence Over Stateless LLM
- APIs. Zenodo. DOI: 10.5281/zenodo.17755157
- Smith, T. (2025). Dignity-First Artificial Intelligence: Privacy, Ethics, and Human Agency in Stateful Systems. Zenodo. DOI: 10.5281/zenodo.17705201
- Smith, T. (2025). Living Thesis: Continuous Validation of Stateful AI Architectures. Zenodo. DOI: 10.5281/zenodo.17280692