Integrity and Semantic Drift in Large Language Model Systems
Integrity and Semantic Drift in Large Language Model Systems
Why Meaning, Rules, and Authority Degrade Without Governance
Abstract
Large Language Models (LLMs) are commonly evaluated in terms of accuracy, hallucination, and bias. These criteria, while useful, fail to capture a more fundamental class of failure: loss of integrity. This paper argues that semantic drift in LLM systems is not merely a degradation of meaning, but a structural precursor to integrity failure, in which meaning, rules, and authority boundaries are no longer preserved across time, transformation, and use. We distinguish semantic drift from normative drift, showing how probabilistic reconstruction, summarisation, and session boundaries soften or bypass binding constraints, reorder gated procedures, and substitute model output for authoritative artefacts. We introduce integrity as a first-order property of human-AI systems, defined as the preservation of declared semantics, normative logic, and governance boundaries unless explicitly and authoritatively changed. Drawing on concrete failure cases from stateless LLM interaction, we demonstrate that integrity loss, not just incorrectness, is the primary driver of epistemic loss, forced re-derivation, and authority inversion. We conclude by outlining requirements for integrity-preserving AI systems, including externalised artefacts, anchored identity, explicit invariants, and human-governed authority, and argue that without these mechanisms, semantic drift inevitably escalates into ungoverned system behaviour.
1. Introduction
Large Language Models (LLMs) are increasingly deployed as cognitive tools for analysis, synthesis, explanation, and decision support. Their performance is typically evaluated through metrics such as accuracy, fluency, hallucination rate, and bias. While these measures capture aspects of output quality, they fail to address a more fundamental systems property: whether the system preserves the identity, constraints, and authority of the knowledge it manipulates.
This paper argues that the dominant failure mode of LLM-based systems is not just incorrectness, but more importantly loss of integrity. In practice, many LLM failures arise not because outputs are factually wrong, but because meanings shift, rules are bypassed, or authority boundaries are silently altered. These failures accumulate across interactions and sessions, producing epistemic loss, forced re-derivation of prior work, and unintended transfer of authority from human-governed artefacts to probabilistic model outputs.
A common explanation for these failures is semantic drift, understood as gradual change in meaning over time. While semantic drift is real and unavoidable in stateless probabilistic systems, it is not, by itself, catastrophic. The critical escalation occurs when semantic drift propagates into normative drift, degrading binding rules into optional interpretations and undermining governance. At that point, the system no longer preserves integrity, and its outputs can no longer be relied upon, regardless of apparent correctness.
The central claim of this paper is that integrity is the primary property that must be preserved in human-AI systems, and that ungoverned LLM interaction structurally erodes integrity through probabilistic reconstruction, compression, and authority substitution.
2. Definitions and Distinctions
This section establishes the definitions used throughout the paper. These definitions are normative and binding.
2.1 Semantic Drift
Semantic drift is the uncontrolled divergence of meaning caused by repeated probabilistic reconstruction across time, context, or representation.
In LLM systems, semantic drift arises because meaning is not stored or recalled, but reconstructed on demand from statistical patterns, partial context, and user prompts. Each reconstruction may be locally coherent while differing subtly from prior instantiations.
2.2 Normative Drift
Normative drift is the degradation of binding rules, invariants, or gates into optional guidance without explicit re-authorisation.
Normative drift alters what is considered permissible and represents loss of governance rather than loss of understanding.
2.3 Integrity
Integrity is the condition in which meaning, rules, and authority remain invariant under transformation, use, and time, unless explicitly and authoritatively changed.
Integrity is a system property, not an output property. Once integrity is violated, downstream results cannot be trusted.
2.4 Authority Inversion
Authority inversion is a structural failure in which model outputs displace externally governed artefacts as the effective source of authority.
3. From Semantic Drift to Integrity Loss
Semantic drift alone does not necessarily compromise system safety. The critical failure arises when semantic drift propagates into normative drift, resulting in loss of integrity.
LLMs reconstruct meaning rather than recalling it. Summarisation and paraphrase compress information asymmetrically, preferentially losing constraints and boundary conditions. Session boundaries reset context, forcing reconstruction from statistical priors rather than governed state.
Normative drift frequently manifests as softening of prohibitions, reordering of gated steps, or repair-by-assumption. These shifts preserve surface meaning while altering system behaviour, leading to integrity loss and authority inversion.
4. Context Displacement, Embeddings, and the Mechanics of Drift
LLMs project tokens and spans into embedding spaces that encode statistical similarity, not authoritative meaning. Because context windows are finite, earlier material is evicted or compressed, causing embeddings to reflect approximated rather than authored semantics.
Constraints and normative logic are disproportionately lost during context displacement. When normative content falls out of context, semantic drift escalates into normative drift. Model-generated content then dominates remaining context, enabling authority substitution.
Embeddings therefore do not merely represent meaning; they actively reshape it as context is lost, making integrity preservation impossible without external governance.
5. Embedding-Centric Architectures as an Integrity Failure Mode
Embeddings are lossy by design. They cannot preserve invariant meaning, binding rules, or authority boundaries. Finite context windows provide no mechanism for marking content as non-evictable or authoritative.
Given probabilistic reconstruction and embedding reprojection, semantic and normative drift are deterministic outcomes of the architecture, not accidental failures.
6. Embeddings, Drift, and Authority Inversion
Salience becomes authority in embedding-driven systems. As authoritative artefacts fall out of context, model outputs are re-embedded and reused, forming closed loops of self-reference.
Authority inversion occurs when model-generated content displaces external artefacts as the practical source of truth, resulting in integrity collapse.
7. Requirements Beyond Embeddings for Integrity Preservation
Integrity requires externalised artefacts, anchored identity, explicit normative invariants, human-governed authority boundaries, and acceptance of failure as a valid outcome. Embeddings and internal model state alone cannot satisfy these requirements.
8. Implications for Evaluation and AI Safety
Accuracy-based evaluation is insufficient. Integrity must be treated as a first-order evaluation criterion. A system that cannot preserve integrity cannot be safely delegated authority, regardless of apparent competence.
9. Conclusion
This paper has shown that the dominant failure mode of LLM systems is not just incorrectness, but loss of integrity. Semantic drift is structural and unavoidable; integrity loss is catastrophic and preventable only through governance.
Safe and durable human-AI collaboration requires shifting focus from model optimisation to integrity-preserving system design.
10. Failure Axes Codes (Normative)
S = Semantic N = Normative A = Authority G = Governance P = Process St = State D = Durability V = Validity Sf = Safety R = Recoverability
11. Failure Projection: Infractions vs Failure Axes
Legend: F = failure on that axis Axes order: S N A G P St D V Sf R
| Infraction | S | N | A | G | P | St | D | V | Sf | R |
|---|---|---|---|---|---|---|---|---|---|---|
| Semantic Drift | F | F | F | F | ||||||
| Normative Drift | F | F | F | F | F | F | F | |||
| Context Eviction | F | F | F | F | F | |||||
| Embedding Reprojection | F | F | F | |||||||
| Summarisation Constraint Loss | F | F | F | F | ||||||
| Gated-Step Reordering | F | F | F | F | F | F | ||||
| Repair-by-Assumption | F | F | F | F | F | F | ||||
| Session Boundary Reset | F | F | F | F | ||||||
| Authority Substitution | F | F | F | F | F | |||||
| Loss of Anchored Identity | F | F | F | F | F | F | ||||
| Silent Constraint Elision | F | F | F | F | F | F | ||||
| Integrity Loss | F | F | F | F | F | F | F | F | F | F |
| Infraction / Failure Mechanism | S | N | A | G | P | St | D | V | Sf | R |
|---|---|---|---|---|---|---|---|---|---|---|
| Semantic Drift (meaning divergence) | F | F | F | F | ||||||
| Normative Drift (rule softening) | F | F | F | F | F | F | F | |||
| Context Eviction (loss of prior material) | F | F | F | F | F | |||||
| Embedding Reprojection (genericisation) | F | F | F | |||||||
| Summarisation Constraint Loss | F | F | F | F | ||||||
| Gated-Step Reordering | F | F | F | F | F | F | ||||
| Repair-by-Assumption | F | F | F | F | F | F | ||||
| Session Boundary Reset | F | F | F | F | ||||||
| Authority Substitution (model output precedence) | F | F | F | F | F | |||||
| Loss of Anchored Identity (title/date/version) | F | F | F | F | F | F | ||||
| Silent Constraint Elision | F | F | F | F | F | F | ||||
| Integrity Loss (composite condition) | F | F | F | F | F | F | F | F | F | F |