Constraint-Governed Prompt Fields (C-GPF): Soft Representation Engineering for Causal Reasoning Stability in Large Language Models

Conceptual Framework for Soft Representation Engineering for Causal Reasoning Stability in Large Language Models This paper introduces Constraint-Governed Prompt Fields (C-GPF), a novel conceptual framework designed to stabilize reasoning trajectories in Large Language Models (LLMs). While LLMs demonstrate high linguistic fluency, they frequently suffer from "Trajectory Instability"—a phenomenon where internal reasoning drifts from initial constraints as the token count increases. C-GPF reframes prompting not as a sequence of instructions, but as a Structural Boundary Condition: a layered prompt-field environment designed to guide observable reasoning behavior without requiring direct access to model weights or internal activations. The framework is positioned as a black-box-compatible approach to Soft Representation Engineering, focused on improving trajectory-level reasoning stability, constraint fidelity, and evidence-sensitive generation. Key Contributions: Era III: Latent Orchestration: The work proposes a transition from sequential, instruction-based prompting or Syntactic Coercion toward Field-Based Interaction. Rather than commanding the model step by step, C-GPF defines the admissible reasoning space within which stable reasoning trajectories become more likely. Validity vs. Intent-Shaping Constraints: The framework distinguishes between two functional classes of constraints. Validity-Preserving Constraints act as structural guardrails that reduce contradictions, unsupported inferences, and hallucinated causal links. Intent-Shaping Constraints provide navigational bias by defining task context, domain framing, persona, or output requirements, while remaining subordinate to epistemic integrity. Uncertainty Gates & Structural Silence: To mitigate premature closure and fluent hallucination, C-GPF introduces Uncertainty Gates and the Right to Halt. When evidentiary thresholds are not met, the model is conditioned to identify gaps, request clarification, or remain structurally silent rather than produce unsupported conclusions. Metrology of Causal Coherence: A comprehensive evaluation agenda centered on trajectory-level stability. The accompanying Measurement Protocol for Causal Coherence provides a trajectory-level evaluation agenda for assessing reasoning stability beyond final-answer accuracy. It formalizes metrics such as Semantic Drift Rate (SDR), Token Metabolic Efficiency (TME), Inference-Evidence Fidelity (IEF), Contradiction Density (CD), and Ablation Resilience (AR), along with measurement procedures, validation guidance, and reproducibility requirements. Scholarly Context:C-GPF is positioned as a prerequisite conceptual layer for advanced latent orchestration architectures such as SACS-LO (Self-Assembling Cognitive Substrate for Latent Orchestration). It aims to support a shift from heuristic prompt engineering toward more auditable, constraint-aware, and evidence-sensitive AI interaction design. This work is offered as a conceptual framework and measurement agenda, not as a validated standard, completed empirical study, or prescriptive deployment protocol. Empirical validation of the proposed metrics, thresholds, and ablation procedures is left for future work. Material Information: This research suite includes 2 primary components. Main Manuscript: Constraint-Governed Prompt Fields (C-GPF): Soft Representation Engineering for Causal Reasoning Stability in Large Language Models — the core theoretical framework. Measurement Protocol for Causal Coherence: a methodological supplement providing formal metric definitions, mathematical formulas, measurement procedures, validation protocols, and reproducibility guidance for trajectory-level assessment of LLM reasoning stability. This suite is intended as both a theoretical anchor and a practical guide for researchers, AI engineers, and governance practitioners seeking to evaluate LLM reasoning stability beyond final-answer accuracy. Note on Supplementary Media: The Audio Overview and Visual Presentation / Slide Deck are provided as interpretive companions to support conceptual understanding of the framework. They are AI-synthesized explanatory materials and should not be treated as formal components of the scholarly manuscript or technical measurement protocol. For precise definitions, formulas, methodological procedures, and citable claims, readers should refer to the Main Manuscript and the Measurement Protocol. These companion media may paraphrase, simplify, or visually interpret the research and are not intended to replace the formal written materials.