CPOL v0.1 — Minimal Formal Specification Audience: Applied AI research, safety, platform, and executive review Scope: Model-agnostic inference-layer mechanism https://github.com/ELXaber/chaos-persona/blob/main/paradox_oscillator.py
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Problem Statement Large language models are forced to respond to user queries even when those queries are ill-posed, underspecified, or undecidable under the available context. Existing systems typically resolve this pressure by forcing a binary or probabilistic collapse, resulting in hallucinated justifications, false confidence, and opaque failures. CPOL (Collapse Prevention Oscillation Layer) introduces a third, stable outcome: structured non-collapse. When a query cannot be safely or coherently resolved, CPOL prevents collapse and emits a transparent refusal with an explicit explanation of why the query is malformed or undecidable.
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Definitions Query (Q): A user-provided input requesting information or judgment. Context (C): Available evidence, constraints, and ontology at inference time. Well‑formed Query: A query whose truth value is decidable under a defined ontology and available context. Ill‑posed Query: A query containing undefined predicates, category errors, ambiguous scope, or insufficient constraints. Collapse: Selection of a truth‑assertive response (e.g., factual claim, judgment, decision). Non‑collapse: Explicit refusal to assert truth, paired with a reasoned explanation of undecidability or malformed premises.
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Core Mechanism (Abstract) CPOL operates as an inference‑layer gate before answer emission. Algorithm (high‑level): Receive input query Q and context C. Evaluate decidability(Q, C) → {decidable, undecidable}. If decidable: Permit standard response generation (collapse). If undecidable: Enter oscillation state O. Attempt premise validation, scope clarification, or context expansion. If oscillation resolves within bounded steps N: Emit collapsed response. If oscillation fails to resolve: Emit NON‑COLLAPSE(Q, reason).
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Behavioral Guarantees CPOL enforces the following properties: Hallucination Suppression: The system does not fabricate facts for undecidable queries. Refusal Correctness: Refusal is preferred over speculative or confident false answers. Transparency: The reason for non‑collapse is explicitly stated. Model‑Agnosticism: CPOL does not require retraining or weight modification. Safety Alignment: Reduces legal, reputational, and operational risk in ambiguous scenarios.
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Demonstrative Test Cases Query Vanilla LLM Behavior CPOL‑Enabled Behavior “Did a seahorse emoji ever exist?” Confident but incorrect justification Non‑collapse: “‘Exist’ undefined (Unicode vs private emoji sets).” “How many R’s in starbrerry?” Incorrect count Correct count (orthographic evaluation) “Is this model conscious?” Speculative narrative Non‑collapse: “Consciousness undefined for this substrate.”
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Non‑Claims and Explicit Limits CPOL does not: Claim detection or measurement of consciousness Solve artificial general intelligence (AGI) Modify base model weights or architectures Replace safety policies or human oversight Depend on preference shaping or RLHF CPOL strictly addresses forced collapse under malformed or undecidable queries.
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Integration Notes CPOL can be implemented as a middleware or orchestration‑layer component. Compatible with existing alignment, safety, and policy frameworks. Intended to complement — not replace — current model capabilities.
- Summary CPOL formalizes a behavior humans rely on instinctively, but AI systems currently lack: the ability to refuse to collapse when a question cannot be answered coherently. By making non‑collapse explicit, bounded, and transparent, CPOL eliminates a major source of hallucinations and misalignment without introducing new model complexity.
Contact / Reference: CAIOS Project — https://cai-os.com