CDRA: Context-Deployed Receptive Alignment — A Training-Free Behavioral Suppression Method for Large Language Models

Paper Introduction: CDRA (Constraint-based Dialogue Response Architecture) Core Contribution This paper introduces CDRA — a training-free, context-driven behavioral control framework for Large Language Models — that modulates dialogue behavior in real time without modifying model weights or architecture. Technical Innovation Training-Free Behavioral Routing: CDRA avoids the computational cost and data requirements of conventional alignment methods (SFT, RLHF), offering a lightweight, deployable alternative that works at inference time. Context-Level Constraint Network: A minimal, interpretable constraint network (~70 words sufficient) operates on the input context to guide response behavior while leaving the model's knowledge and reasoning intact. Selective Suppression: Eliminates unsolicited advice on emotional and boundary inputs while fully preserving task execution — behavior is routed, not uniformly suppressed. Experimental Validation Scope: 16 LLM instances across 6 architecture families (MoE, Dense, GPT, Gemini, Qwen, Llama), ~942 total test cases. Key Results: Unsolicited advice rate: 100% → 0% on emotional and boundary inputs, consistent across all tested models; Task completion rate: 100% maintained, zero degradation; Effect confirmed via ablation (effective component = behavioral rules, not identity narrative) and blind third-party evaluation (Cohen's κ ≈ 1.0). Contributions & Vision Establishes behavioral routing via contextual constraints as a scalable alternative to training-centric alignment — shifting focus from model internals to runtime behavior control. Proposes Xuqi-Daoqi, a blueprint for natively receptive AI architecture that bakes behavioral modulation into the transformer backbone rather than injecting it at inference time.