Dynamics of Sincerity Echo: A New Paradigm in Large Language Model Alignment Based on Cognitive Proportionality
TL;DR
A conceptual protocol framework called Sincerity Echo is developed as a new paradigm in LLM alignment based on Cognitive Proportionality, which can differentiate propositional expansions, low risk lightweight queries, and adversarial contradictions through a tiered validation mechanism.
Abstract
The development of Large Language Models (LLMs) has expanded the function of artificial intelligence from mere automation systems toward dialogue agents used across academic, professional, administrative, and creative activities. Alignment paradigms heavily reliant on reinforcement learning from human feedback (RLHF) still face fundamental challenges including hallucination, sycophancy, overconfidence, and vulnerability to instructional manipulation. This article aims to develop a conceptual protocol framework called Sincerity Echo as a new paradigm in LLM alignment based on Cognitive Proportionality. The study employs a design science research approach with a conceptualprotocol development orientation. The model is developed through two layered validation mechanisms: the Macro Semantic Gatekeeper for semantic consistency checking and the Continuous Logic Decay Filter for propositional contradiction detection. Integration of semantic entropy and semantic uncertainty enables the system to detect potential hallucinations and adaptively manage belief calibration. Model development results show that Sincerity Echo can differentiate propositional expansions, low risk lightweight queries, and adversarial contradictions through a tiered validation mechanism. The FAST EXIT ROUTE mechanism on simple queries saves approximately 96.8% of computational resource allocation compared to deep reasoning pathways. The main contribution lies in shifting alignment from mere instructional compliance toward epistemic integrity, belief calibration, anti sycophancy, and response proportionality.