CSLV: Cross-Source Logical Validation for Reducing Source-Induced Reasoning Bias in Large Language Models

This work introduces CSLV (Cross-Source Logical Validation), a framework designed to detect and mitigate source-induced reasoning bias in large language models. The approach evaluates the logical consistency between the original query, provided examples, and the model's generated reasoning. The report also evaluates ANIMA, a cognitive architecture concept that integrates identity-based memory structures and validation layers to enhance reasoning reliability in AI systems. Experimental validation and conceptual analysis are provided to demonstrate how cross-source validation can reduce hallucination and example-dependency in LLM outputs.