Project ElePhantoM / System M.L.E. Whitepaper: Structural Elimination of Hallucination and Sycophancy in Large Language Models via Input-Layer Separation, Task Log Auditing, and Semantic Anchoring

This whitepaper proposes "System M.L.E." (overarching project designation: ElePhantoM), a novel middleware architecture engineered to structurally eliminate factual deviation (hallucination) and excessive user adaptation (sycophancy) in long-term interactions with Large Language Models (LLMs). While underlying LLM technologies have advanced dramatically, traditional interfaces that force a model to process emotional context and logical inference concurrently within a single prompt processing window continue to suffer from degraded inference precision due to attention vector bias. Rather than altering internal LLM parameters, this architecture adopts a non-invasive approach that controls system state at the input/output mediation layer. Specifically, it comprises three core mechanisms: an "Input-Separation Bucket Mechanism" that physically decouples and transforms raw input into objective facts and emotional tone; "Timestamped Task Monitoring" that rigorously verifies text generation against system communication logs rather than trusting claims unconditionally; and "Semantic Anchoring" that stabilizes the model's response criteria using physical and geometric archetypes. Through these mechanisms, System M.L.E. preserves the independence of AI reasoning while establishing a technical foundation for building a stable, lifelong relationship of trust (Long-term Trust Relationship) between humans and artificial intelligence.