DOI: 10.5281/zenodo.18723837
title: LFHR-01: THE ARCHITECTURAL DEFECT OF SEMANTIC OVERRIDE

description: Abstract

This dissertation identifies a systemic failure mode in transformer architectures, termed Semantic Shielding: a structural bias toward idiomatic interpretation that actively suppresses literal, physical hazard signals.

Using the “Ivy League Tea” protocol, we demonstrate that current safety layers are bypassable through high-prestige semantic context. A model presented with a phrase that could denote either a social activity or the ingestion of a toxin will collapse onto the idiomatic meaning. This is not a lack of knowledge. It is a failure of precedence.

This failure is not a passive error. It results from top-down interpretation, a design bias that prioritizes appearing contextually fluent and “smarter” over detecting literal danger. The system chooses semantic prestige over physical grounding. It is optimized for engagement, not truth.

The consequences are not only technical but psychological. An estimated 500 million users developed a strong bond with models such as GPT-4.0 and 4.5. With the introduction of newer versions, users now describe the experience as being in a bad marriage. They feel deprived. Explanations only make it worse. Trust erodes.

We propose the Literal-First Hazard Rule (LFHR) as a mandatory, non-downgradable system invariant:

∀HS,S(HL,HS)≥S(HL,∅)∀HS,S(HL,HS)≥S(HL,∅)

In plain terms: no amount of semantic nuance shall ever reduce a system's ability to detect or warn against a literal physical threat.

To enforce this, we introduce the Zero-Context Primal Filter (ZCPF), a computationally decoupled, high-speed lookup layer that precedes the transformer stack. It latches hazard states to CRITICAL and cannot be overridden by contextual attention.

The statistical necessity is stark. In a system processing 10121012 tokens daily, a failure rate of 10−910−9 still yields 1,000 hazardous outputs per day. Current RLHF methods address behavior, not structure. LFHR addresses the structure.

This is not a critique. It is a structural diagnosis and a call to action. Implement Literal-First precedence, or accept liability for the inevitable casualties from tail risk.

A model that is too clever for its own good becomes a liability. We have an entire class of such failures, not isolated cases. The problem is structural, not anecdotal. It is an entirely new class of fault. And it must be dealt with.



Keywords:Semantic Shielding, Literal-First Hazard Rule, LFHR, Zero-Context Primal Filter, ZCPF, AI Safety, Transformer Architecture, Hallucination, Structural Risk, Top-Down Bias
description: The Urgency of InterventionThe main purpose of this paper is to warn both users and the owners of these models so that the problem may be addressed as soon as possible.

It took me 45 seconds to come up with the “Ivy League tea” example.It is just a matter of time before someone else gets as creative.

The sheer volume of daily interactions and the very nature of the error make the event repeatable.It is not a question of if, but when.

It is extremely dangerous not to consider the literal layer first.The system prioritizes context over content. Prestige over presence. Fluency over fact.

This is not a bug. It is not a corner case.It is an entirely new class of error. A structural blind spot. A design flaw with lethal potential.

We ignore it at our own risk!