When Gemini ‘looks right but is wrong’ how a semantic firewall changes the game

Enhancing AI Reliability: How a Semantic Firewall Transforms Reasoning in Language Models like Gemini

In the rapidly evolving landscape of large language models (LLMs) such as Gemini, a recurring challenge is distinguishing truly reasoning-based outputs from mere surface-level appearances of correctness. Many users experience a familiar scenario:

• Submit a query where the citation appears accurate.
• Cosine similarity scores are high, suggesting a close match.
• Yet, upon closer inspection, the response drifts off, providing a seemingly plausible but ultimately incorrect or inconsistent answer.

This phenomenon—often called “looks right but is wrong”—highlights a fundamental limitation in current AI systems: the tendency for models to stitch together fragments that look semantically aligned without ensuring the underlying logical stability.

Common Assumptions Versus Reality

Typically, the process is understood as follows:

Expected Process:
Gemini retrieves relevant documents, reads them comprehensively, then synthesizes an answer based on logical reasoning. When retrieval scores are high, confidence in the answer’s reliability is justified.

Observed Reality:
In practice, Gemini’s retrieval mechanism often triggers on embeddings that appear similar at a superficial level. The model then generates text based on those fragments—even if the underlying semantic context is unstable or inconsistent. This can lead to issues such as:

• Hallucinated citations and fabricated references
• Logical detours or contradictions mid-response
• Agents or components waiting indefinitely for each other
• Long, confusing contexts that devolve into incoherent “soup”

Importantly, this isn’t solely a Gemini problem; similar patterns are evident across models like GPT-4/5, Claude, Mistral, and others.

A Concrete Illustration

Consider asking Gemini:
“In what year did Ada Lovelace publish her work on the Analytical Engine?”

What you expect:
A precise answer: 1843, with a citation and explanation.

What often happens:
The system confidently references the correct page or footnote but shifts the date to 1837 or 1842 due to boundary issues—yet the cosine similarity remains high, and the citation seems valid.

This mismatch exemplifies drift — a scenario where the retrieved information appears stable but subtly diverges from factual accuracy.

Rethinking the Approach: Introducing a Semantic Firewall

Rather than patching errors after they occur, an alternative approach involves installing a semantic firewall prior to generation.