Semantic Fidelity Lab Series: Failure Modes in LLM Systems
A collection of core documents examining a recurring failure pattern in modern AI systems: outputs remain coherent, fluent, and well-structured while gradually losing alignment with meaning, intent, and underlying reality.
These documents focus on specific breakdowns across large language model pipelines, including semantic misalignment, benchmark evaluation gaps, embedding compression error, multi-agent drift, interpretation failure, and multi-step inconsistency. Each paper isolates a failure mode that is often treated as a separate issue, but emerges from a shared structural problem.
As language is compressed, transformed, and reused across systems, meaning is not preserved by default. Structure survives. Fluency survives. But alignment degrades.
This collection introduces semantic fidelity as a lens for evaluating whether meaning and intent are preserved across representation, retrieval, reasoning, and generation in AI systems.
Together, these documents map how drift emerges not as a singular failure, but as a systemic property of modern AI architectures operating under compression, scale, and optimization.
Documents — Failure Modes in LLM Systems
- Why ChatGPT Sounds Right But Is Wrong (SFL 01) [PDF]
Explains how language models can produce convincing outputs while failing to preserve meaning and intent.
[DOI] [Github] [Hugging Face]
- Why AI Benchmarks Fail in the Real World (SFL 02) [PDF]
Shows how benchmark performance diverges from real-world reliability due to evaluation misalignment.
[DOI] [Github] [Hugging Face]
- Why Embedding Similarity Is Not Understanding (SFL 03) [PDF]
Breaks down how vector similarity captures structure but not meaning, leading to misleading retrieval.
[DOI] [Github] [Hugging Face]
- Why Multi-Agent Systems Drift Over Time (SFL 04) [PDF]
Examines how meaning degrades across chained agents while coordination remains intact.
[DOI] [Github] [Hugging Face]
- How to Measure Agent Drift in LLM Systems (SFL 05) [PDF]
Explains why task completion fails as a metric and how drift accumulates across steps.
[DOI] [Github] [Hugging Face]
- Why Retrieval Can Be Correct but the Answer Is Wrong (SFL 06) [PDF]
Shows how systems retrieve correct information but distort meaning during interpretation.
[DOI] [Github] [Hugging Face]
- Why AI Outputs Become Inconsistent Across Steps (SFL 07) [PDF]
Explains how small deviations accumulate across steps, leading to inconsistency.
[DOI] [Github] [Hugging Face]
- Why Accuracy Fails as a Metric for RAG Systems (SFL 08) [PDF]
Examines how accuracy metrics miss meaning degradation in retrieval-augmented systems.
[DOI] [Github] [Hugging Face]
Related Items: Semantic Fidelity Lab Canonical Glossary
This work is part of the Semantic Fidelity Lab and has been integrated into the broader Reality Drift framework. This site functions as a lightweight archive and reference layer. Primary essays and long-form writing are distributed across external platforms:
Substack · GitHub · DOI · Slideshare
Part of the Reality Drift Framework by A. Jacobs (2023–2026)