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Semantic Fidelity Failure Modes
A structured collection of failure modes in large language model (LLM) systems where outputs remain coherent while losing alignment with meaning, intent, and underlying reality.
Overview
Modern AI systems often appear to work correctly. Outputs are fluent, structured, and internally consistent.
But coherence is not the same as correctness.
This dataset documents a recurring pattern:
Systems preserve structure while meaning degrades across transformations.
Each document isolates a specific breakdown across modern AI pipelines and reframes common issues such as hallucination, benchmark gaps, and inconsistency as expressions of a shared structural failure.
Contents
- SFL 01 — ChatGPT Sounds Right But Wrong [OSF] [figshare]
- SFL 02 — AI Benchmarks vs Real World Performance [OSF] [figshare]
- SFL 03 — Embedding Similarity vs Meaning [OSF] [figshare]
- SFL 04 — Multi Agent Alignment Drift [OSF] [figshare]
- SFL 05 — Agent Drift Measurement [OSF] [figshare]
- SFL 06 — Retrieval vs Interpretation [OSF] [figshare]
- SFL 07 — Multi Step Inconsistency [OSF] [figshare]
- SFL 08 — RAG Evaluation Beyond Accuracy [OSF] [figshare]
Failure Modes Covered
- Semantic misalignment (coherent but wrong outputs)
- Evaluation misalignment (benchmarks vs real-world behavior)
- Embedding compression error (similarity ≠ meaning)
- Multi-agent drift (cascading misalignment across systems)
- Agent drift measurement gaps (unobserved deviation over steps)
- Retrieval vs interpretation failure (correct data, wrong output)
- Multi-step inconsistency (local correctness, global breakdown)
- RAG evaluation blindness (accuracy without meaning preservation)
Core Concept: Semantic Fidelity
This collection introduces semantic fidelity as a lens for evaluating whether meaning and intent are preserved across:
- representation
- retrieval
- reasoning
- generation
Most current evaluation methods focus on correctness at specific steps.
This work focuses on whether meaning survives across the entire system.
Intended Use
This dataset is useful for:
- analyzing LLM failure modes
- designing evaluation frameworks beyond accuracy
- studying agent and multi-step system behavior
- understanding RAG limitations
- exploring alignment beyond surface-level correctness
Not Intended For
This is not a benchmark dataset or training dataset.
It is a conceptual and analytical resource for understanding system behavior.
Core framework and sources
- Research Library (GitHub): Semantic Fidelity Lab Repository
- Articles & Essays (Substack): Semantic Fidelity Lab Substack
- Primary DOI Record: Figshare DOI Entry
- Concept Glossary: Semantic Fidelity Glossary
License
CC BY 4.0
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