Semantic Fidelity Paper Series: Failure Modes in LLM Systems
This collection examines a recurring failure pattern in modern AI systems. Outputs can remain fluent, coherent, and well-structured while losing alignment with meaning, intent, and the reality they are supposed to represent.
The documents focus on specific failure modes in language model systems, including semantic misalignment, evaluation gaps, embedding compression error, interpretation failure, multi-agent drift, and stepwise inconsistency. These are often treated as separate problems, but they point toward the same deeper issue. Meaning is not preserved automatically as language is compressed, retrieved, transformed, and regenerated. Surface structure can survive while fidelity degrades.
This collection uses semantic fidelity to evaluate whether meaning and intent are preserved across representation, retrieval, reasoning, and generation. Taken together, the documents map how drift becomes a systemic property of AI architectures operating under compression, scale, and optimization.
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.
- Why AI Benchmarks Fail in the Real World SFL 02 (PDF)
Shows how benchmark performance diverges from real-world reliability due to evaluation misalignment.
- Why Embedding Similarity Is Not Understanding SFL 03 (PDF)
Breaks down how vector similarity captures structure but not meaning, leading to misleading retrieval.
- Why Multi-Agent Systems Drift Over Time SFL 04 (PDF)
Examines how meaning degrades across chained agents while coordination remains intact.
- 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.
- Why Retrieval Can Be Correct but the Answer Is Wrong SFL 06 (PDF)
Shows how systems retrieve correct information but distort meaning during interpretation.
- Why AI Outputs Become Inconsistent Across Steps SFL 07 (PDF)
Explains how small deviations accumulate across steps, leading to inconsistency.
- Why Accuracy Fails as a Metric for RAG Systems SFL 08 (PDF)
Examines how accuracy metrics miss meaning degradation in retrieval-augmented systems.
Related Framework Concepts
Semantic Fidelity Resources:
- Semantic Fidelity Definition
- Glossary
- Visual Frameworks
- Semantic Fidelity Paper Series
- Failure Modes in LLM Systems
- Drift Detection in AI Systems
Core Concepts: