The Coherence Trap: Why LLMs Feel Smart (But Aren’t Thinking) - Travis Frisinger
Jun 3, 2025 · 20:47
Travis Frisinger argues that large language models are not intelligent but coherent, introducing 'coherence reconstruction' as a mental model for understanding their utility. He explains that hallucinations are a feature, not a bug, as models fill gaps predictably to maintain pattern completion. Prompts act as force vectors navigating latent space, and the key to reliable outputs is a 'frame, generate, judge, iterate' loop. Frisinger presents a three-layer model: latent space, execution layer (tools and RAG), and conversational interface. He advises engineers to design for emergence rather than control, using dense context as anchors to steer generation, and to watch for breakdowns in tone as early signs of lost coherence.
