Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations
Large language models (LLMs) hallucinate: they produce fluent outputs that are factually incorrect. We present a geometric dynamical systems framework in which hallucinations arise from task-dependent basin structure in latent space. Using autoregressive hidden-state trajectories across multiple open-source models and benchmarks, we find that separability is strongly task-dependent rather than universal: factoid settings can show clearer basin separation, whereas summarization and misconception-heavy settings are typically less stable and often overlap. We formalize this behavior with task-complexity and multi-basin theorems, characterize basin emergence in L-layer transformers, and show ...
| Authors | Kalyan Cherukuri, Lav R. Varshney |
|---|---|
| Primary category | cs.CL |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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