Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination
Language models draw on two knowledge sources: facts baked into weights (parametric memory, PM) and information in context (working memory, WM). We study two mechanistically distinct failure modes--conflict, when PM and WM disagree and interfere; and hallucination, when the queried fact was never learned. Both produce confident output regardless, making output-based monitoring blind by design. We show both failures share a unified geometric account. In the hidden-state space of autoregressive generation, learned facts form attractor basins. Conflict is basin competition: WM disrupts convergence to the correct basin without raising output entropy. Hallucination is basin absence: the hidden...
| Authors | Qiyao Liang, Risto Miikkulainen, Ila Fiete |
|---|---|
| Primary category | cs.AI |
| Published | 7 May 2026 |
| Last updated at source | 14 July 2026 |
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