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Furthermore, we show that a two-layer, one-head model composes information from the previous layer primarily through query-ke","title":"Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformers","url":"https://arxiv.org/abs/2510.25013","vendor":"arxiv_cs_ai"},"summary":"arXiv:2510.25013v2 Announce Type: replace-cross \nAbstract: Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits. However, the complexity of pretrained models often obscures the minimal mechanisms required for specific reasoning tasks. In this work, we train small, attention-only transformers from scratch on a symbolic version of the Indirect Object Identification (IOI) task, a benchmark for studying coreference-like reasoning in transformers. Surprisingly, a single-layer model with only two attention heads achieves perfect IOI accuracy, despite lacking MLPs and normalization layers. 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