Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866911715385409536 |
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| author | Aljaafari, Nura Valentino, Marco Freitas, André |
| author_facet | Aljaafari, Nura Valentino, Marco Freitas, André |
| contents | Predicting a label correctly does not necessarily require representing the operation that produces it. Transformer representations are known to carry label-level information, but whether they encode semantic operations producing those labels is unclear. We investigate this in Natural Language Inference using controlled premise-hypothesis pairs that differ by a single semantic transformation. Using layer-wise activations, we estimate operation-level subspaces via SVD and test their causal relevance through activation steering in four open-weight decoder models. Transformation effects are decodable with $84.8$-$99\%$ accuracy and occupy partially distinct but overlapping subspaces, exceeding random-subspace baselines. Steering experiments show that these directions causally influence predictions, though steerability varies across models; cross-operation steering further reveals structured interference and a dissociation between subspace selectivity and cross-operation independence. These findings indicate that the models encode not only that a hypothesis relates to a premise but also, in part, how it does so, implying that mechanistic analysis and control should operate at the level of semantic operations rather than predicted labels alone. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_25520 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation Aljaafari, Nura Valentino, Marco Freitas, André Computation and Language Predicting a label correctly does not necessarily require representing the operation that produces it. Transformer representations are known to carry label-level information, but whether they encode semantic operations producing those labels is unclear. We investigate this in Natural Language Inference using controlled premise-hypothesis pairs that differ by a single semantic transformation. Using layer-wise activations, we estimate operation-level subspaces via SVD and test their causal relevance through activation steering in four open-weight decoder models. Transformation effects are decodable with $84.8$-$99\%$ accuracy and occupy partially distinct but overlapping subspaces, exceeding random-subspace baselines. Steering experiments show that these directions causally influence predictions, though steerability varies across models; cross-operation steering further reveals structured interference and a dissociation between subspace selectivity and cross-operation independence. These findings indicate that the models encode not only that a hypothesis relates to a premise but also, in part, how it does so, implying that mechanistic analysis and control should operate at the level of semantic operations rather than predicted labels alone. |
| title | Is Inference Mediated by Distinct Semantic Structures in LLMs? A Mechanistic Interpretation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.25520 |