arXiv:2605.25520v1 Announce Type: new Abstract: 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 d
The proliferation of increasingly complex LLMs necessitates a deeper understanding of their internal mechanisms to enhance transparency, reliability, and control.
Understanding how LLMs perform inference could unlock significant advancements in explainable AI, more efficient model design, and new benchmarks for intelligence.
This research provides a foundational step towards mechanistically interpreting LLMs, moving beyond black-box empiricism to a more scientific understanding of their internal operations.
- · AI researchers
- · ML engineers
- · Explainable AI developers
- · Foundation model developers
- · Black-box AI development
- · Purely empirical AI evaluation methods
Improved debugging and robustness for large language models.
Development of more precisely steerable and controllable AI agents.
New architectures specifically designed for interpretable and causally transparent semantic operations could emerge.
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