Show HN: A Transformer Is All You Need
Researchers have developed a new method to interpret the decisions made by transformer models, which are a type of artificial intelligence. This method, called the hybrid weight-activation probe, can identify which weights in the model are responsible for a particular decision. The technique has been tested on several different transformer models and has shown promising results.
- ▪The hybrid weight-activation probe is a new method for interpreting transformer models.
- ▪The method can identify which weights in the model are responsible for a particular decision.
- ▪The technique has been tested on several different transformer models, including GPT-2 and LLaMA.
- ▪The method can be used for a variety of applications, including causal diagnostics and security and forensics.
- ▪The technique can also be used for capability operations, such as localization and transplantation.
Opening excerpt (first ~120 words) tap to expand
Published June 26, 2026 | Version v1 Preprint Open A Transformer Is All You Need Authors/Creators Lamoureux, Marc Description The unanswered question in mechanistic interpretability of pretrained transformers is plain: for any prompt and any decoder-only transformer, which weights at which layers along which residual-stream dimensions produced the decision the model emitted? Activation probing reports a per-depth accuracy curve. Sparse dictionaries decompose activations into monosemantic features. Logit and tuned lenses trace the trajectory of a prediction through the residual stream. None of these names the weight that did the work.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Zenodo.