ActMap: Single-Pass Uncertainty Quantification from Generation-Time Activation Maps
Practical uncertainty quantification (UQ) for large language models must decide, from a single generation, whether a specific answer should be trusted. Existing methods either sample multiple generations, read only output-token probabilities, or reduce the model's internal computation to a single hidden state. We introduce ActMap, a white-box representation that compresses the generation-time hidden-state trajectory (every layer, every generated token) into a fixed $12\times32\times128$ tensor of temporal-statistic channels that preserves structure across transformer depth and pooled hidden coordinates. The map is captured during the generation pass with no measurable overhead, has a fixe...
| Authors | Jacopo Dardini, Roberta Calegari |
|---|---|
| Primary category | cs.AI |
| Published | 10 September 2026 |
| Last updated at source | 11 September 2026 |
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