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As of Oct 12, U-Space maps semantic anchors for doubt and certainty into a model's residual space, covering ambiguity, incomplete information, conflicting evidence and general uncertainty.
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Researchers release U-Space, a label-free way to read LLM uncertainty from hidden states in a single pass
Posted on arXiv on Oct 12, 2026 (source: arxiv.org).
U-Space maps semantic anchors for doubt and certainty into a model's residual space, covering ambiguity, incomplete information, conflicting evidence and general uncertainty. Its U-Lens score combines how strongly each token's hidden state leans toward doubt with predictive entropy, from the one generation that produced the answer, with no correctness labels, repeated sampling or training. The authors say it beats established baselines on reasoning benchmarks, including length-controlled evaluation; code is on GitHub.
Source: arXiv
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