2026-10-12 · America/Los_Angeles · 社区动态 · #14

U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]

If you build on open-weight reasoning models, you have probably wanted a "how sure is the model about this answer?" signal that does not cost 5x sampling, a judge model, or a labeled dataset. Hence, we built the U-Lens. In one line: score = (how strongly the hidden state points toward different "doubt" directions) x (mean token entropy over the reasoning tokens) Label-free: No correctness labels; no training and no no fine-tuning. Single pass. No resampling. Interpretable. It tells you what kind of uncertainty: (i) ambiguity, (ii) missing information, (iii) conflicting evidence, or (iv)…

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