U-Space 提出 U-Lens：免标注单次前向估计语言模型不确定性
U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]
研究者发布 U-Space 项目，提出 U-Lens 不确定性估计方法，用隐藏状态在 4 个“怀疑”方向上的投影乘以推理 token 的平均熵，无需正确性标注、无需训练或微调、单次前向即可给出置信信号。
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) general uncertainty. (not necessary for many applications but potentially useful)
- Strong. Best estimator in our 3-seed, 3-model, 4-benchmarks evaluation (on Gemma 4, Qwen3.5 and Magistral). ahead of 5-pass, 10-pass and supervised baselines.
Small caveat:
The only model-specific ingredient is a Jacobian lens (the J-Lens from Anthropic's global workspace paper). Once you have one:
- Build the 4-direction U-Space once, on CPU. It only requires the unembedding rows of a few hundred words, you don't need to load the full model.
- At inference, grab one hidden state at the end of thinking (a forward hook on one layer), dot it with 4 vectors, and multiply by the mean entropy you get from the logits anyway.
Obtaining a fitted J-Lens
- 40+ pre-fitted lenses are on Hugging Face (neuronpedia/jacobian-lens): Gemma 2/3/4, Qwen3/3.5/3.6, Llama 3.1/3.3, gpt-oss-20b, OLMo 3 and more.
- Your model is not there? Fit one with Anthropic's reference code (anthropics/jacobian-lens, jlens.fit): about 100 unlabeled prompts, no parameters trained, cost dominated by backward passes. That is how we built the Magistral lens, which ships in our repo. --> Usually, i checked whether the Lens is proper by using the examples from the Anthropic blog post.
Entry point:
git clone https://github.com/s2labres/U-Space && cd U-Space pip install -r requirements.txt ./run_smoke.sh # Qwen3.5-4B, 10 TriviaQA questions, end to end (1 GPU) from src import uspace U, categories, _ = uspace.basis(model_cfg, terms) # once per model score = uspace.a_cone(eot_state[None], U) * think_entropy.mean() # per answer What we would love to see people build:
- Route low-confidence answers to a bigger model or a human.
- Trigger retrieval when missing information lights up.
- Ask a clarifying question when ambiguity lights up.
- Flag conflicting evidence in RAG pipelines.
Disclaimer: This is our research code (a reproduction pipeline, not a pip package yet). What would you plug it into? Happy to help with integration/discuss! 😄
submitted by /u/AdRevolutionary3242
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来源：r/MachineLearning · reddit.com