Hugging Face Trending Papers
Chain-of-Thought Entropy as a Reliability Signal: A Preregistered Reproduction
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arXiv Machine Learning
Sep 18
Chain-of-Thought Entropy as a Reliability Signal: A Preregistered Reproduction
This study independently reproduces the dissociation reported by Zhao (2026) regarding chain-of-thought entropy in large language models. It confirms that the shape of the entropy trajectory predicts answer correctness, while the total entropy drop magnitude does not, across four open-weight models and two benchmarks (GSM8K and MATH‑500). The reproduction also maps settings where the magnitude signal holds or fails and documents protocol differences not reported in the original work.
arXiv AI
Jul 10
When Thinking Hurts: Epistemic Signals in the Reasoning Chains of Visual Language Models
arXiv:2607. 08059v1 Announce Type: cross Abstract: Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution.
arXiv AI
Jul 29
Reliability Scales Inversely: Hallucinations Snowball Faster in Bigger Language Models
arXiv:2607. 18292v3 Announce Type: replace-cross Abstract: Bigger language models are less reliable.
arXiv Machine Learning
Aug 12
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
arXiv:2604. 08941v2 Announce Type: replace Abstract: Medical Vision-Language Models (VLMs) answering binary presence questions on chest radiographs can fail in two linked ways: they are confidently wrong, and they change answers when a clinically equivalent question is rephrased.
arXiv AI
Sep 10
Quantization Amplifies Determinism, Not Bias: Scale-Dependent Behavioral Effects of Serving-Time Weight Compression
arXiv:2609.07901v1 Announce Type: new Abstract: Weight quantization largely determines the economics of serving open-weight LLMs. Its costs are usually assessed with capability benchmarks, on which 4...
arXiv Computation and Language
6d ago
Where a Model Sends Its Own Repeated Token
arXiv:2609. 31181v1 Announce Type: new Abstract: Black-box model identification works by scoring a model's response to natural-language prompts.