The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2606. 00467v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with user-provided instructions.
arXiv:2610.34610v1 Announce Type: new Abstract: Vision-language anomaly detectors describe normal and abnormal appearance with text, and their gains are commonly credited to the semantic content of t...
arXiv:2509. 21012v4 Announce Type: replace-cross Abstract: In-context Learning (ICL) is an emerging few-shot learning paradigm based on modern Language Models (LMs), yet its inner mechanism remains unclear.
The paper examines how Large Language Models’ internal representations become sparser as task difficulty rises, finding that harder inputs lead to more concentrated activation patterns in the last hidden state. This sparsity–difficulty relationship holds across various models and domains, indicating that models learn dense representations for familiar patterns but default to sparse activations for unfamiliar ones. Leveraging this insight, the authors propose Sparsity‑Guided Curriculum In‑Context Learning (SG‑ICL), which selects few‑shot demonstrations based on sparsity and outperforms standard CoT and Auto‑CoT baselines on the MATH‑500 benchmark.
arXiv:2602. 23197v2 Announce Type: replace-cross Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations.
Why do language models fail on long inputs? The common answer is length. We show that, for retrieval, length is cheap and competition is not. On five open models (0.5B to 3B parameters, four families)...