The study investigates whether large language models retain traces of abandoned syntactic analyses after encountering garden‑path sentences. Across 24 models ranging from 124 M to 70 B parameters, the authors find that a residue of the initial, incorrect analysis influences later object attachment, while an adaptation toward the resolved structure scales with disambiguation error. Activation‑patching experiments show that the residue is carried by noun‑phrase states computed under the object analysis, and that overriding these states largely removes the effect.
By Xiang Ding
arXiv:2609.08372v2 Announce Type: replace
Abstract: A dense retriever encodes a question as one vector, but the question arrives one word at a time. We read 2,144 held-out headlines from Thu Vien Pha...
By Tran Minh Quan
The paper investigates how periodic subject changes—termed interruptions—affect the perceived novelty and coherence of text generated by base language models. By inserting a new subject every few hundred tokens into a stream that otherwise repeats, the authors find that judged surprise increases by 1.2 to 1.4 points and connection by 0.8 points compared to habituation alone. The study also reports that such interruptions do not produce integrated documents, and that the effect is robust across different models and evaluation protocols.
arXiv:2608. 19893v1 Announce Type: cross Abstract: Where does the novelty a base language model produces with no task come from, and what can an LLM judge of a long stream actually see?
By Roberto I. Ono Filho
What do a language model's hidden states say about the organization of a single text? From one forward pass, without training, we score every token position on two properties.
arXiv:2608. 09093v1 Announce Type: cross Abstract: How a document's arrangement is written down, its notation, is a training variable that no dataset card records.
By E. M. Freeburg