collaborators

6 papers

cs.CL2026

Distinct dynamics of conceptual and referential disruptions in human reading and large language model processing

Rui He, Nihal Altay, Wolfram Hinzen

Linguistic meaning is grounded in conceptual content, from which reference to particular entities emerges as words enter discourse. To examine the processing dynamics associated wi…

cs.CL2026

The Changing Geometry of Grammar: Dimensionality and Neighborhood Reorganization across Transformer Layers

Samuele Vallisa, Federico Ravenda, Claudio Palominos +5

Transformer representations describe trajectories through high-dimensional vector spaces, which are shaped dynamically as tokens incorporate relational context across layers. Such…

cs.CL2026

Activation-Guided Neuron Intervention to Induce Alzheimer's-Related Computational Language Phenotypes in a Large Language Model

Rui He, Ercong Nie, Hong Jiang +3

Changes in spontaneous speech provide an early signal of cognitive dysfunction in Alzheimer's disease (AD) that large language models (LLMs) can detect. However, detection alone ca…

cs.CV2026

Linear-Time Global Visual Modeling without Explicit Attention

Ruize He, Dongchen Han, Gao Huang

Existing research largely attributes the global sequence modeling capability of Transformers to the explicit computation of attention weights, a process that inherently incurs quad…

cs.CL2026

The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

Rui He, Claudio Palominos, Samuele Vallisa +16

Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cro…

cs.CL2026

Cross-lingual brain-language model alignment is robust but challenges hierarchical and computational accounts

Ni Yang, Rui He, Philipp Homan +3

Brain-language model alignment is often interpreted as evidence that transformer models implement computations similar to those of the human brain. This assumes that neural predict…