11 papers
Does a Language Server Save Tokens for Coding Agents? A Measurement Methodology and Preliminary Study
Pengcheng Xu
Coding agents spend most of their context budget on retrieval. Lexical retrieval (grep) is universal, instant, and zero-setup, but noisy: it cannot tell a definition from a call fr…
Representational Curvature Modulates Behavioral Uncertainty in Large Language Models
Jack King, Evelina Fedorenko, Eghbal A. Hosseini
In autoregressive large language models (LLMs), temporal straightening offers an account of how the next-token prediction objective shapes representations. Models learn to progress…
Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion
Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko +1
Neural networks exhibit a remarkable degree of representational convergence across diverse architectures, training objectives, and even data modalities. This convergence is predict…
Different types of syntactic agreement recruit the same units within large language models
Daria Kryvosheieva, Andrea de Varda, Evelina Fedorenko +1
Large language models (LLMs) can reliably distinguish grammatical from ungrammatical sentences, but how grammatical knowledge is represented within the models remains an open quest…
What does it mean to understand language?
Colton Casto, Anna Ivanova, Evelina Fedorenko +1
Language understanding entails not just extracting the surface-level meaning of the linguistic input, but constructing rich mental models of the situation it describes. Here we pro…
Modeling the language cortex with form-independent and enriched representations of sentence meaning reveals remarkable semantic abstractness
Shreya Saha, Shurui Li, Greta Tuckute +5
The human language system represents both linguistic forms and meanings, but the abstractness of the meaning representations remains debated. Here, we searched for abstract represe…