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cs.CL2026
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…
cs.CL2024
Visual Grounding Helps Learn Word Meanings in Low-Data Regimes
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
Modern neural language models (LMs) are powerful tools for modeling human sentence production and comprehension, and their internal representations are remarkably well-aligned with…
cs.CL2024
Lexicon-Level Contrastive Visual-Grounding Improves Language Modeling
Chengxu Zhuang, Evelina Fedorenko, Jacob Andreas
Today's most accurate language models are trained on orders of magnitude more language data than human language learners receive - but with no supervision from other sensory modali…