23 citations · 25 across the 11 of their papers we have counts for
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cs.CR2026
Toward Open Weight Models Without Risks: Separating Public and Private Capabilities in LLMs
Charbel El Feghali, Arkil Patel, Nicholas Meade +3
Open-weight Large Language Models (LLMs) enable scientific progress and broad deployment. However, they make it difficult to control access to sensitive capabilities. Current pract…
cs.CL2026
Forecasting Downstream Performance of LLMs With Proxy Metrics
Arkil Patel, Siva Reddy, Marius Mosbach +1
Progress in language model development is often driven by comparative decisions: which architecture to adopt, which pretraining corpus to use, or which training recipe to apply. Ma…