3 papers
cs.CL2026
How Far Do Auto-Interpretation Labels Generalize: A Controlled Study Across Languages, Scripts, and Rewordings
Sripad Karne
Sparse autoencoder (SAE) features are increasingly used to interpret language models, with auto-generated natural-language labels serving as the primary interface for understanding…
cs.LG2026
AgentOpt v0.1 Technical Report: Client-Side Optimization for LLM-Based Agent
Wenyue Hua, Sripad Karne, Qian Xie +4
AI agents are increasingly deployed in real-world applications, including systems such as Manus, OpenClaw, and coding agents. Existing research has primarily focused on server-side…
cs.CL2026
One Language, Two Scripts: Probing Script-Invariance in LLM Concept Representations
Sripad Karne
Do the features learned by Sparse Autoencoders (SAEs) represent abstract meaning, or are they tied to how text is written? We investigate this question using Serbian digraphia as a…