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MacOS Harness is a Python tool that enables LLMs to control Mac systems via accessibility features and scripting, offering full control with minimal code.
This paper investigates when activation steering succeeds or fails for LLMs by analyzing early decoding dynamics. The authors introduce ASTEER, a large testbed of steered generations, and train a GBDT classifier to predict steering outcomes from early hidden states, enabling efficient steering strength search.
This paper investigates when rank-1 activation steering is effective and cost-efficient, proposing geometry-guided search and the concept of granularity to explain variability, and introduces the GRACE framework for efficient LLM control.