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Overall, the results show that SAE- and probe-based techniques often outperform basic prompt-based methods for steering LLM agents, although this advantage depends on the specific prompting strategy involved.
Automation metrics can mislead model selection, as LLMs that excel in direct output generation often fail to enhance the performance of weaker agents.