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LLM-powered honeypots can trick even frontier models into longer interactions than rule-based systems, all while costing less to run.
LLM benchmark translations can be dramatically improved by test-time compute scaling, revealing a surprisingly cheap way to get more reliable multilingual evaluations.
Context files like AGENTS.md, intended to guide coding agents, often *hurt* performance and increase costs, challenging the common practice of using them.
