Decision-Making

TL;DR for operators A clustering score is not a neutral verdict. It is a policy for deciding which mistakes count. Pasi Fränti’s review of external clustering measures separates that policy into three choices: how predicted clusters are matched to reference clusters, how similarity is scored, and how results are normalized.1 Those choices determine whether the metric rewards getting many individual records right, getting each cluster right regardless of size, or locating the correct cluster structure. ...

TL;DR for operators A smart agent can still be a bad decision-maker. That is the useful, slightly annoying lesson from LLMs are Greedy Agents: Effects of RL Fine-tuning on Decision-Making Abilities.1 The paper studies Gemma2 models acting in simple decision environments and finds that they often fail not because they cannot describe the right strategy, but because they do not reliably execute it. ...