
Ep 194: Closed-loop agent tests show naming the right Xiangqi move succeeds in only 13.9 percent…
Models & Agents Closed-loop agent tests show naming the right Xiangqi move succeeds in only 13.9 percent of trials once an engine defender responds. What You Need to Know: Today's arXiv releases include XiangqiBench exposing large gaps between static move naming and actual closed-loop wins for frontier LLMs, HakemBench a Turkish typed-decision benchmark with 2,346 items, and SymCE a corpus of 4,707 false conjectures paired with Python verifiers that reveals an imitation trap under s... Sources: arxiv.org AI Disclosure: This podcast is curated by Patrick but uses AI-generated voice synthesis for audio production. 🎬 Watch on YouTube: https://www.youtube.com/watch?v=DsvUncXUubg If this episode was useful, a rating or a short review on Apple Podcasts or Spotify is how the next listener finds the show — and following it in your app means the next episode is there when you are. 📝 Full show notes, transcript & sources: read the episode page 🌐 Part of the Nerra Network — explore every show at nerranetwork.com.
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