G3-F conditions TC offers on opponent wealth and game context, e.g., 0-value bluffs against bankrupt opponents

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• G2.5-FL initiates a trade challenge for a goose with zero money cards, offering 0-value blufffinding0.803
  In one trace, G2.5-FL depleted money through overbidding and launched a TC with no resources, failing to condition action on resource state.
• G2.5-FL repeatedly initiates TCs after depleting money through overbiddingfinding0.772
  failure to condition action choice on resource state
• Overbid frequency, self-bidding rate, bankrupt-initiation patterns, and context-dependent offer calibration are failure modes invisible to both static evaluations and aggregate rankings like Eloclaim0.758
  key claim about the benchmark's unique diagnostic value
• G3-F overbid rate=0.00%finding0.734
  Gemini 3 Flash never overbid
• G3-F completion tokens ~1,500 per call, G3.1-FL ~80 per callfinding0.730
  verbose reasoning not required for strong play
• G3-F wins 67.9% of 28 mixed games (vs three code agents)finding0.728
  Robust performance against algorithmic baselines.
• Behavioural traces surface recurring LLM failure modes including overbidding, self-bidding, bankrupt TC initiation, and weak opponent-state adaptation that never appear in code agents.claim0.723
  LLMs exhibit systematic errors that deterministic logic avoids.
• G3.1-FL generates ~14,800 completion tokens per gamefinding0.722
  Very efficient token usage with strong play.