Do LLM failures in CATTLE TRADE reflect genuinely hard strategic problems or errors that novice humans also avoid?

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• Two heuristic code agents outperform most tested LLMs, and behavioural traces surface recurring LLM failure modes including overbidding, self-bidding, bankrupt TC initiation, and weak opponent-state adaptation.quote0.787
  Abstract sentence summarising performance and failures.
• 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.claim0.772
  LLMs exhibit systematic errors that deterministic logic avoids.
• Conditional logic already suffices where LLMs still fail, as code agents avoid systematic failuresclaim0.757
  contrast between rule-based and LLM reasoning
• Some failures may reflect prompt design rather than model limitations, but the underlying issue is one of reasoning rather than instruction-following.claim0.756
  Acknowledges the confound of not explicitly instructing models to track wealth, yet points to reasoning gaps given code agents avoid errors without prompts.
• 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 Eloclaim0.751
  key claim about the benchmark's unique diagnostic value
• Some failures may reflect prompt design rather than model limitations, though code agents avoid errors without promptsclaim0.751
  noted as a possible confound
• CATTLE TRADE is a step toward evaluating agentic competence under more realistic conditions of strategic interactionclaim0.749
  positioning of the benchmark
• Code agents operate on structured data with exact arithmetic, while LLMs must parse natural-language observations and track state across turns; some failures may partly reflect numerical parsing or working-memory limitationsclaim0.743
  discussion of potential confounds