Interactive reasoning benchmark exposes LLM query efficiency gaps
474-game benchmark measures not just success rate but interaction efficiency and robustness under contextual perturbations—LLMs fail harder on counterfactual revision than baseline tasks.
June 2, 2026
Summary
Reveals whether your LLM can actually acquire evidence iteratively and adapt reasoning when assumptions break. Standard benchmarks hide interaction patterns that matter in production agentic systems.
Why it matters
Reveals whether your LLM can actually acquire evidence iteratively and adapt reasoning when assumptions break. Standard benchmarks hide interaction patterns that matter in production agentic systems.
Implementation verdict
Replaces single-shot eval frameworks with multi-turn reasoning assessment. Requires ability to run executable games and parse LLM query sequences. Not production-ready yet—preprint under review, no public benchmark release confirmed. Worth monitoring for agentic eval methodology.
Sources
- 1.multi-turn interactive framework for reasoning evaluation that treats reasoning as active evidence acquisition and belief updating
- 2.contextual perturbations cause moderate but consistent declines, whereas counterfactual revision and necessity judgment lead to much larger drops
- 3.benchmark of 474 executable games, each evaluated under five fixed configuration search spaces corresponding to five difficulty levels
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100+ sources compressed into one 4-minute read. Ranked, cited, implementation-ready.