Diagnostic dataset forces models to request missing facts under budget, then solve—separating information-seeking from computation errors.
Summary
Reveals whether reasoning failures stem from not knowing what to ask versus failing downstream integration. Helps target model training on actual bottleneck—fact retrieval or calculation—rather than conflating both.
Why it matters
Reveals whether reasoning failures stem from not knowing what to ask versus failing downstream integration. Helps target model training on actual bottleneck—fact retrieval or calculation—rather than conflating both.
Implementation verdict
Not a replacement tool; a diagnostic benchmark. Requires building test harness around model's natural-language requests and fact validation. Worth running now if you're debugging reasoning accuracy on constrained-information problems, but immature for production deployment. Generators and verifiers are open.
Sources
Dev Signal
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