arXiv:2607.07391v1 Announce Type: new Abstract: Mathematical reasoning benchmarks typically provide all facts needed to solve each problem, while interactive benchmarks often mix reasoning with tools, retrieval, and long-horizon dialogue. We introduce MIRA-Math, a benchmark for a narrower diagnostic capability: solving mathematical problems whose full latent state has a unique answer, but whose solver-facing view is missing exactly one necessary atomic fact. The solver must request the missing information in natural language under a strict budget and then integrate the returned fact into an ex
The AI industry is rapidly developing more advanced models, necessitating new benchmarks to diagnose increasingly granular capabilities like minimal information requesting and mathematical reasoning under budget constraints.
This benchmark directly addresses a key limitation in current mathematical reasoning evaluations, pushing AI development towards more efficient and human-like problem-solving strategies, which is crucial for real-world agentic applications.
The focus shifts from simply providing all facts to evaluating an AI's ability to identify and strategically request missing information, fundamentally altering how advanced reasoning capabilities are assessed and developed.
- · AI research labs developing agentic systems
- · Companies building AI-powered mathematical tools
- · Developers focused on efficient AI resource utilization
- · AI models that cannot effectively manage information requesting
- · Proponents of 'brute-force' data ingestion without strategic querying
- · Benchmarks that ignore information-seeking behaviors
MIRA-Math will accelerate the development of AI systems capable of more nuanced interaction and problem-solving, particularly in complex domains.
Improved performance on this benchmark could lead to AI agents requiring less upfront data for tasks, enhancing efficiency and reducing computational overhead.
The ability of AI to diagnose and request missing information could fundamentally alter human-AI collaboration paradigms, making AI partners more proactive and less reliant on explicit user prompting for every detail.
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Read at arXiv cs.AI