MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning

2026-07-08 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

MIRA-Math, a new benchmark published on 2026-07-08, evaluates large language models' (LLMs) ability to request minimal missing information for mathematical problems. Unlike typical benchmarks providing all facts, MIRA-Math presents 2,310 generated instances from 22 mathematical families, including algebra, probability, and signal processing, where exactly one atomic fact is missing. Solvers must request this fact in natural language under a strict budget, then integrate it to provide an exact final answer. A constrained LLM responder provides the fact only if the request matches. Experiments with frontier and smaller models reveal that request success and final-answer accuracy are separable capabilities, indicating models might ask correctly but fail computation, or fail to obtain the hint. The authors release generators, verifiers, and prompts for reproducible evaluation.

Key takeaway

For AI Scientists and Machine Learning Engineers developing mathematical reasoning models, MIRA-Math offers a critical diagnostic tool. You should use this benchmark to specifically evaluate your models' capacity to identify and request missing information, rather than solely assessing problem-solving with complete data. This approach helps pinpoint whether model failures stem from information retrieval gaps or computational errors, enabling more targeted improvements in your LLM architectures.

Key insights

MIRA-Math benchmarks LLMs on requesting and integrating single missing facts for mathematical problem-solving.

Principles

• Mathematical reasoning benchmarks often provide all facts.
• Request success and final-answer accuracy are separable.
• LLM responders can be constrained to offer specific facts.

Method

MIRA-Math problems require solvers to request a single missing atomic fact under budget, then integrate it into an exact final answer. A fixed LLM responder provides the fact if the request matches.

In practice

• Evaluate LLMs on minimal information requesting.
• Test integration of externally provided facts.
• Reproducibly assess mathematical reasoning.

Topics

• MIRA-Math
• Mathematical Reasoning
• LLM Benchmarking
• Information Requesting
• Problem Solving
• Artificial Intelligence

Best for: Research Scientist, AI Engineer, NLP Engineer, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.