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

• URL: http://arxiv.org/abs/2607.07391v1
• Date: Wed, 08 Jul 2026 13:23:56 GMT
• Title: MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning
• Abstract summary: MIRA-Math is a benchmark for solving problems whose full latent state has a unique answer, but whose solver-facing view is missing exactly one necessary atomic fact.<n>The solver must request the missing information in natural language under a strict budget and then integrate the returned fact into an exact final answer.<n> MIRA-Math contains 2,310 generated instances from 22 typed mathematical families spanning algebra, probability, linear systems, discrete structures, signal processing, Markov chains, circuits, computation, and numerical boundary-value problems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 exact final answer. A fixed constrained LLM responder sees only the dataset-provided atomic fact and must either offer the quoted fact when the request matches it, or decline otherwise. Thus, instance generation, typed hint specifications, validation, and final-answer verification are deterministic, while request metrics are measured under a fixed LLM-mediated responder channel. MIRA-Math contains 2{,}310 generated instances from 22 typed mathematical families spanning algebra, probability, linear systems, discrete structures, signal processing, Markov chains, circuits, interpolation, and numerical boundary-value problems. Experiments across frontier and small models show that request success and final-answer accuracy are separable: models may ask for the right fact yet fail the downstream computation, or fail before obtaining the canonical hint. We release generators, verifiers, prompts, run metadata, and dataset documentation to support reproducible evaluation of minimal information requesting in mathematical reasoning.

Related papers

• SABER-Math: Automated Benchmark for Information Retrieval Evaluation in Mathematics [4.685475117723465]
  We introduce SABER-Math, the first fully automated benchmark for evaluating mathematical IR without expert annotation.<n>Starting from 283K high-school-level math problems with solutions, SABER-Math builds challenging reranking tasks in three steps.<n>We evaluate lexical retrievers, specialized mathematical retrieval systems, and recent embedding models.
  arXiv  Detail & Related papers  (2026-06-29T07:32:50Z)
• Reaching Beyond the Mode: RL for Distributional Reasoning in Language Models [78.68818219506313]
  This paper describes a multi-answer reinforcement learning approach for training LMs to perform distributional reasoning over multiple answers during inference.<n>Across question-answering, medical diagnostic, and coding benchmarks, we observe improved diversity, coverage, and set-level calibration scores compared to single answer trained baselines.
  arXiv  Detail & Related papers  (2026-03-25T22:20:25Z)
• HorizonMath: Measuring AI Progress Toward Mathematical Discovery with Automatic Verification [54.06301039725887]
  We introduce HorizonMath, a benchmark of over 100 predominantly unsolved problems spanning 8 domains in computational and applied mathematics.<n>Our benchmark targets a class of problems where discovery is hard, requiring meaningful mathematical insight, but verification is computationally efficient and simple.
  arXiv  Detail & Related papers  (2026-03-16T17:59:53Z)
• SciML Agents: Write the Solver, Not the Solution [69.5021018644143]
  We introduce two new datasets: a diagnostic dataset of adversarial "misleading" problems; and a large-scale benchmark of 1,000 diverse ODE tasks.<n>We evaluate open- and closed-source LLM models along two axes: (i) unguided versus guided prompting with domain-specific knowledge; and (ii) off-the-shelf versus fine-tuned variants.<n>Preliminary results indicate that careful prompting and fine-tuning can yield a specialized LLM agent capable of reliably solving simple ODE problems.
  arXiv  Detail & Related papers  (2025-09-12T02:53:57Z)
• Self-Questioning Language Models [58.73276539661649]
  We propose an asymmetric self-play framework where a proposer is given the topic and generates a question for a solver.<n>Both the proposer and solver are trained via reinforcement learning.<n>We study this asymmetric self-play framework on three benchmarks: three-digit multiplication, algebra problems from the OMEGA benchmark, and programming problems from Codeforces.
  arXiv  Detail & Related papers  (2025-08-05T17:51:33Z)
• Solving Inequality Proofs with Large Language Models [42.667163027148916]
  Inequality proving is crucial across diverse scientific and mathematical fields.<n>This makes it a demanding frontier for large language models (LLMs)<n>We release IneqMath, an expert-curated dataset of Olympiad-level inequalities.
  arXiv  Detail & Related papers  (2025-06-09T16:43:38Z)
• Let's Verify Math Questions Step by Step [29.69769942300042]
  MathQ-Verify is a novel pipeline designed to rigorously filter ill-posed or under-specified math problems.<n>MathQ-Verify first performs format-level validation to remove redundant instructions.<n>It then formalizes each question, decomposes it into atomic conditions, and verifies them against mathematical definitions.
  arXiv  Detail & Related papers  (2025-05-20T04:07:29Z)
• Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models [86.45058529521258]
  OlymMATH is a novel Olympiad-level mathematical benchmark designed to rigorously test the complex reasoning capabilities of LLMs.<n>OlymMATH features 200 meticulously curated problems, each manually verified and available in parallel English and Chinese versions.
  arXiv  Detail & Related papers  (2025-03-27T11:20:17Z)
• HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics [1.5716764919736026]
  We introduce HARDMath, a dataset featuring challenging applied mathematics problems that require analytical approximation techniques.<n>Our framework auto-generates a large number of problems with solutions validated against numerical ground truths.<n>We evaluate both open- and closed-source LLMs on HARDMath-mini, a sub-sampled test set of 366 problems, as well as on 40 word problems formulated in applied science contexts.
  arXiv  Detail & Related papers  (2024-10-13T20:09:41Z)
• GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers [68.77382332826167]
  Large language models (LLMs) have achieved impressive performance across various mathematical reasoning benchmarks. One essential and frequently occurring evidence is that when the math questions are slightly changed, LLMs can behave incorrectly. This motivates us to evaluate the robustness of LLMs' math reasoning capability by testing a wide range of question variations.
  arXiv  Detail & Related papers  (2024-02-29T15:26:14Z)
• CHAMP: A Competition-level Dataset for Fine-Grained Analyses of LLMs' Mathematical Reasoning Capabilities [25.857946070979576]
  Concept and Hint-Annotated Math Problems (CHAMP) consists of high school math competition problems annotated with concepts. This benchmark is difficult, with the best model only scoring 58.1% in standard settings. We find that models often arrive at the correct final answer through wrong reasoning steps.
  arXiv  Detail & Related papers  (2024-01-13T03:18:16Z)
• Test Set Sizing Via Random Matrix Theory [91.3755431537592]
  This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split for a simple linear regression. It defines "ideal" as satisfying the integrity metric, i.e. the empirical model error is the actual measurement noise. This paper is the first to solve for the training and test size for any model in a way that is truly optimal.
  arXiv  Detail & Related papers  (2021-12-11T13:18:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.