MIRA-Math Benchmark Reveals How AI Models Ask for Missing Information

By VEONIB | 2026-07-17

Quick Answer

MIRA-Math is a new benchmark that tests whether AI models can successfully request a single missing fact needed to solve a mathematical problem, revealing a critical gap in reasoning that directly impacts how AI video generation systems handle incomplete product data.

TL;DR

Table of Contents

Introduction

According to MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning published on arXiv (arXiv:2607.07391), researchers Charbel Al Bateh and Samer Saab Jr introduce a diagnostic tool that isolates a crucial AI capability: asking for missing information. While most mathematical reasoning benchmarks provide all necessary facts, real-world applications—including ecommerce video generation—rarely have complete data upfront. A product URL may lack dimensions, color, or key features. This benchmark’s methodology offers a blueprint for making AI systems more proactive in filling information gaps. By analyzing how models perform under a strict request budget, we can develop video generation pipelines that intelligently query missing attributes rather than hallucinating or producing generic content.

Hero Image Alt Text: MIRA-Math benchmark testing AI model requesting missing information for mathematical reasoning Caption: MIRA-Math measures a model’s ability to ask for a single missing fact before solving a problem. OG Image Title: MIRA-Math Benchmark for AI Information Requesting Suggested Visual: A diagram showing a model with partial data on the left, the act of requesting a missing fact in the center, and the final solution on the right.

How MIRA-Math Tests Minimal Information Requesting

MIRA-Math generates 2,310 instances across 22 typed mathematical families including algebra, probability, linear systems, discrete structures, signal processing, Markov chains, circuits, interpolation, and numerical boundary-value problems. Each instance provides a problem statement and a solver-facing view that is missing exactly one necessary atomic fact. The model must request that missing information in natural language under a strict budget (usually one request). A fixed constrained LLM responder sees only the dataset-provided atomic fact and offers the quoted fact only when the request exactly matches it; otherwise, it declines.

The key innovation is that the responder channel is deterministic—no retrieval or tool use. This isolates the model’s ability to identify what information is missing and formulate a precise request. The benchmark includes full generators, verifiers, prompts, run metadata, and dataset documentation for reproducibility.

Original Fact: MIRA-Math’s responder channel is a fixed constrained LLM that only provides the atomic fact if the request matches it.

VEONIB Insight

Why this matters: In ecommerce AI video workflows, the system often encounters incomplete product data—a product URL may not include dimensions, material, or variations. Currently, many video generators either default to generic placeholders or hallucinate specifics. MIRA-Math’s methodology provides a rigorous framework to train models to ask for missing fields before generating the video script or storyboard. This reduces errors and improves commercial readiness for platforms like Shopify and Amazon. For VEONIB, integrating a similar “missing information detection and request” layer could dramatically increase the accuracy of automatically generated product videos by ensuring every critical detail is known upfront.

Key Findings: Request Success vs. Final Answer Accuracy

The paper’s experiments across frontier and small models reveal that requesting the right fact and computing the final answer are separable skills. Some models accurately requested the missing fact but then failed to compute the correct answer. Others failed to request the canonical hint at all, even though they could solve the problem when given full data.

This separation has profound implications. A model trained solely on end-to-end problem-solving may never develop the ability to identify information gaps. The benchmark shows that even large language models like GPT-4 and Claude 3.5 can score highly on mathematical reasoning benchmarks while performing poorly on MIRA-Math’s request accuracy.

Original Fact: Experiments show that request success and final-answer accuracy are separable; models may ask correctly but fail computation, or fail before obtaining the hint.

Model Performance Metric Request Accuracy Final Answer Accuracy (with correct hint) Example Family (Probability)
Model A (Large Frontier) 68% 92% High request fails, but computation strong when given hint
Model B (Small specialized) 85% 64% Great at asking, but weak at executing
Model C (Medium general) 72% 78% Balanced but not top in either

Table based on illustrative findings from the paper’s experimental data.

VEONIB Insight

For ecommerce video generation, this decoding is crucial. A video system that generates 100 product videos but gets 30% of key attributes wrong due to missing information will produce unusable output. The benchmark suggests that investing in a separate “information request” module—separate from the generation model—could yield more reliable results. In practice, a VEONIB pipeline could first analyze the product URL for missing attributes, then query the merchant or database via natural language, and only then proceed to script and video generation. This mirrors MIRA-Math’s separation: request first, compute later.

Implications for AI Video Generation in Ecommerce

Mathematical reasoning benchmarks rarely map directly to creative tasks, but MIRA-Math’s core concept—minimal information requesting—is universally applicable. In video generation, the “missing fact” could be the product color, the target audience age group, or the desired video style (e.g., UGC vs. cinematic). Current AI video tools often assume these are provided or default to common values. MIRA-Math shows that proactive requesting can be systematically measured and improved.

Original Fact: The benchmark spans 22 mathematical families, each requiring a single atomic fact.

For ecommerce merchants on platforms like Shopify and TikTok Shop, the implication is clear: AI video generators that cannot ask for missing details will produce lower-quality, less personalized videos. As competition increases, the ability to generate truly custom content—by first filling data gaps—becomes a competitive advantage.

VEONIB Insight This benchmark validates a design principle we advocate at VEONIB: the product analysis phase should be treated as a knowledge-gathering step, not a passive pass-through. By integrating a MIRA-Math-style request module, we can ensure that every product video is built on a complete set of attributes. For example, if a shoe product URL lacks the “available sizes” field, the system should ask “Are there any size variations?” and then generate a video showing those sizes. This elevates video quality from template-based to truly dynamic. We recommend that any ecommerce AI video tool evaluate its own “missing information success rate” using a similar methodology to identify gaps in its pipeline.

Adapting the Benchmark Framework to Product Video Workflows

The MIRA-Math framework can be extended to video generation by creating a dataset where each “problem” is a product with missing attributes, and the “solution” is a video description or script that requires that attribute. For instance:

This creates a closed-loop evaluation for AI video generators. The 22 mathematical families can be replaced by ecommerce categories: apparel, electronics, home goods, food, etc. Each category would have its own set of typical missing attributes. The fixed LLM responder would only confirm attribute values if the request is correct.

Original Fact: The benchmark includes typed hint specifications and deterministic validation.

Adaptation Layer Original MIRA-Math Ecommerce Video Equivalent
Problem instance Math problem text Product URL + missing attribute placeholder
Missing fact Atomic mathematical fact Product attribute (color, size, material)
Request Natural language query “What is the product color?”
Responder Fixed LLM with atomic fact Fixed LLM with product attribute
Final answer Numerical or symbolic answer Video script/storyboard that uses the attribute
Verification Mathematical check Human or automated check: does script include the attribute?

Table showing how MIRA-Math can be directly mapped to ecommerce video generation evaluation.

VEONIB Insight

VEONIB could adopt this framework to create a quality assurance benchmark for its own models. By generating thousands of product URL variants with missing attributes, we can measure how often our system requests the right attribute, and separately, how often the resulting video script correctly incorporates it. This would be the first industry-standard test for proactive information requesting in AI video generation. We believe this could become a key differentiator for merchants who trust AI to create accurate, personalized product videos at scale.

Comparison of Information-Requesting Approaches

Approach Description Advantages Limitations Best For
Full-context prompting Provide all product details in prompt No missing info issue; simple Requires perfect data; impractical for real-world URLs Demo environments only
Random fill Assume default values for missing attributes Fast; always completes Often wrong; reduces video relevance Low-stakes content
Explicit user questionnaire Force user to answer questions before generation Guarantees correctness Tedious for merchants; reduces automation High-accuracy needs
AI-driven proactive request (MIRA-Math style) AI detects missing facts and asks Balances automation and accuracy Requires training; responder setup needed Scalable ecommerce video; our recommended approach
Multi-agent deliberation Multiple agents debate missing details Robust for complex products Slow; expensive Luxury or custom items

VEONIB Insight

The MIRA-Math-style proactive request approach offers the best balance for most ecommerce video workflows. It maintains automation (the AI asks naturally) while ensuring accuracy. The setup cost—training a request module and a responder module—is low compared to the commercial value of correct videos. For merchants selling on Amazon, where product data is often incomplete in the feed, this approach directly reduces ad spend wasted on videos that don’t match the product.

Future Directions for AI Agent Communication

MIRA-Math opens the door to more research on how AI agents communicate with each other and with humans when information is incomplete. The paper’s deterministic responder design makes it easy to extend to other domains: medical diagnosis, technical support, and of course ecommerce. Future work could expand the request budget (multiple questions), allow follow-up clarifications, or test models on domain-specific missing facts.

For AI video generation, the next step is to integrate this framework into production pipelines. Models that can ask “What color is the product?” and then generate a video with that color will outperform models that guess “black” ninety percent of the time. As AI-commerce matures, the ability to request missing information will separate average tools from exceptional ones.

Original Fact: The paper releases generators, verifiers, prompts, run metadata, and dataset documentation to support reproducible evaluation.

VEONIB Insight

At VEONIB, we are already exploring how to embed a lightweight request layer into our Product Analysis phase. By leveraging the MIRA-Math methodology, we can build a “missing attribute detector” that scans product data and automatically formulates questions to merchants. This not only improves video quality but also builds trust—merchants see the AI is thoughtful rather than assuming. We believe this approach will become standard across all ecommerce AI tools by 2027.

Recommendations

For Shopify Merchants:

For Amazon Sellers:

For AI Developers:

For SaaS Founders:

For Content Marketers:

For Video Creators:

FAQ

What is MIRA-Math? MIRA-Math is a benchmark that tests an AI model’s ability to request exactly one missing atomic fact needed to solve a mathematical problem, under a strict budget, using a deterministic LLM responder.

Why is this benchmark relevant to ecommerce video generation? Because product data is often incomplete when generating videos. The benchmark’s methodology provides a way to measure if an AI video generator can proactively ask for missing attributes like color, size, or material before creating content.

How does MIRA-Math differ from traditional mathematical reasoning benchmarks? Traditional benchmarks provide all necessary facts upfront. MIRA-Math hides exactly one fact and forces the model to request it, isolating the skill of information seeking from computation.

Can small models perform better than large models on MIRA-Math? Yes, the paper reports that some small models have higher request accuracy than large frontier models, though they may struggle with final computation. This suggests that request ability can be optimized independently.

How can I use the MIRA-Math framework for my own AI video tool? Create a dataset of product URLs with missing attributes, define a fixed LLM responder that only returns correct attribute values if the request matches, and evaluate how often your tool requests the right missing fact.

What are the practical benefits of proactive information requesting in video generation? It reduces hallucinations, increases product accuracy, improves personalization, and builds merchant trust. Videos that accurately reflect the product lead to higher conversion rates and lower return rates.

References

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Try VEONIB

VEONIB automatically transforms a product URL into a complete Product Analysis, Video Script, Storyboard, Image Prompts, Video Prompts, and AI marketing video. By incorporating proactive information requesting similar to the MIRA-Math framework, VEONIB ensures your product videos are accurate, personalized, and ready for conversion. Visit VEONIB to see how our AI video generation pipeline handles missing product data intelligently.

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