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MADQA is not an embedding-model benchmark in isolation. It is a multimodal, agentic document-question-answering benchmark: systems search a collection of heterogeneous PDFs, inspect textual and visual content, combine evidence from pages, and produce answers. Its score therefore reflects the behavior of an entire document-QA stack—agent planning, retrieval or file-search configuration, document interpretation, and answer generation—not just the quality of a vector representation.
That distinction matters when you use a public result to choose technology. MADQA can show how a tested system handles a particular document collection and question set, including the trade-off between answer quality and search effort. It cannot, by itself, predict results on your corpus, your users’ questions, your access controls, or your latency and cost limits.
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What does MADQA measure?
MADQA evaluates agents that answer questions over a collection of 800 heterogeneous PDF documents. The benchmark contains 2,250 human-authored questions, according to the 2026 ICML paper by Łukasz Borchmann and colleagues and the project repository. The documents are not treated as a flat text database. A system may need to locate relevant pages, read prose and tables, interpret visual material, follow evidence across pages, and decide when it has searched enough.
That makes MADQA a test of document-QA behavior under realistic search conditions rather than a laboratory measurement of one embedding function. The repository describes a workflow in which a system retrieves pages, reasons over textual and visual content, and aggregates evidence across pages. Different implementations can use different retrieval mechanisms, models, and agent policies while operating within the benchmark’s evaluation harness.
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The published answer and evidence measures
The evaluation code lists several measures rather than one universal score:
| Measure | What it is intended to capture | How to read it |
|---|---|---|
| Accuracy | Whether the submitted answer is correct under the benchmark’s answer evaluation. | A top-line answer result, but not a diagnosis of retrieval, planning, or evidence quality. |
| ANLS* | A benchmark-listed answer-similarity measure for answers where surface wording can vary. | Useful alongside exact correctness; do not assume it isolates any single system component. |
| Semantic Accuracy | Whether the answer is judged semantically correct. | Helps account for equivalent wording, while still depending on the benchmark’s evaluator. |
| Page F1 | Overlap between pages retrieved by the system and the pages identified as relevant. | Evidence-location information that can reveal retrieval problems hidden by a plausible final answer. |
| Kuiper | A further evaluation statistic listed by the project’s harness. | Report its value with the exact script and configuration; a bare number is not portable between setups. |
The paper also describes an accuracy-effort trade-off protocol. A system that gets answers right by making many searches is behaving differently from one that reaches the same accuracy with fewer searches. Both dimensions are relevant when an agent is deployed with tool-call limits, latency targets, or usage charges.
Is MADQA an embedding benchmark?
No. Embeddings may be one part of a MADQA system, but the benchmark does not hold every other component constant and output a pure embedding-quality ranking. A result can be affected by:
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- the agent’s planning policy, including whether it searches broadly, iteratively, or redundantly;
- the retriever, index, chunking, ranking, and file-search settings;
- how pages, tables, figures, and other visual elements are represented;
- the mechanism used to aggregate evidence from multiple pages;
- the answer-generation and answer-scoring configuration; and
- limits on tool calls or search rounds.
The repository includes baselines such as BM25-based search and managed Google and OpenAI file-search approaches. Their inclusion illustrates the point: MADQA compares complete approaches in a document-QA workflow, not just vectors produced by interchangeable embedding models.
When a result page presents a model together with a retriever, treat that as a model-plus-retriever configuration. For example, Mixedbread’s vendor-maintained MADQA page reports configurations and both accuracy and tool-call counts. Those numbers are useful evidence about the named setup, but they are not an independent ranking of embedding models.
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What does a MADQA score tell me about retrieval?
A score tells you how the evaluated system performed on the benchmark’s questions under the stated conditions. It can provide evidence that the system found useful material and transformed it into acceptable answers. Page F1 adds a more direct view of whether relevant pages were retrieved. Search-effort reporting can show whether the system needed repeated calls to achieve that result.
It does not tell you that the embedding layer alone is responsible for success or failure. A low answer score might result from missed pages, poor ranking, an agent that stopped too early, an agent that failed to read a figure, weak cross-page reasoning, or an answer generator that misunderstood correct evidence. A high score can likewise come from strong planning or answer generation even when the vector search itself is not exceptional.
Read answer and page metrics together
Use the measures as a diagnostic pattern rather than selecting the largest single number:
- High answer accuracy and high Page F1: the system generally located the right evidence and used it effectively.
- High answer accuracy and low Page F1: inspect whether the answer can be produced from alternate pages, whether the page labels are evaluated differently, or whether answer generation is compensating for noisy retrieval.
- Low answer accuracy and high Page F1: retrieval may be adequate while multimodal interpretation, reasoning, or answer generation is failing.
- Similar accuracy with very different effort: the lower-effort system may be preferable operationally, but only after confirming that tool-call counts were defined and measured the same way.
These patterns are hypotheses for investigation, not automatic diagnoses. They become meaningful only when the benchmark split, evaluator, and system configuration are held constant.
What did the 2026 MADQA paper find?
Borchmann and colleagues report that the best agents can match human searchers in raw accuracy on the benchmark. That result is narrower than saying agents search like people or reason like people. The paper states that the agents succeed on largely different questions from the humans and rely on brute-force search to compensate for weak strategic planning.
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The paper frames the issue this way: do these agents demonstrate genuine strategic reasoning, or merely stochastic trial-and-error search?
This is the authors’ framing in the paper abstract. It is not evidence that every agent or every production retrieval system behaves randomly; it identifies the behavior the benchmark is designed to examine.
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These findings make MADQA valuable for studying planning and search behavior. They also warn against reading a human-level accuracy number as proof of human-level reasoning, efficient retrieval, or generalization to a private knowledge base.
How does MADQA measure search effort?
The benchmark’s accuracy-effort protocol considers how much searching an agent performs in addition to whether it answers correctly. The project materials and vendor reports may express effort as tool calls or search rounds. Those terms are not automatically interchangeable: an outer API request can contain several internal retrieval actions, and an implementation may count calls differently from another implementation.
What to record in a fair comparison
- the exact definition of a tool call, round, or search action;
- whether internal calls are counted or only outer API calls;
- the agent, model, retriever, and multimodal document-handling configuration;
- the benchmark split and evaluation-script version;
- accuracy, semantic scoring, and page-level measures;
- the date of the run and any limits on calls or tokens; and
- latency and monetary cost if the comparison is intended to guide deployment.
Mixedbread’s page says it used public benchmark scripts and labels its MADQA results as tested in May 2026. It also describes how its agent tool calls are counted. That makes the page a dated, reproducible vendor example, not a neutral leaderboard. A comparison is only meaningful when competing systems publish equivalent definitions and conditions.
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Can I use benchmark rankings to choose an embedding model for my data?
Not by themselves. A MADQA result can narrow the field of complete systems worth testing, but it cannot establish which embedding model will work best for your organization’s documents and queries. The benchmark’s PDFs, question distribution, page structure, and evaluation objectives may differ substantially from your workload.
Build a held-out evaluation set that represents the decisions your users actually make. Include difficult cases such as long documents, tables, scanned pages, diagrams, cross-page questions, ambiguous terminology, and questions whose answer is absent. Keep a separate test set that is not used to tune prompts, chunking, retrieval thresholds, or agent policies.
A practical evaluation procedure
- Define the task. Specify what counts as a correct answer, whether citations or page references are required, and which users and permissions the system must support.
- Assemble representative documents. Sample the formats, languages, visual layouts, document lengths, update rates, and access-control rules found in production.
- Create labeled questions. Record accepted answers and relevant pages or passages. Include unanswerable and multi-hop questions instead of measuring only easy lookups.
- Freeze comparable conditions. Use the same question set, document snapshot, evaluator, timeout, call budget, and reporting conventions for every system.
- Test complete configurations. Change one component at a time where possible—embedding model, sparse retriever, reranker, chunking, agent policy, or answer model—so a score change has an interpretable cause.
- Report more than accuracy. Include page or passage retrieval quality, semantic answer quality, failure categories, tool calls, latency, and cost.
- Check operational constraints. Measure performance with real concurrency, permissions, update pipelines, and the hardware or hosted service you will use.
- Validate on a fresh holdout. Confirm the apparent winner on questions and documents that were not used during tuning.
This process turns a public benchmark into one input to a decision rather than a substitute for local evidence.
What MADQA cannot predict about a production system
The benchmark does not, by itself, establish performance for a particular organization’s corpus, query mix, latency target, cost ceiling, access-control model, or update workflow. Those are extrapolations beyond the benchmark’s defined scope.
Corpus mismatch
MADQA uses 800 heterogeneous PDFs and 2,250 authored questions. Your corpus may be dominated by HTML, spreadsheets, tickets, source code, handwritten scans, or highly repetitive policy documents. A retriever that performs well on visually rich PDFs may not be the best choice for short, structured records.
Question mismatch
Users may ask for exact fields, comparisons across versions, calculations, citations, or answers that require refusing when evidence is missing. Measure those behaviors explicitly; a benchmark average can hide a failure mode that matters most to your users.
Operational mismatch
Public results may omit your indexing schedule, permission checks, regional hosting requirements, concurrency, observability, and budget. A system with strong benchmark accuracy can still be unsuitable if it exceeds the allowed latency or performs searches that are too expensive at your request volume.
How to compare two published MADQA results responsibly
First verify that both results use the same benchmark split and evaluation script. Then compare the full configuration, not just the model name. A useful report should identify the agent, answer model, retriever or file-search service, document and image handling, call-count definition, metrics, test date, and whether the result comes from an independent paper or a vendor evaluation.
| Comparison question | Why it matters |
|---|---|
| Are the documents, questions, and split identical? | A different split or corpus can change difficulty more than a model change. |
| Is the evaluator and script version identical? | Metric implementations and answer normalization affect reported values. |
| Is the whole model-plus-retriever setup named? | Embedding, ranking, planning, and generation all contribute to the result. |
| Are effort counts defined the same way? | One system’s internal rounds may be another’s single outer call. |
| Is the source independent or vendor-maintained? | Provenance changes how strongly you should generalize a result. |
| Are latency and cost reported? | Accuracy alone cannot select a production system. |
If any of these details are missing, label the comparison as incomplete instead of presenting the numbers as a universal ranking.
What a MADQA result is good for
Use MADQA to study whether a complete agentic document-QA system can navigate heterogeneous documents, recover evidence across pages, and balance answer quality against search effort. It is especially useful for exposing the difference between raw accuracy and strategic behavior: an agent may reach a competitive answer rate through repeated, inefficient searches and may solve a different subset of questions from human searchers.
Use your own representative evaluation to decide whether that behavior is acceptable for your workload. The right embedding or retrieval choice is the one that remains accurate, explainable, affordable, and responsive under your documents, permissions, and question distribution—not necessarily the system with the highest public MADQA number.
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