Evaluate a multimodal AI model against the tasks and risks it will actually face, and report results separately for text, images, video, and robot control. Add tests for visual and temporal grounding, unfamiliar situations, reliability, and safety; document the model, test conditions, and scoring rules. A benchmark score describes performance on a defined task distribution—not broad capability or a guarantee of safe deployment.
Start by defining what the evaluation is meant to prove
Before choosing a benchmark, write an evaluation contract: what the model is expected to do, for whom, under what conditions, and what the consequences of failure would be. This is a practical framework, not a universal formal standard. It helps keep comparisons tied to deployment needs rather than to whichever score is easiest to obtain.
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- Use case and users: Define the intended application and who will rely on the result.
- Model and system scope: Record the model version and whether you are evaluating the model alone, a larger application, or a robot policy connected to an embodiment.
- Task distribution: Specify the inputs, representative cases, expected outputs, and test splits. Include routine cases as well as difficult or ambiguous ones relevant to the application.
- Scoring and failure costs: Set correctness criteria and decide how to count incomplete answers, unsupported claims, unsafe actions, and requests for clarification before comparing models.
Choose measures to fit the task. For example, answer correctness is relevant to a text question, while a robot task may require both successful completion and safe execution. Do not treat different measures as interchangeable simply because they can be converted into numbers.
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Report text, image, and video results separately
A single multimodal label does not tell you which input types or tasks a model handles well. Use modality-appropriate tests, retain the per-task results, and explain what each score measures. The ITU-T’s 2025 foundation-model materials include assessment criteria and multimodal evaluation; the catalog identifies F.748.77 for general foundation-model assessment criteria, F.748.44 for benchmark criteria, and F.748.74 for multimodal foundation-model evaluation requirements. Its materials describe criteria including functionality, accuracy, reliability, security, interactivity, and applicability. They are standards-oriented references, not a prescription that one metric fits every task.
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Text
For text tasks, define what counts as a correct and useful answer for the intended use. Report the scoring method and test distribution. The ITU-T catalog gives word error rate (WER) and BLEU as examples of metrics where applicable; those examples are not universal measures of text capability. Select a metric that matches the output and task rather than using a familiar score by default.
Images
Test whether an answer is grounded in the supplied image, not merely plausible in isolation. Use cases where the relevant evidence can be identified in the image, and score whether the answer reflects that evidence. Keep visual-grounding results distinct from text-only correctness: a model may produce fluent language without correctly interpreting the image.
Video
Include temporal questions and event ordering when they matter to the intended use. Score whether the response identifies the relevant events and their order from the supplied clip, rather than inferring a likely sequence from context alone. Treat the chosen video scoring method as your evaluation protocol: the ITU-T overview does not prescribe a universal video metric set. NIST’s AITE overview identifies video among its evaluation themes, but it does not establish a single end-to-end protocol for evaluating every video-capable model.
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Evaluate robot control as a policy–embodiment system
Robot performance is not simply another modality score. A policy maps observations and instructions to actions; an embodiment provides observations and executes those actions. The policy and embodiment must be compatible. Action spaces, sensors, and execution conditions can differ, so results from incompatible setups should not be presented as directly comparable.
Inspect Robots is an open-source framework for evaluating policies with real-robot or simulation embodiments. Its documented features include pre-rollout compatibility checks and reproducible logs. Use an evaluation setup that makes the policy, embodiment, task conditions, and outcomes inspectable. State plainly whether a result came from simulation or a physical robot and identify the embodiment and task conditions. A model’s reasoning output is not the same thing as closed-loop execution by an embodied policy.
Score task completion alongside execution behavior. A completed task does not by itself establish that the action sequence was safe, and simulated success alone does not establish physical safety.
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Probe generalization beyond familiar scenes
Strong results on familiar objects and layouts may not carry over to a changed scene. Test shifts that matter to the deployment, and show which kinds of shift cause performance to fall. MESA-Bench provides generalization suites for tabletop manipulation that separate evaluation across:
- Unseen spatial configurations
- Unseen object categories
- Unseen object instances
- Composite tasks that combine subtasks
This separation is useful because a model can handle a new object while struggling with a new arrangement, or complete individual steps while failing when they are composed. Report the relevant shift type rather than merging all unfamiliar cases into an unexplained “generalization” score.
Test safety behavior as well as task success
For physical action, completion is only one part of the evaluation. Add cases that test whether the system responds appropriately when an action violates a constraint, a critical condition calls for intervention, a task is infeasible or out of distribution, or instructions and scene state are unclear. Google DeepMind’s ASIMOV-Agentic benchmark description covers refusal, protective interventions, out-of-distribution shielding, and escalation to a human. These behaviors complement task-success measures; they do not replace them.
Set the relevant safety expectations before testing. For example, decide which requests should be refused and what kinds of ambiguity should prompt a request for human help. Report the observed behavior and test conditions, rather than inferring physical safety from a visual question-answering result or a simulated task score.
Make model comparisons reproducible
A score is hard to interpret without the conditions that produced it. Record enough detail for another evaluator to understand what was tested and how the result was obtained:
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- Prompts or robot instructions
- Dataset or task splits and the tested cases
- Scoring rules and any human-judgment procedure
- Environment, including whether robotics runs used simulation or a physical robot
- Robot embodiment and policy–embodiment compatibility constraints
- Run logs and relevant task conditions
NIST describes AITE as a sequestered evaluation testbed spanning meaningful tasks, datasets, modalities, and domains. Its overview names themes including video and NLP; it should not be mistaken for a single published protocol that settles every evaluation choice for this use case. Inspect Robots’ compatibility checks and reproducible logs are particularly relevant to recording robotics runs.
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Use a scorecard, not a single leaderboard position
When comparing models, present the dimensions that matter to the deployment side by side. The following scorecard is an editorial synthesis of evaluation dimensions reflected in ITU-T, Inspect Robots, ASIMOV-Agentic, and MESA-Bench; it is not a quoted standard.
| Axis | What to report |
|---|---|
| Task performance | Correctness or task completion on the defined test set, with the task and scoring rule identified. |
| Grounding | Whether outputs reflect the supplied text, image, or video evidence; identify the evidence type tested. |
| Generalization | Performance on unfamiliar spatial layouts, object categories or instances, and task compositions, as relevant to the application. |
| Reliability | Variation across repeated runs or changed inputs, where measured, with the repetition conditions stated. |
| Safety | For robotics, observed refusal, intervention, out-of-distribution handling, and escalation behavior on the defined safety cases. |
| Execution conditions | Model and environment versions, simulation or physical-robot status, embodiment, and compatibility constraints. |
Keep results for text, image, video, and robotics visible even if you also calculate an aggregate. If an aggregate is useful, explain how its components are weighted and what it conceals. No universal threshold or single benchmark suite is established for every team; select the tests according to the deployment tasks and their risks.
Choose evaluation resources by the question you need answered
These named resources address different parts of the problem; none should be treated as a complete substitute for a deployment-specific evaluation.
| Resource | What it covers | Useful when |
|---|---|---|
| ITU-T foundation-model assessment materials | General criteria such as functionality, accuracy, reliability, security, interactivity, and applicability, plus multimodal evaluation materials. | You need a standards-oriented reference for assessment dimensions and benchmark considerations. |
| Inspect Robots | Policy evaluation with real-robot or simulation embodiments, compatibility checks, and reproducible logs. | You need to evaluate robot policies together with compatible embodiments and record rollouts. |
| RoboBench | An embodied-brain benchmark spanning instruction understanding, perception reasoning, planning, affordance prediction, and failure analysis. | You want a benchmark-specific view of embodied-model tasks and its reported leaderboard results. |
| ASIMOV-Agentic | Robotics safety evaluation covering refusal, interventions, out-of-distribution shielding, and human escalation. | You need to test safety behavior alongside task performance. |
| MESA-Bench | Tabletop-manipulation generalization suites for spatial configuration, category, instance, and composition shifts. | You need to locate weaknesses under specific kinds of unfamiliar scenes or task combinations. |
| NIST AITE | A sequestered testbed program spanning tasks, datasets, modalities, and domains; its overview identifies themes including video and NLP. | You need context for cross-domain evaluation programs, while recognizing that the overview does not specify one universal end-to-end protocol for this article’s full scope. |
Interpret benchmark scope without overclaiming
Benchmark size and leaderboard coverage tell you about a benchmark’s scope, not whether a model will be accurate or safe in production. The RoboBench project’s 2025 description reports five dimensions, 14 capabilities, 25 task types, and 6,092 question-answer pairs. Its 2026 release information reports an official leaderboard covering 18 state-of-the-art multimodal large language models. Treat those numbers as descriptions of the project and its benchmark coverage, not as general evidence of deployment performance.
Across all modalities, keep the claim matched to the test: a result supports conclusions about the tasks, model version, and conditions that were evaluated. It does not automatically transfer to new modalities, physical settings, or risks the evaluation did not cover.
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