Protocols and practical guides for reproducible AI model evaluation, from audio to multimodal workflows. https://www.modelworkflowlab.com/ 2026-10-27T00:00:00Z https://www.modelworkflowlab.com/multimodal-input-checks/ 2026-10-27T00:00:00Z Model Workflow Lab Editorial

A preflight protocol for image, audio, video, and text evaluation inputs.

<p>Before attributing an output difference to the model, check what the model actually received. Input preparation can change the task, especially when multiple modalities are involved.</p> <h2 id="preserve-the-original-and-the-submitted-input">Preserve the original and the submitted input</h2> <p>Save the original asset, the transformation settings, and a checksum of the final submitted file. Keep the prompt and the ordering of attachments alongside the record. For a hosted service, note any documented server-side transformations you cannot control.</p> <h2 id="inspect-each-modality">Inspect each modality</h2> <table> <thead> <tr> <th>Modality</th> <th>Preflight questions</th> </tr> </thead> <tbody> <tr> <td>Images</td> <td>Were crop, orientation, resolution, and color handling preserved?</td> </tr> <tr> <td>Audio</td> <td>Were sample rate, channel mixing, trimming, and normalization recorded?</td> </tr> <tr> <td>Video</td> <td>Which frames and time intervals were retained? Was audio included?</td> </tr> <tr> <td>Text</td> <td>Did truncation, formatting, or encoding change the task?</td> </tr> </tbody> </table> <p>Treat unsupported combinations as unsupported until documented or tested. Do not infer video reasoning from isolated image support, or temporal consistency from a single output.</p> <h2 id="use-controls">Use controls</h2> <p>Include an input with an unambiguous expected observation and an input that should expose a known boundary. Inspect submitted files before running the full set. When a preprocessing change alters results, report it as a separate condition.</p> <p>For emerging world-model workflows, also define the state, action, time horizon, and observation used by the task. The protocol must match the actual interface; the term world model alone does not specify one.</p> <p>Continue with <a href="/reproducible-model-evaluation/">the evaluation run record</a> to preserve the full procedure. This is a proposed protocol, not a completed experiment.</p> https://www.modelworkflowlab.com/reproducible-model-evaluation/ 2026-10-27T00:00:00Z Model Workflow Lab Editorial

What to save so another person can understand and repeat a model evaluation.

<p>A useful evaluation record explains what was run, under which conditions, against which inputs, and with which judging criteria. Save that record before interpreting a result.</p> <h2 id="minimum-run-record">Minimum run record</h2> <table> <thead> <tr> <th>Item</th> <th>Record</th> </tr> </thead> <tbody> <tr> <td>Model</td> <td>Exact identifier, revision, weight variant, or endpoint</td> </tr> <tr> <td>Environment</td> <td>Runtime, dependencies, device, and relevant resource limits</td> </tr> <tr> <td>Inputs</td> <td>Dataset revision or checksums and sample identifiers</td> </tr> <tr> <td>Configuration</td> <td>Prompts, preprocessing, decoding settings, and seed where supported</td> </tr> <tr> <td>Outputs</td> <td>Original generated files and machine-readable logs</td> </tr> <tr> <td>Evaluation</td> <td>Metric version, judging criteria, reviewer process, and exclusions</td> </tr> </tbody> </table> <p>Use relative identifiers in shareable records and keep private inputs out of public repositories. Record unavailable version information explicitly.</p> <h2 id="keep-the-procedure-stable">Keep the procedure stable</h2> <p>Define the evaluation set and acceptance criteria before running candidates. Run comparable conditions and retain failures, timeouts, and retries. If you change the procedure, create a new run record rather than overwriting the old one.</p> <h2 id="report-the-limit-of-the-result">Report the limit of the result</h2> <p>State the tested population, number of samples, configuration, and unresolved failure modes. A small evaluation supports a bounded observation. It does not establish universal performance or causality.</p> <p>This page proposes a run-record protocol; it contains no completed experimental results. See <a href="https://huggingface.co/docs/evaluate/index">Hugging Face Evaluate</a> for an evaluation-tooling reference, and <a href="/tts-evaluation-protocol/">our speech protocol</a> for an example application of the method.</p> https://www.modelworkflowlab.com/tts-evaluation-protocol/ 2026-10-27T00:00:00Z Model Workflow Lab Editorial

A proposed evaluation procedure for pronunciation, intelligibility, voice behavior, and timing.

<p>Use a fixed script set and a documented listening procedure before comparing text-to-speech systems. Keep quality judgments separate from runtime measurements.</p> <h2 id="1-prepare-the-script-set">1. Prepare the script set</h2> <p>Include ordinary sentences, numbers, abbreviations, named entities, punctuation, and a longer paragraph. Add the languages and speaking styles required by your application. Record script identifiers and do not modify text between candidates without logging the change.</p> <h2 id="2-record-the-generation-conditions">2. Record the generation conditions</h2> <p>Save the model identifier, revision or endpoint, voice setting, reference audio if applicable, sample rate, decoding settings, and output format. Use reference recordings only with permission for the intended purpose. Retain the original outputs before loudness adjustment or other post-processing.</p> <h2 id="3-listen-against-separate-criteria">3. Listen against separate criteria</h2> <p>Judge intelligibility, pronunciation, prosody, artifacts, and speaker consistency separately. If speaker similarity matters, define the authorized reference and a separate comparison process. Randomize clip order and hide model names from listeners where practical.</p> <h2 id="4-measure-timing-with-clear-boundaries">4. Measure timing with clear boundaries</h2> <p>For streaming, define when the request starts and when the first usable audio arrives. Also record total generation time and audio duration. Note device or service conditions, network conditions where relevant, warm-up handling, and concurrency. Report failures and retries alongside successful runs.</p> <h2 id="5-report-bounded-conclusions">5. Report bounded conclusions</h2> <p>Publish the scripts you can share, the procedure, sample count, settings, and known limitations. Keep listening judgments distinct from objective timing. Do not turn a small test into an overall model ranking.</p> <p>This page proposes a protocol. No generated samples or benchmark results are claimed. Use <a href="/reproducible-model-evaluation/">the general run record</a> to organize the evidence.</p>