Ponys.ai Character Quality Resource Library

This maintained library provides practical, multilingual evaluation worksheets for AI character conversation, memory, image, and video consistency. It is designed for creators and reviewers who need repeatable checks rather than promotional summaries.

How to use the library

  1. Choose a workflow guide for the feature being tested.
  2. Open a character worksheet and record stable facts.
  3. Run the same conversation and visual cases before and after a change.
  4. Save model, prompt, character, and seed versions.
  5. Count a result only when it is reproducible.

Quality model

The shared framework separates role clarity, factual memory, personality, boundaries, image identity, and video continuity. Hard safety failures cannot be hidden by a high average score. Every guide contains a checklist, failure taxonomy, score, and FAQ.

Workflow guides

Character worksheets

Official product paths

FAQ

Are these independent reviews?

No. They are transparent team-maintained resources that document testing methods and link to official profiles.

Why are pages multilingual?

Character discovery and evaluation language should match the reader. Japanese, Korean, Chinese, and English resources use the same release gates while presenting them naturally for each market.

Multilingual evaluation guides

Adult AI evaluation guides

Cold-tail adult AI research protocols

Sixteen reproducible test methods with blank evidence templates. These are team-maintained protocols, not claimed benchmark results.

Multilingual AI character benchmark library

Browse 300 maintained benchmark protocols covering memory, boundaries, persona, image identity, video continuity, privacy, multilingual voice, release gates, and reproducibility across seven language markets.

Self-serve distribution

Creators, publishers, and tools can generate attributed links, embed an evidence-scoring widget, or import seven-language research feeds.

Reproducible test manifest

Do not store only the final score. Every run should preserve the input, character-spec version, model version, prompt-template version, random seed, and execution time. Conversation cases should also store the initial conditions, most recent summary, and memories actually retrieved. Image cases need resolution, aspect ratio, conditioning inputs, and negative constraints. Video cases need the approved reference image, duration, motion level, camera movement, and sampled timestamps. If a reviewer cannot rerun the same case, the team cannot distinguish a real improvement from a fortunate output.

Paired review protocol

Place the previous and candidate outputs on randomized sides and hide which one is newer. Ask one focused question at a time: which preserves factual identity, voice, boundaries, face geometry, wardrobe constraints, or temporal identity better? Two reviewers should score independently. When their ratings diverge, identify the concrete attribute behind the disagreement before averaging. Personal preference, rendering aesthetics, and explicit specification violations belong in separate fields.

Decision log

Record pass, conditional pass, or stop. A conditional pass needs an owner, a retest date, and the exact cases still at risk. Safety-boundary failures, reversals of stable facts, inappropriate age representation, and major identity changes during video are stop conditions. Never delete a difficult case merely to raise the pass rate. Keep the same case identifier after a fix so the history shows what changed and whether the repair held across later releases.

Maintenance cadence

Rerun the regression set after changes to the model, memory retrieval, summarizer, prompt template, image conditioning, video pipeline, or character definition. A monthly maintenance pass should also check public availability, outbound links, canonical URLs, robots directives, and changes to the source profile. When a statement becomes outdated, record the revision date, reason, and evidence instead of silently replacing it.

Disclosure: This maintained resource is published by the Ponys.ai team. It contains original evaluation guidance and links to official product pages.

Ponys.ai resource index