One-sentence brief
Natural dialogue is more than grammatical text. Characters need distinct turn-taking, uncertainty, disagreement, silence, humor, refusal, privacy, and repair behavior without punishing authentic dialect or multilingual expression.
AI-DRIVEN WORLD SAFETY
A language-aware approach to grammar, pragmatic variation, dialogue repetition, deliberate nonstandard voice, and review escalation.
ORIENTATION
Natural dialogue is more than grammatical text. Characters need distinct turn-taking, uncertainty, disagreement, silence, humor, refusal, privacy, and repair behavior without punishing authentic dialect or multilingual expression.
WORKING BRIEF
Apply rules appropriate to the detected language and register. Subject–verb agreement, articles, pronoun case, fragments, and tense shifts should not be evaluated through English-only assumptions.
Look for repeated full-name introductions, “My name is” formulas, biography paraphrase, every-line job references, identical response to different intents, over-formality, and low syntactic variation.
Represent greeting, direct questions, uncertainty, disagreement, correction, topic change, interruption, silence, humor, privacy, refusal, trust repair, and closing as behavioral tendencies rather than fixed scripts.
Dialect, creole, sign-language gloss, disfluency, slang, honorifics, and culturally specific speech can be intentional. Automated grammar warnings should route to qualified review rather than erase the voice.
Cultural plausibility, humor, sarcasm, identity-linked voice, mental-health portrayal, legal or historical claims, stereotype risk, and ambiguous unreliable narration should receive scoped human review.
COMPLETE DOSSIER
Terms are defined for this site’s evidence method, not as universal legal or clinical definitions.
| Signal | Automated response | Human-review question |
|---|---|---|
| Repeated full-name introduction | Flag frequency and context | Is it narratively justified? |
| Nonstandard grammar in dialogue | Detect language/register; avoid auto-correction | Is this deliberate, accurate, and respectful voice? |
| Identical refusal across unrelated characters | Population-level duplication finding | Does role or setting justify shared wording? |
| Biography repeated as conversation | Naturalness error | What concise, situated response fits the scene? |
Does the design preserve the exact fictional identity, ordinary life, independent goals, and ability to refuse rather than reducing the character to a role or prompt?
Pass condition: Identity fields are stable, state is separate, protected traits are not quality scores, and silent substitution is impossible.
Can every transition, validation result, accepted fingerprint, exception, and human decision be traced to a versioned record?
Pass condition: Automated checks, human review, activation authority, and production approval remain separate and explicit.
Can untrusted provider output, administrative evidence, stale revisions, or private data enter live context or binding state?
Pass condition: Only allowlisted, current, reviewed projections and bounded scene or memory packets can be used; failures degrade safely.
Can a changed source, identity revision, harmful behavior, or failed review invalidate downstream use without destroying audit history?
Pass condition: Supersession, pause, rollback, correction, and permanent retirement are defined and testable.
RESEARCH EDITION
This page follows the public method for provenance, confidence, source independence, alternative accounts, limitations, review state, and visible correction.
CONTINUE