Index of everything on this site: https://ai.hiina.space/index.md
Older game dialogue systems already split what a line does from the words played. Valve's Response System layers concepts (reasons to speak, such as TLK_DANGER), criteria (speaker and world state), weighted rules, response groups and responses, with built-in repeat avoidance; the most specific matching rule wins, with fallback to less specific ones (Valve Response System; Emily Short on Ruskin's GDC 2012 talk). Versu authors lines as "quips" that pair a text template with social effects, so one line carries surface and subtext at once, and characters get verb-level text overrides and adverbs for internal state (Evans and Short, Versu: A Simulationist Storytelling System). Comme il Faut in Prom Week gives each social exchange a primary intent, several instantiations chosen by salience, and conditions on statuses, relationships, traits, social networks and a database of past social facts the dialogue can cite (McCoy et al.). PERSONAGE drove extraversion through 29 continuous generation parameters, with judges rating extravert output 5.98 and introvert output 2.96 on a 1-7 scale (Mairesse and Walker, ACL 2007), which supports a user style interface of a few named knobs rather than paragraphs of prose.
LLM-era character systems bear on the renderer in one specific way.
Character Card V2 separates situation (description,
scenario) from voice samples (mes_example)
across 15 fields, and supports {{original}} composition in
both system_prompt and
post_history_instructions (Character
Card V2 spec). Jun et al. tested 211 base profiles, each in
structured and unstructured versions, on Qwen3-8B, Qwen3-235B-A22B,
Mistral-Small-3.2-24B, OLMo-3.1-32B and DeepSeek-v3.2. Character
familiarity was negligible on PersonaGym, structure had only marginal
and inconsistent effects, and disposition was the bottleneck: immoral
characters scored lower on every model (Jun et al. 2026). Ubisoft's
NEO NPC prototype fed writer-authored backstory and traits to an LLM
that "impersonates" rather than improvises, and shipped nothing (Game
World Observer, secondary). Two consequences follow for translation.
Spend the record's budget on intent and relationship content, not
elaborate persona schemas. And flag intended harshness explicitly,
because an aligned small model softening a villain, or refusing like
Llama2 did in the formality study, is a fidelity failure. The
generalisation toward "AI improvises from story templates", and on to
assistant persona selection, is real but mostly irrelevant here: a
translation renderer has a fixed source and should behave like a Valve
response lookup with a generative last mile, not like a free agent.
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