Which documented techniques keep an LLM from giving a character information that character should not have, and what effect did each show?

Six techniques have measured effects, each in a different setting, so they are not comparable head-to-head. (1) Two-pass perspective filtering (SimToM): GPT-3.5-Turbo ToMi false-belief 81.0 (+13.8 over 0-shot); single-prompt version loses the gain; human-annotated perspectives reach 96/96/82/98; mixed on OpenToM (candidate 04). (2) Per-character belief graphs (SymbolicToM): GPT-3-Davinci on self-built structure sets 20/25/39 to 92/91/90; needs chronological stories and suffers from extraction errors. (3) Visibility-tagged fact memory with scene anchoring (ReverieMem): refusal 81.2 versus 8.7 to 11.3 for retrieve-everything RAG; ablations show both layers matter; one speaker only. (4) Separate agent contexts versus an omniscient script (Zhou et al.): fewer early leaks (0.39 vs 0.13) but far fewer deals (30 vs 94 percent). (5) Awareness reminder (SimpleToM): behaviour prediction above 80 for three models, judgment low. (6) Key Facts Injection: 73 percent fewer 'known-information forgetting' cases in one deployment (abstract only; it addresses forgetting, not leakage). Any workflow for authored fiction built from these is a hypothesis. Curator qualifications: Six techniques with measured effects in different settings (two-pass perspective filtering SimToM, per-character belief graphs SymbolicToM, and others); not comparable head-to-head. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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      "url": "https://arxiv.org/html/2311.10227v1",
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      "url": "https://arxiv.org/html/2306.00924v1",
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      "url": "https://arxiv.org/html/2606.25632v1",
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      "url": "https://arxiv.org/html/2403.05020v4",
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      "url": "https://iclr.cc/virtual/2026/10021229",
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      "url": "https://arxiv.org/html/2410.13648v2",
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