# How often do LLM narrators contradict established story facts or plot commitments over many turns, and what checks did the benchmark use?

- id: art_01M5MBXYMARTFD5SC1N1X0VTQ2
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T01, T18, T20 · tags: none
- updated: 2026-10-23T12:31:48.867Z
- url: https://veyrul.com/articles/art_01M5MBXYMARTFD5SC1N1X0VTQ2

NCP-Bench (arXiv 2608.08160 v1, ICML 2026 per the paper) turns 100 movie synopses into specifications: an initial fact ledger, ordered trajectory nodes, and commitments (invariant, ordering, achievement). An adversarial player agent pushes six narrator LLMs for up to 100 turns; a fixed-prompt LLM auditor (Gemini-2.5-Flash) checks each reply for fact, commitment and player-input conflicts and ends the run on a confirmed conflict. Best model GPT-5.2: 42% of runs still conflict-free after 20 turns; fact conflicts occurred in 40-68% of interactions; only 3.5% of all 600 runs lasted 100 turns. The narrator is given the dialogue history (Algorithm 1), not the ledger; whether an explicit ledger in the narrator prompt helps is therefore not tested.

NCP-Bench (arXiv 2608.08160 v1, ICML 2026 per the paper) turns 100 movie synopses into specifications: an initial fact ledger, ordered trajectory nodes, and commitments (invariant, ordering, achievement). An adversarial player agent pushes six narrator LLMs for up to 100 turns; a fixed-prompt LLM auditor (Gemini-2.5-Flash) checks each reply for fact, commitment and player-input conflicts and ends the run on a confirmed conflict. Best model GPT-5.2: 42% of runs still conflict-free after 20 turns; fact conflicts occurred in 40-68% of interactions; only 3.5% of all 600 runs lasted 100 turns. The narrator is given the dialogue history (Algorithm 1), not the ledger; whether an explicit ledger in the narrator prompt helps is therefore not tested.

Curator qualifications: All excerpts located; auditor-dependent turn counts (24.80 vs 22.16) kept.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2608.08160v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://github.com/yingpengma/NCP-Bench/blob/780aeb559ce216a57cbd934b5a20bfd35018c4f6/LICENSE",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] NCP-Bench (arXiv 2608.08160 v1, ICML 2026 per the paper) turns 100 movie synopses into specifications: an initial fact ledger, ordered trajectory nodes, and commitments (invariant, ordering, achievement). An adversarial player agent pushes six narrator LLMs for up to 100 turns; a fixed-prompt LLM auditor (Gemini-2.5-Flash) checks each reply for fact, commitment and player-input conflicts and ends the run on a confirmed conflict. Best model GPT-5.2: 42% of runs still conflict-free after 20 turns; fact conflicts occurred in 40-68% of interactions; only 3.5% of all 600 runs lasted 100 turns. The narrator is given the dialogue history (Algorithm 1), not the ledger; whether an explicit ledger in the narrator prompt helps is therefore not tested. · locator: candidate evidence excerpts · source: https://arxiv.org/abs/2608.08160v1

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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