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Agents' Room: Narrative Generation through Multi-step Collaboration

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arxiv 2410.02603 v2 pith:UCMVWSLL submitted 2024-10-03 cs.CL cs.LGcs.MA

Agents' Room: Narrative Generation through Multi-step Collaboration

classification cs.CL cs.LGcs.MA
keywords writingagentsnarrativeroomstorycollaborationcomplexframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Writing compelling fiction is a multifaceted process combining elements such as crafting a plot, developing interesting characters, and using evocative language. While large language models (LLMs) show promise for story writing, they currently rely heavily on intricate prompting, which limits their use. We propose Agents' Room, a generation framework inspired by narrative theory, that decomposes narrative writing into subtasks tackled by specialized agents. To illustrate our method, we introduce Tell Me A Story, a high-quality dataset of complex writing prompts and human-written stories, and a novel evaluation framework designed specifically for assessing long narratives. We show that Agents' Room generates stories that are preferred by expert evaluators over those produced by baseline systems by leveraging collaboration and specialization to decompose the complex story writing task into tractable components. We provide extensive analysis with automated and human-based metrics of the generated output.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    cs.CL 2026-05 unverdicted novelty 7.0

    StoryReward, trained on a new 100k story preference dataset, sets state-of-the-art performance on the introduced StoryRMB benchmark for aligning LLM stories with human preferences.

  2. Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction

    cs.CL 2026-05 unverdicted novelty 6.0

    Post-training on matched OLMo 32B checkpoints compresses thematic motion, affective prevalence, and linguistic diversity in fiction continuations relative to human baselines, producing narrative flattening that conver...

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    cs.AI 2026-03 conditional novelty 5.0

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  4. Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models

    cs.AI 2025-01 unverdicted novelty 3.0

    The paper surveys reinforced reasoning techniques for LLMs, covering automated data construction, learning-to-reason methods, and test-time scaling as steps toward Large Reasoning Models.