arXiv AI
PAUSE: Editable Strategy Artifacts for Long-Form Cultural Story Adaptation
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arXiv AI
Jul 2
Controllable Narrative Rendering for Enhanced Assisted Writing
arXiv:2607. 00009v1 Announce Type: cross Abstract: Despite the remarkable proficiency of large language models (LLMs) in basic writing assistance, their utility in creative writing is fundamentally hindered by a persistent binary failure.
arXiv AI
Jul 28
Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS
arXiv:2607. 22657v1 Announce Type: cross Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing machine-unlearning algorithms can suppress this behavior.
arXiv AI
Jun 30
Towards Human-Level Book-Writing Capability
arXiv:2605. 17064v2 Announce Type: replace Abstract: Large language models are optimized for instruction following and agentic tasks remain poorly aligned with the requirements of high-quality creative writing.
arXiv AI
2d ago
CHARM: Character Hallucination for Multicultural Role Play Benchmark
CHARM is a multicultural benchmark that tests large language models’ ability to adopt a character’s style while respecting knowledge boundaries. It includes 40 real and fictional characters from five cultural-linguistic regions and evaluates two boundary types—Temporal and Cross-Universe—using abstention-enabled multiple-choice questions. The study finds that hallucinations mainly stem from compliance failures: models often recognize a query is out of scope yet still provide out-of-character answers, revealing systematic cultural variations in these errors.
arXiv Machine Learning
Jun 5
Operation-Guided Progressive Human-to-AI Text Transformation Benchmark for Multi-Granularity AI-Text Detection
arXiv:2606. 06481v1 Announce Type: cross Abstract: As AI writing assistants become increasingly integrated into real-world drafting and revision workflows, many documents are no longer purely human-written or AI-generated, but instead result from progressive human-AI co-editing.
arXiv AI
Aug 19
SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution
SAGE is a framework that automates storyboard creation by learning and evolving directing rules from expert demonstrations. It attributes each narrative group’s decisions to specific rules, refines those rules with localized feedback, and routes only relevant rules to each group during generation. In tests, SAGE matched professional directors on a rubric and reduced authoring time by over 83%.