# VeriForge: Mitigating Latent Knowledge Gaps in Narrative Drafting via Mixed-Initiative Scaffolding

arXiv: https://arxiv.org/abs/2608.09698 · HTML: https://mlsanity.com/paper/2608.09698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 72 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 23 of 23)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://mlsanity.com/paper/2402.03216/) (2024)
- [Unifying Large Language Models and Knowledge Graphs: A Roadmap](https://mlsanity.com/paper/2306.08302/) (2023)
- [Homogenization Effects of Large Language Models on Human Creative Ideation](https://mlsanity.com/paper/2402.01536/) (2024)
- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation](https://mlsanity.com/paper/2310.12953/) (2023)
- [AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances](https://mlsanity.com/paper/2409.11360/) (2024)
- [Adapting Large Language Models to Domains via Reading Comprehension](https://mlsanity.com/paper/2309.09530/) (2023)
- [Need Help? Designing Proactive AI Assistants for Programming](https://mlsanity.com/paper/2410.04596/) (2024)
- [Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support](https://mlsanity.com/paper/2502.18658/) (2025)
- [INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions](https://mlsanity.com/paper/2207.00746/) (2022)
- [Synergi: A Mixed-Initiative System for Scholarly Synthesis and Sensemaking](https://mlsanity.com/paper/2308.07517/) (2023)
- [Patchview: LLM-Powered Worldbuilding with Generative Dust and Magnet Visualization](https://mlsanity.com/paper/2408.04112/) (2024)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [Visual Story-Writing: Writing by Manipulating Visual Representations of Stories](https://mlsanity.com/paper/2410.07486/) (2024)
- [TaleStream: Supporting Story Ideation with Trope Knowledge](https://mlsanity.com/paper/2309.03790/) (2023)
- [ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database](https://mlsanity.com/paper/2410.03224/) (2024)
- [StorySage: Conversational Autobiography Writing Powered by a Multi-Agent Framework](https://mlsanity.com/paper/2506.14159/) (2025)
- [Constella: Supporting Storywriters' Interconnected Character Creation through LLM-based Multi-Agents](https://mlsanity.com/paper/2507.05820/) (2025)
- [ClueCart: Supporting Game Story Interpretation and Narrative Inference from Fragmented Clues](https://mlsanity.com/paper/2503.06098/) (2025)
- [AI Rivalry as a Craft: How Resisting and Embracing Generative AI Reshape Writing Professions](https://mlsanity.com/paper/2503.09901/) (2025)
- [Composable Prompting Workspaces for Creative Writing: Exploration and Iteration Using Dynamic Widgets](https://mlsanity.com/paper/2503.21394/) (2025)
- [Vistoria: A Multimodal System to Support Fictional Story Writing through Instrumental Text-Image Co-Editing](https://mlsanity.com/paper/2509.13646/) (2025)

## Abstract (arXiv)

Great fiction earns its verisimilitude through precise details, from how a longsword is gripped to pierce armor gaps to why a bleeding corpse cannot yet smell of decay, weaving domain expertise into the fabric of invented worlds. Current AI writing tools offer limited support for discovering and integrating unfamiliar domain knowledge into narrative. They require explicit queries that authors cannot formulate, generate finished prose that risks homogenizing voice, or assist only within the boundaries of what authors already know. We argue that AI should reveal latent knowledge gaps to writers while preserving their agency to transform discovered knowledge into authentic prose. Grounded in formative interviews with 9 fiction writers, we present VeriForge, a mixed-initiative writing system that divides cognitive labor so that the system assumes initiative over domain discovery while the author retains full initiative over narrative synthesis. VeriForge realizes this through three complementary mechanisms. Proactive inline highlighting flags potential knowledge gaps as authors draft. Dual-stream querying pairs conversational responses with source-anchored Knowledge Cards for direct fact extraction. A spatial Knowledge Canvas allows authors to organize and connect discovered knowledge across their writing. These mechanisms are powered by a graph-based retrieval-augmented generation pipeline grounded in domain-specific source materials. A within-subjects user study (N=12) provides preliminary evidence that this paradigm helps authors recognize previously overlooked knowledge gaps, supports creative exploration, and is perceived by expert raters to produce passages with stronger domain grounding in a controlled cold-start writing task.