125 papers and books. 37 of them fed a step of the lab. Each step links to its card on the Tests page.

Analogy and retrieval

• A Structured Clustering Approach for Inducing Media Narratives

  Rohan Das, Advait Deshmukh, Alexandria Leto et al., 2026

• Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives

  Mohammadhossein Khojasteh, Yifan Jiang, Stefano De Giorgis et al., 2026

• Retriever-Free Retrieval: Navigating Documents with Decision Models

  Chao Wan, Paul Jünger, Beixi Du, Bodong Liu, Navaneetha Krishnan, Kilian Q. Weinberger, 2026

• Story Embeddings: Narrative-Focused Representations of Fictional Stories

  Hatzel, Biemann, 2024, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

  Used in: Oct 26 · The lift

  Theory: Reading a novel

• StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding

  Jiayang et al., 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

• Precise Zero-Shot Dense Retrieval without Relevance Labels

  Gao, Ma, Lin, Callan, 2022, Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

• Document Expansion by Query Prediction

  Nogueira, Yang, Lin, Cho, 2019

  Used in: Oct 25 · Scene index and search (background)

• A Retrieve-and-Edit Framework for Predicting Structured Outputs

  Hashimoto, Guu, Oren, Liang, 2018, NeurIPS 2018

• Generating Sentences by Editing Prototypes

  Guu, Hashimoto, Oren, Liang, 2018, Transactions of the Association for Computational Linguistics

• Retrieve and Refine: Improved Sequence Generation Models For Dialogue

  Weston, Dinan, Miller, 2018, Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

• Extending Analogical Generalization with Near-Misses

  McLure, Friedman, Forbus, 2015, Proceedings of the AAAI Conference on Artificial Intelligence

  Used in: Oct 27 · The lift (background); Oct 27 · The story ledger; Oct 27 · Planning (background)

  Theory: Reading a novel

• The SAM Algorithm for Analogy-Based Story Generation

  Ontañón, Zhu, 2011, AIIDE 2011

  Used in: Oct 27 · Planning

• Generating Story Analogues

  Riedl, León, 2009, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

  Used in: Oct 27 · The lift (background); Oct 27 · The story ledger (background); Oct 27 · Planning

  Theory: Reading a novel

• SEQL: Category learning as progressive abstraction using structure mapping

  Kuehne, Forbus, Gentner, Quinn, 2000, Cognitive Science Society 2000

• MAC/FAC: A model of similarity-based retrieval

  Forbus, Gentner, Law, 1995, Cognitive Science

  Used in: Oct 25 · Scene index and search (background); Oct 27 · The story ledger; Oct 27 · Scene index and search

  Theory: Reading a novel

• The structure-mapping engine: Algorithm and examples

  Falkenhainer, Forbus, Gentner, 1989, Artificial Intelligence

  Used in: Oct 27 · Planning

Corpus and text methods

• Deep-speare: A joint neural model of poetic language, meter and rhyme

  Lau, Cohn, Baldwin, Brooke, Hammond, 2018, ACL 2018

• Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

  Hamilton, Leskovec, Jurafsky, 2016, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

• Quantifying origin and character of long-range correlations in narrative texts

  Stanisław Drożdż, Paweł Oświęcimka, Andrzej Kulig et al., 2014

• Quantitative Analysis of Culture Using Millions of Digitized Books

  Michel et al., 2011, Science

  Used in: Oct 27 · The story ledger

  Theory: Reading a novel

• Comparing corpora using frequency profiling

  Rayson, Garside, 2000, Proceedings of the workshop on Comparing corpora -

• Accurate methods for the statistics of surprise and coincidence

  Dunning, 1993, Computational Linguistics

Detecting and judging machine text

• "I've Seen How This Goes": Characterizing Diversity via Progressive Conditional Surprise

  Matthew Khoriaty, David Williams-King, Shi Feng, 2026

• Autorubric: A Unifying Framework for Rubric-Based LLM Evaluation on Non-Verifiable Tasks

  Delip Rao, Chris Callison-Burch, 2026

• Benchmarking LLM-as-a-Judge for Long-Form Output Evaluation

  Junjie Chen, Yuxi Dong, Haitao Li et al., 2026

• Do Large Language Models Always Tell The Same Stories?

  Thennal DK, Hans Ole Hatzel, 2026

• Illusions of the Gold Standard: A Large-scale Analysis of Human Evaluation Protocols for Long-form Text Generation

  Katelyn Xiaoying Mei, Yi-Li Hsu, Minjoon Choi et al., 2026

• LLMs Exhibit Significantly Lower Uncertainty in Creative Writing Than Professional Writers

  Peiqi Sui, 2026

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

  Zehan Li, Yutong Zhu, Siyang Wu et al., 2026

• NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment

  Wenqing Wu, Yi Zhao, Yuzhuo Wang et al., 2026

  Used in: Oct 15 · The gate and the reader panel (background)

  Theory: Judging our work

• StoryAlign: Evaluating and Training Reward Models for Story Generation

  Haotian Xia, Hao Peng, Yunjia Qi et al., 2026

• StoryScope: Investigating idiosyncrasies in AI fiction

  Jenna Russell, Rishanth Rajendhran, Chau Minh Pham et al., 2026

• NoveltyBench: Evaluating Language Models for Humanlike Diversity

  Yiming Zhang, Harshita Diddee, Susan Holm et al., 2025

  Used in: Oct 15 · The gate and the reader panel (background)

  Theory: Judging our work

• Readers Prefer Outputs of AI Trained on Copyrighted Books over Expert Human Writers

  Tuhin Chakrabarty, Jane C. Ginsburg, Paramveer Dhillon, 2025

• Style over Story: Measuring LLM Narrative Preferences via Structured Selection

  Donghoon Jung, Jiwoo Choi, Songeun Chae et al., 2025

• Art or Artifice? Large Language Models and the False Promise of Creativity

  Chakrabarty et al., 2024, Proceedings of the CHI Conference on Human Factors in Computing Systems

• Do LLMs write like humans? Variation in grammatical and rhetorical styles

  Alex Reinhart, Ben Markey, Michael Laudenbach et al., 2024

• Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text

  Hans et al., 2024

  Used in: Oct 18 · The gate and the reader panel; Oct 18 · Prose and render; Oct 19 · Trained voices

  Theory: Judging our work, Prose that reads human

• Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges

  Vinay Samuel, Yue Zhou, Henry Peng Zou, 2024

• Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

  Zheng et al., 2023, NeurIPS 2023 Datasets and Benchmarks

• Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4

  Chang, Cramer, Soni, Bamman, 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Narrative planning

• Causal Necessity as a Narrative Planning Step Cost Function

  Ware, Senanayake, Farrell, 2023, AIIDE 2023

  Used in: Oct 16 · The story ledger (background); Oct 16 · Measures and profiles (background); Oct 17 · The story ledger (background)

  Theory: Reading a novel

• Salience as a Narrative Planning Step Cost Function

  Ware, Farrell, 2022, IEEE CoG 2022

  Used in: Oct 16 · The story ledger (background); Oct 16 · Measures and profiles (background); Oct 17 · The story ledger (background)

  Theory: Reading a novel

• Sabre: A Narrative Planner Supporting Intention and Deep Theory of Mind

  Ware, Siler, 2021, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

  Used in: Oct 16 · The story ledger (background); Oct 16 · Measures and profiles (background); Oct 17 · The story ledger (background); Oct 27 · Planning

  Theory: Reading a novel

• Exploring Regression-Based Narrative Planning

  Ware, Fisher, 2020, INT/WICED 2020

  Used in: Oct 16 · The story ledger (background); Oct 16 · Measures and profiles (background); Oct 17 · The story ledger (background)

  Theory: Reading a novel

• Plan-and-Write: Towards Better Automatic Storytelling

  Yao et al., 2019, Proceedings of the AAAI Conference on Artificial Intelligence

• Narrative Planning: Balancing Plot and Character

  Mark Owen Riedl, Robert Michael Young, 2014

  Used in: Oct 27 · Planning

• Lessons Learned From a Rational Reconstruction of Minstrel

  Tearse et al., 2012, AAAI 2012

  Used in: Oct 27 · Planning

• The Creative Process: A Computer Model of Storytelling and Creativity

  Turner, 1994, Lawrence Erlbaum (book)

  Used in: Oct 27 · The lift (background); Oct 27 · The story ledger (background); Oct 27 · Planning

  Theory: Reading a novel

• Plot units and narrative summarization

  Lehnert, 1981, Cognitive Science

  Used in: Oct 22 · Foundation; Oct 25 · Scene index and search (background); Oct 26 · The lift (background)

  Theory: Reading a novel

• Scripts, Plans, Goals, and Understanding

  Schank, Abelson, 1977, Lawrence Erlbaum (book)

  Used in: Oct 25 · Scene index and search (background); Oct 26 · The lift (background)

  Theory: Reading a novel

Reader reception and book success

• Dimensions of Quality: Contrasting Stylistic vs. Semantic Features for Modelling Literary Quality

  Feldkamp (Moreira), Bizzoni, 2023

• Item Recommendation on Monotonic Behavior Chains

  Wan, McAuley, 2018, RecSys 2018

• A Multi-task Approach to Predict Likability of Books

  Maharjan et al., 2017, Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

• Success with Style: Using Writing Style to Predict the Success of Novels

  Ashok, Feng, Choi, 2013, Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

Readers: suspense, surprise and reading

• Spoiler Alert: Narrative Forecasting as a Metric for Tension in LLM Storytelling

  Peiqi Sui, Yutong Zhu, Tianyi Cheng et al., 2026

• Do Language Models Agree with Human Perceptions of Suspense in Stories?

  Glenn Matlin, Devin Zhang, Rodrigo Barroso Loza et al., 2025

• The Challenge and Reward of Fair Play in Narrative: A Computational Approach

  Eitan Wagner, Renana Keydar, Omri Abend, 2025

  Used in: Oct 17 · The reader model (background); Oct 19 · The reader model (background)

• Creating Suspenseful Stories: Iterative Planning with Large Language Models

  Kaige Xie, Mark Riedl, 2024

• Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse

  Tsipidi et al., 2024, EMNLP 2024

• Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation

  David Wilmot, Frank Keller, 2020

  Used in: Oct 17 · The reader model (background); Oct 19 · The reader model (background)

• Presenting GECO: An eyetracking corpus of monolingual and bilingual sentence reading

  Cop, Dirix, Drieghe, Duyck, 2017, Behavior Research Methods

  Used in: Oct 17 · The reader model; Oct 18 · The reader model (background); Oct 19 · The reader model (background)

• The emotional arcs of stories are dominated by six basic shapes

  Reagan et al., 2016, EPJ Data Science

• Suspense and Surprise

  Ely, Frankel, Kamenica, 2015, Journal of Political Economy

  Used in: Oct 17 · The reader model (background); Oct 19 · The reader model (background)

• Suspenser: A Story Generation System for Suspense

  Cheong, Young, 2015, IEEE Transactions on Computational Intelligence and AI in Games

  Used in: Oct 27 · The lift (background); Oct 27 · The story ledger (background); Oct 27 · Planning

  Theory: Reading a novel

• Dramatis: A Computational Model of Suspense

  O'Neill, Riedl, 2014, AAAI 2014

  Used in: Oct 17 · The reader model (background); Oct 19 · The reader model (background); Oct 27 · The lift (background); Oct 27 · The story ledger (background); Oct 27 · Planning

  Theory: Reading a novel

• The Cognitive Structure of Emotions

  Ortony, Clore, Collins, 1988

Story structure and representation

• Codified Foreshadowing-Payoff Text Generation

  Longfei Yun, Kun Zhou, Yupeng Hou et al., 2026

  Used in: Oct 27 · Planning

• From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives

  Chloe Ho, Aayush Aluru, Muhammad Hammouri et al., 2026

• Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction

  Mohammad Saifullah, Thomas Kornmaier, Taaha Kazi et al., 2026

• Scaling Long-Form Story Generation via Narrative State Tracking

  Zhennan Wan, Jianfei Chen, 2026

• Shared Worlds, Private Minds: Structured Memory for Long-Form Writing as World Creation

  Qiuyu Tian, Xiaowen Gu, Hang Su et al., 2026

• Codifying character logic

  Peng, Shang, 2025, Advances in Neural Information Processing Systems 38

• An answer set programming-based implementation of epistemic probabilistic event calculus

  D'Asaro et al., 2024, International Journal of Approximate Reasoning

• FACTTRACK: Time-Aware World State Tracking in Story Outlines

  Zhiheng Lyu, Kevin Yang, Lingpeng Kong et al., 2024

• Towards a Formal Model of Narratives

  Louis Castricato, Stella Biderman, Rogelio E. Cardona-Rivera et al., 2021

  Used in: Oct 17 · The reader model (background); Oct 19 · The reader model (background)

• Event Representations for Automated Story Generation with Deep Neural Nets

  Martin et al., 2018, Proceedings of the AAAI Conference on Artificial Intelligence

  Used in: Oct 26 · The lift (background)

  Theory: Reading a novel

• Overview of PicTropes, a film trope dataset

  García-Ortega et al., 2018

• Modeling Dynamic Relationships Between Characters in Literary Novels

  Snigdha Chaturvedi, Shashank Srivastava, Hal Daume et al., 2015

• Abstract Meaning Representation for Sembanking

  Banarescu et al., 2013, Linguistic Annotation Workshop 2013

• DramaBank: Annotating Agency in Narrative Discourse

  Elson, 2012, LREC 2012

• FactBank: A Corpus Annotated with Event Factuality

  Saurí, Pustejovsky, 2009, Language Resources and Evaluation

• Unsupervised Learning of Narrative Event Chains

  Chambers, Jurafsky, 2008, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - ACL-IJCNLP '09

  Used in: Oct 26 · The lift (background)

  Theory: Reading a novel

• The Event Calculus Explained

  Shanahan, 1999, Artificial Intelligence Today (Springer)

• Rhetorical Structure Theory: Toward a functional theory of text organization

  Mann, Thompson, 1988, Text - Interdisciplinary Journal for the Study of Discourse

  Used in: Oct 26 · The lift (background)

  Theory: Reading a novel

• Stories Are to Entertain: A Structural-Affect Theory of Stories

  Brewer, Lichtenstein, 1982, Journal of Pragmatics

• Narrative Discourse: An Essay in Method

  Genette, 1980, Cornell University Press (book)

  Used in: Oct 27 · The story ledger

  Theory: Reading a novel

• Expositional Modes and Temporal Ordering in Fiction

  Sternberg, 1978, Johns Hopkins University Press (book)

• Remembrance of things parsed: Story structure and recall

  Mandler, Johnson, 1977, Cognitive Psychology

  Used in: Oct 22 · Measures and profiles (background); Oct 22 · The story ledger (background)

  Theory: Reading a novel

• Notes on a schema for stories

  Rumelhart, 1975, Representation and Understanding

  Used in: Oct 22 · Measures and profiles (background); Oct 22 · The story ledger (background)

  Theory: Reading a novel

• Morphology of the Folktale

  Propp, 1968, University of Texas Press (book)

• Narrative analysis: Oral versions of personal experience

  Labov, Waletzky, 1967, Essays on the Verbal and Visual Arts (book chapter)

Training and tuning models

• DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing

  Qian Cao, Yahui Liu, Wei Bi et al., 2026

• Predicting Steering Vectors and Adapter Weights for Few-Shot Author-Style Transfer

  Leonard Popp, Danni Liu, Supriti Sinhamahapatra et al., 2026

• Score Centering Stabilizes Off-policy Reinforcement Learning

  Martin Marek, Max Ryabinin, 2026

• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

  Lakshya A Agrawal, Shangyin Tan, Dilara Soylu et al., 2025

  Used in: Oct 18 · The gate and the reader panel (background); Oct 18 · Prose and render (background)

  Theory: Judging our work, Prose that reads human

• SparseLoRA: Accelerating LLM Fine-Tuning with Contextual Sparsity

  Samir Khaki, Xiuyu Li, Junxian Guo et al., 2025

• GaLore$+$: Boosting Low-Rank Adaptation for LLMs with Cross-Head Projection

  Xutao Liao, Shaohui Li, Yuhui Xu et al., 2024

• GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

  Jiawei Zhao, Zhenyu Zhang, Beidi Chen et al., 2024

• LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

  Yaowei Zheng, Richong Zhang, Junhao Zhang et al., 2024

• Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients

  Zhenyu Zhang, Ajay Jaiswal, Lu Yin et al., 2024

Writing long stories with models

• ConWriter: Transition-Constrained Stateful Long-Form Story Generation with Lightweight Neuro-Symbolic Consistency Control

  Jindong Li, Yang Yang, Zihao Liu et al., 2026

• Creative Collision: Directorial Persona Steering and Competition in Large Language Models

  Subramanyam Sahoo, Justin Shenk, 2026

• Lost in Stories: Consistency Bugs in Long Story Generation by LLMs

  Junjie Li, Xinrui Guo, Yuhao Wu et al., 2026

• Narrix: Remixing Narrative Strategies from Examples for Story Writing

  Chao Zhang, Shunan Guo, Abe Davis et al., 2026

• POLARIS: Guiding Small Models to Write Long Stories

  Rishanth Rajendhran, Jenna Russell, Mohit Iyyer et al., 2026

• CreAgentive: An Agent Workflow Driven Multi-Category Creative Generation Engine

  Yuyang Cheng, Linyue Cai, Changwei Peng et al., 2025

• Finding Flawed Fictions: Evaluating Complex Reasoning in Language Models via Plot Hole Detection

  Kabir Ahuja, Melanie Sclar, Yulia Tsvetkov, 2025

• Learning to Reason for Long-Form Story Generation

  Alexander Gurung, Mirella Lapata, 2025

• LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing

  Daniel Fein, Sebastian Russo, Violet Xiang et al., 2025

  Used in: Oct 15 · The gate and the reader panel (background)

  Theory: Judging our work

• R$^2$: A LLM Based Novel-to-Screenplay Generation Framework with Causal Plot Graphs

  Zefeng Lin, Yi Xiao, Zhiqiang Mo et al., 2025

• StoryWriter: A Multi-Agent Framework for Long Story Generation

  Haotian Xia, Hao Peng, Yunjia Qi et al., 2025

• WebNovelBench: Placing LLM Novelists on the Web Novel Distribution

  Leon Lin, Jun Zheng, Haidong Wang, 2025

• What Matters in Evaluating Book-Length Stories? A Systematic Study of Long Story Evaluation

  Dingyi Yang, Qin Jin, 2025

• Are Large Language Models Capable of Generating Human-Level Narratives?

  Tian et al., 2024, Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

• DOC: Improving Long Story Coherence With Detailed Outline Control

  Yang, Klein, Peng, Tian, 2023, ACL 2023

• Re3: Generating Longer Stories With Recursive Reprompting and Revision

  Yang, Tian, Peng, Klein, 2022, EMNLP 2022

• SNaC: Coherence Error Detection for Narrative Summarization

  Goyal, Li, Durrett, 2022, EMNLP 2022

• Automatic Story Generation: A Survey of Approaches

  Alhussain, Azmi, 2021, ACM Computing Surveys

• Plot-guided Adversarial Example Construction for Evaluating Open-domain Story Generation

  Ghazarian et al., 2021, NAACL 2021

• Story Realization: Expanding Plot Events into Sentences

  Ammanabrolu et al., 2020, Proceedings of the AAAI Conference on Artificial Intelligence

• Counterfactual Story Reasoning and Generation

  Qin et al., 2019, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

• Strategies for Structuring Story Generation

  Fan, Lewis, Dauphin, 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

  Used in: Oct 26 · The lift (background)

  Theory: Reading a novel

• Hierarchical Neural Story Generation

  Fan, Lewis, Dauphin, 2018, ACL 2018

Writing and readability

• Five Principles for Generating Readable AI Text in Bulk

  Shreya Shankar, 2026, Full Stack Data Lab blog