{"total":23,"rows":[{"id":"2509.02534","title":"Jointly Reinforcing Diversity and Quality in Language Model Generations","created":"2025-09-02","cited_by_count":80,"area":null},{"id":"2509.15194","title":"Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation","created":"2025-09-18","cited_by_count":45,"area":null},{"id":"2509.04784","title":"Post-training Large Language Models for Diverse High-Quality Responses","created":"2025-09-05","cited_by_count":19,"area":null},{"id":"2505.16245","title":"Diverse, not Short: A Length-Controlled Data Selection Strategy for Improving Response Diversity of Language Models","created":"2025-05-22","cited_by_count":9,"area":null},{"id":"2511.05650","title":"Optimizing Diversity and Quality through Base-Aligned Model Collaboration","created":"2025-11-07","cited_by_count":8,"area":null},{"id":"2504.09389","title":"Measuring LLM Novelty As The Frontier Of Original And High-Quality Output","created":"2025-04-13","cited_by_count":6,"area":null},{"id":"2602.03429","title":"DiscoverLLM: From Executing Intents to Discovering Them","created":"2026-02-03","cited_by_count":6,"area":null},{"id":"2601.08003","title":"LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback","created":"2026-01-12","cited_by_count":5,"area":null},{"id":"2508.06094","title":"ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline","created":"2025-08-08","cited_by_count":4,"area":null},{"id":"2603.19519","title":"Inducing Sustained Creativity and Diversity in Large Language Models","created":"2026-03-19","cited_by_count":2,"area":null},{"id":"2604.02319","title":"No Single Best Model for Diversity: Learning a Router for Sample Diversity","created":"2026-04-02","cited_by_count":2,"area":null},{"id":"2605.11128","title":"Sampling More, Getting Less: Calibration is the Diversity Bottleneck in LLMs","created":"2026-05-11","cited_by_count":2,"area":null},{"id":"2605.26442","title":"Alignment Tuning for Large Language Models: A Data-Centric Lens on Alignment Data Pipelines","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2605.27878","title":"Narrative Flattening: How Post-Training Compresses Thematic, Affective, and Stylistic Variation in LLM Fiction","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2511.05018","title":"Pluralistic Behavior Suite: Stress-Testing Multi-Turn Adherence to Custom Behavioral Policies","created":"2025-11-07","cited_by_count":0,"area":null},{"id":"2604.14969","title":"Discovering Novel LLM Experts via Task-Capability Coevolution","created":"2026-04-16","cited_by_count":0,"area":null},{"id":"2605.10574","title":"LLM Jaggedness Unlocks Scientific Creativity","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.26492","title":"Elias in the Lighthouse, Again? Diagnosing Low Diversity in LLM Stories","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2608.03233","title":"On the Diversity of Analogy Making in Large Language Models","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.07460","title":"CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity","created":"2026-08-07","cited_by_count":0,"area":null},{"id":"2609.07120","title":"PTCG: Persona-guided Tree-based Counterargument Generation","created":"2026-09-07","cited_by_count":0,"area":null},{"id":"2609.16454","title":"Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs","created":"2026-09-15","cited_by_count":0,"area":null},{"id":"2609.33038","title":"Improving the Diversity of LLM Outputs without a Trade-off","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}