{"total":24,"rows":[{"id":"2505.06120","title":"LLMs Get Lost In Multi-Turn Conversation","created":"2025-05-09","cited_by_count":429,"area":null},{"id":"2505.13995","title":"ELEPHANT: Measuring and understanding social sycophancy in LLMs","created":"2025-05-20","cited_by_count":129,"area":null},{"id":"2505.10831","title":"Creating General User Models from Computer Use","created":"2025-05-16","cited_by_count":44,"area":null},{"id":"2507.23158","title":"User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal","created":"2025-07-30","cited_by_count":19,"area":null},{"id":"2601.19792","title":"LVLMs and Humans Ground Differently in Referential Communication","created":"2026-01-27","cited_by_count":8,"area":null},{"id":"2510.25744","title":"Completion $\\neq$ Collaboration: Scaling Collaborative Effort with Agents","created":"2025-10-29","cited_by_count":6,"area":null},{"id":"2506.21532","title":"\"What's Up, Doc?\": Analyzing How Users Seek Health Information in Large-Scale Conversational AI Datasets","created":"2025-06-26","cited_by_count":5,"area":null},{"id":"2504.09283","title":"Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale","created":"2025-04-12","cited_by_count":3,"area":null},{"id":"2605.14175","title":"Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations","created":"2026-05-13","cited_by_count":3,"area":null},{"id":"2510.12699","title":"Generation Space Size: Understanding and Calibrating Open-Endedness of LLM Generations","created":"2025-10-14","cited_by_count":2,"area":null},{"id":"2605.04454","title":"Deployment-Relevant Alignment Cannot Be Inferred from Model-Level Evaluation Alone","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2606.15766","title":"Rethinking Scaffolding in LLM Tutors: The Interactional Mismatch Between Benchmarks and Real-World Deployments","created":"2026-06-14","cited_by_count":1,"area":null},{"id":"2609.12373","title":"Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue","created":"2026-09-11","cited_by_count":1,"area":null},{"id":"2609.21117","title":"From Task Success to Productive Success: Evaluating Human-AI Collaboration by Quality and Cost","created":"2026-09-17","cited_by_count":1,"area":null},{"id":"2509.11295","title":"The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences","created":"2025-09-14","cited_by_count":0,"area":null},{"id":"2604.21827","title":"Alignment has a Fantasia Problem","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2604.22109","title":"Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations","created":"2026-04-23","cited_by_count":0,"area":null},{"id":"2608.08210","title":"Illusion of Alignment: Detecting Hidden Disagreement in Collaborative Dialogue","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.23978","title":"When Seeing Is Not Enough: Benchmarking Interactive Visual Grounding in LVLMs","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2608.29591","title":"How You Ask Shapes What You Get: A Theory-Seeded Measurement of Articulation in Advice-Seeking LLM Conversations","created":"2026-08-30","cited_by_count":0,"area":null},{"id":"2609.22607","title":"Pretrained Persona Mixture Models and Tandem Models for Human Simulation","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.35953","title":"Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.39518","title":"Referential Uncertainty in Human--AI Collaboration","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.05534","title":"Dataset Signatures in Human-LLM Interactions and User Modeling","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}