{"total":69,"rows":[{"id":"2505.13995","title":"ELEPHANT: Measuring and understanding social sycophancy in LLMs","created":"2025-05-20","cited_by_count":129,"area":null},{"id":"2502.08177","title":"SycEval: Evaluating LLM Sycophancy","created":"2025-02-12","cited_by_count":108,"area":null},{"id":"2505.23840","title":"Measuring Sycophancy of Language Models in Multi-turn Dialogues","created":"2025-05-28","cited_by_count":104,"area":null},{"id":"2504.02234","title":"LLM Social Simulations Are a Promising Research Method","created":"2025-04-03","cited_by_count":99,"area":null},{"id":"2508.02087","title":"When Truth Is Overridden: Uncovering the Internal Origins of Sycophancy in Large Language Models","created":"2025-08-04","cited_by_count":50,"area":null},{"id":"2509.03730","title":"The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs","created":"2025-09-03","cited_by_count":36,"area":null},{"id":"2503.11656","title":"TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models","created":"2025-02-04","cited_by_count":33,"area":null},{"id":"2510.04721","title":"BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs","created":"2025-10-06","cited_by_count":27,"area":null},{"id":"2602.19141","title":"Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians","created":"2026-02-22","cited_by_count":21,"area":null},{"id":"2508.13743","title":"Sycophancy under Pressure: Evaluating and Mitigating Sycophantic Bias via Adversarial Dialogues in Scientific QA","created":"2025-08-19","cited_by_count":18,"area":null},{"id":"2605.21778","title":"What Counts as AI Sycophancy? A Taxonomy and Expert Survey of a Fragmented Construct","created":"2026-05-20","cited_by_count":16,"area":null},{"id":"2504.11373","title":"Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions","created":"2025-04-15","cited_by_count":15,"area":null},{"id":"2506.21521","title":"Potemkin Understanding in Large Language Models","created":"2025-06-26","cited_by_count":15,"area":null},{"id":"2508.17450","title":"Persuasion Dynamics in LLMs: Investigating Robustness and Adaptability in Knowledge and Safety with DuET-PD","created":"2025-08-24","cited_by_count":13,"area":null},{"id":"2604.02423","title":"SWAY: A Counterfactual Computational Linguistic Approach to Measuring and Mitigating Sycophancy","created":"2026-04-02","cited_by_count":8,"area":null},{"id":"2506.10297","title":"\"Check My Work?\": Measuring Sycophancy in a Simulated Educational Context","created":"2025-06-12","cited_by_count":7,"area":null},{"id":"2510.01395","title":"Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence","created":"2025-10-01","cited_by_count":6,"area":null},{"id":"2604.26965","title":"The Impact of AI-Generated Text on the Internet","created":"2026-04-14","cited_by_count":6,"area":null},{"id":"2605.23932","title":"When Correct Beliefs Collapse: Epistemic Resilience of LLMs under Clinical Pressure","created":"2026-04-23","cited_by_count":6,"area":null},{"id":"2504.08812","title":"Mechanistic Anomaly Detection for \"Quirky\" Language Models","created":"2025-04-09","cited_by_count":5,"area":null},{"id":"2510.13912","title":"AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs","created":"2025-10-15","cited_by_count":4,"area":null},{"id":"2606.06306","title":"Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustness","created":"2026-06-04","cited_by_count":4,"area":null},{"id":"2506.20666","title":"Cognitive models can reveal interpretable value trade-offs in language models","created":"2025-06-25","cited_by_count":3,"area":null},{"id":"2509.12158","title":"Pun Unintended: LLMs and the Illusion of Humor Understanding","created":"2025-09-15","cited_by_count":3,"area":null},{"id":"2512.08937","title":"When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being","created":"2025-10-24","cited_by_count":3,"area":null},{"id":"2603.00801","title":"The Synthetic Web: Adversarially-Curated Mini-Internets for Diagnosing Epistemic Weaknesses of Language Agents","created":"2026-02-28","cited_by_count":3,"area":null},{"id":"2603.15423","title":"Invisible failures in human-AI interactions","created":"2026-03-16","cited_by_count":3,"area":null},{"id":"2510.21180","title":"Social Simulations with Large Language Model Risk Utopian Illusion","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2602.04003","title":"When AI Persuades: Adversarial Explanation Attacks on Human Trust in AI-Assisted Decision Making","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2604.05051","title":"This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA","created":"2026-04-06","cited_by_count":2,"area":null},{"id":"2604.15316","title":"Anthropomorphism and Trust in Human-Large Language Model interactions","created":"2026-03-01","cited_by_count":2,"area":null},{"id":"2604.23593","title":"When AI reviews science: Can we trust the referee?","created":"2026-04-26","cited_by_count":2,"area":null},{"id":"2504.00012","title":"I'm Sorry Dave: How the old world of personnel security can inform the new world of AI insider risk","created":"2025-03-26","cited_by_count":1,"area":null},{"id":"2505.07846","title":"Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Models","created":"2025-05-07","cited_by_count":1,"area":null},{"id":"2511.07669","title":"Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2601.21439","title":"The Paradox of Robustness: Decoupling Rule-Based Logic from Affective Noise in High-Stakes Decision-Making","created":"2026-01-29","cited_by_count":1,"area":null},{"id":"2602.11244","title":"Stress Tests REVEAL Fragile Temporal and Visual Grounding in Video-Language Models","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2603.03970","title":"Generative AI for Managerial Decision-Making under Ambiguity and Sycophancy","created":"2026-03-04","cited_by_count":1,"area":null},{"id":"2604.26851","title":"Resume-ing Control: (Mis)Perceptions of Agency Around GenAI Use in Recruiting Workflows","created":"2026-04-29","cited_by_count":1,"area":null},{"id":"2605.07462","title":"The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2605.23952","title":"Machine Psychometrics: A Mathematical Psychology of Artificial Intelligence","created":"2026-05-10","cited_by_count":1,"area":null},{"id":"2606.09068","title":"Emergent Misalignment Can Be Induced by Sycophancy and Reversed via Alignment Gating","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2506.16653","title":"LLMs in Coding and their Impact on the Commercial Software Engineering Landscape","created":"2025-06-19","cited_by_count":0,"area":null},{"id":"2509.22926","title":"Large language models management of medications: three performance analyses","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2512.10961","title":"AI as Equalizer or Amplifier? Task Complexity as the Moderating Factor for Human Expertise in Hybrid Intelligence Systems","created":"2025-10-30","cited_by_count":0,"area":null},{"id":"2601.03285","title":"Feedback Indices to Evaluate LLM Responses to Rebuttals for Multiple Choice Type Questions","created":"2026-01-02","cited_by_count":0,"area":null},{"id":"2601.05529","title":"Before We Trust Them: Decision-Making Failures in Navigation of Foundation Models","created":"2026-01-09","cited_by_count":0,"area":null},{"id":"2601.06056","title":"Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications","created":"2025-12-22","cited_by_count":0,"area":null},{"id":"2601.17577","title":"Status Hierarchies in Language Models","created":"2026-01-24","cited_by_count":0,"area":null},{"id":"2602.11924","title":"Who Does What? Archetypes of Roles Assigned to LLMs During Human-AI Decision-Making","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2602.20400","title":"Three Concrete Challenges and Two Hopes for the Safety of Unsupervised Elicitation","created":"2026-02-23","cited_by_count":0,"area":null},{"id":"2603.04299","title":"The Company You Keep: How LLMs Respond to Dark Triad Traits","created":"2026-03-04","cited_by_count":0,"area":null},{"id":"2604.07369","title":"The Role of Emotional Stimuli and Intensity in Shaping Large Language Model Behavior","created":"2026-04-07","cited_by_count":0,"area":null},{"id":"2604.09620","title":"LLM Nepotism in Organizational Governance","created":"2026-03-18","cited_by_count":0,"area":null},{"id":"2605.03261","title":"Can AI Help You Get Over Your Breakup? One Session with a Belief-Reframing Chatbot Shows Sustained Distress Reduction","created":"2026-05-05","cited_by_count":0,"area":null},{"id":"2605.28188","title":"Framing Matters: Addressing Framing Sensitivity in Decision-Making through Behaviorally-Grounded Value Alignment","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2605.30187","title":"Modularizing Educational LLM-Agency for Fostering Responsible Learning Assistance","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2606.02973","title":"Chatbots Output Meaningful (but Problematic) Language","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.03096","title":"Can Factual Opinions Be Edited (Manipulated) in Large Language Models?","created":"2026-06-02","cited_by_count":0,"area":null},{"id":"2606.08629","title":"Sycophancy Towards Researchers Drives Performative Misalignment","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2606.15601","title":"SCAN: A Decision-Making Framework for Effective Task Allocation with Generative AI","created":"2026-06-14","cited_by_count":0,"area":null},{"id":"2606.30219","title":"EvalSafetyGap: A Hybrid Survey and Conceptual Framework for LLM Evaluation-Safety Failures","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2607.15957","title":"From Plausible to Actionable: A Position on LLM Self-Explanations","created":"2026-07-17","cited_by_count":0,"area":null},{"id":"2608.21409","title":"Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?","created":"2026-08-10","cited_by_count":0,"area":null},{"id":"2608.25267","title":"Mitigating LLM sycophancy with RL-based fine-tuning: Bayesian Truth Serum approach","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2608.25894","title":"From Passive Response to Proactive Correction: Enhancing LLM Robustness Against Input Fact Perturbations","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2608.29198","title":"How Identity and Opinion Shape Political Sycophancy in LLMs","created":"2026-08-29","cited_by_count":0,"area":null},{"id":"2609.00250","title":"CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2610.18159","title":"Claim Framing as a Driver of LLM Sycophancy","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}