{"total":22,"rows":[{"id":"2605.05115","title":"Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior","created":"2026-05-06","cited_by_count":26,"area":null},{"id":"2602.02315","title":"The Shape of Beliefs: Geometry, Dynamics, and Interventions along Representation Manifolds of Language Models' Posteriors","created":"2026-02-02","cited_by_count":15,"area":null},{"id":"2601.20834","title":"Linear representations in language models can change dramatically over a conversation","created":"2026-01-28","cited_by_count":10,"area":null},{"id":"2602.02343","title":"Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics","created":"2026-02-02","cited_by_count":9,"area":null},{"id":"2601.10567","title":"Generative AI collective behavior needs an interactionist paradigm","created":"2026-01-15","cited_by_count":6,"area":null},{"id":"2603.03308","title":"Old Habits Die Hard: How Conversational History Geometrically Traps LLMs","created":"2026-02-08","cited_by_count":6,"area":null},{"id":"2603.21398","title":"Persona Vectors in Games: Measuring and Steering Strategies via Activation Vectors","created":"2026-03-22","cited_by_count":6,"area":null},{"id":"2603.02578","title":"How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities","created":"2026-03-03","cited_by_count":3,"area":null},{"id":"2605.12412","title":"Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space","created":"2026-05-12","cited_by_count":3,"area":null},{"id":"2601.05905","title":"Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency","created":"2026-01-09","cited_by_count":2,"area":null},{"id":"2602.00533","title":"Convergent World Representations and Divergent Tasks","created":"2026-01-31","cited_by_count":2,"area":null},{"id":"2604.22027","title":"Shared Lexical Task Representations Explain Behavioral Variability In LLMs","created":"2026-04-23","cited_by_count":2,"area":null},{"id":"2605.28854","title":"Large language models reorganize representational geometry during in-context learning","created":"2026-05-16","cited_by_count":2,"area":null},{"id":"2606.12360","title":"Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal","created":"2026-06-10","cited_by_count":2,"area":null},{"id":"2604.23985","title":"Representational Curvature Modulates Behavioral Uncertainty in Large Language Models","created":"2026-04-27","cited_by_count":0,"area":null},{"id":"2605.08405","title":"Belief or Circuitry? Causal Evidence for In-Context Graph Learning","created":"2026-05-08","cited_by_count":0,"area":null},{"id":"2607.02668","title":"Improving LLMs via Validator-to-Generator Alignment","created":"2026-07-02","cited_by_count":0,"area":null},{"id":"2607.10578","title":"Laguerre Geometry for Interpreting Large Language Models","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2609.06851","title":"You Are What You Read: Misalignment via In-Context Persona Induction","created":"2026-09-06","cited_by_count":0,"area":null},{"id":"2609.20408","title":"Xeno-Interpretability: Investigating the Alien Minds of LLMs","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2610.00679","title":"Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow","created":"2026-09-30","cited_by_count":0,"area":null},{"id":"2610.19177","title":"Multi-System Cognitive Steering for Generative Communication","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}