{"total":34,"rows":[{"id":"2311.03658","title":"The Linear Representation Hypothesis and the Geometry of Large Language Models","created":"2023-11-07","cited_by_count":716,"area":null},{"id":"2403.03867","title":"On the Origins of Linear Representations in Large Language Models","created":"2024-03-06","cited_by_count":79,"area":null},{"id":"2308.03977","title":"PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning","created":"2023-08-08","cited_by_count":31,"area":null},{"id":"2402.09236","title":"Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models","created":"2024-02-14","cited_by_count":29,"area":null},{"id":"2406.04331","title":"PaCE: Parsimonious Concept Engineering for Large Language Models","created":"2024-06-06","cited_by_count":19,"area":null},{"id":"2407.08521","title":"Emergent Visual-Semantic Hierarchies in Image-Text Representations","created":"2024-07-11","cited_by_count":19,"area":null},{"id":"2307.05134","title":"TIAM -- A Metric for Evaluating Alignment in Text-to-Image Generation","created":"2023-07-11","cited_by_count":18,"area":null},{"id":"2310.17611","title":"Uncovering Meanings of Embeddings via Partial Orthogonality","created":"2023-10-26","cited_by_count":15,"area":null},{"id":"2410.14817","title":"A Complexity-Based Theory of Compositionality","created":"2024-10-18","cited_by_count":13,"area":null},{"id":"2507.07102","title":"Does Data Scaling Lead to Visual Compositional Generalization?","created":"2025-07-09","cited_by_count":13,"area":null},{"id":"2311.16254","title":"Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models","created":"2023-11-27","cited_by_count":12,"area":null},{"id":"2510.12066","title":"AI Agents as Universal Task Solvers","created":"2025-10-14","cited_by_count":9,"area":null},{"id":"2504.13938","title":"Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection","created":"2025-04-15","cited_by_count":8,"area":null},{"id":"2406.18534","title":"Towards Compositionality in Concept Learning","created":"2024-06-26","cited_by_count":7,"area":null},{"id":"2604.03340","title":"Learning Additively Compositional Latent Actions for Embodied AI","created":"2026-04-03","cited_by_count":7,"area":null},{"id":"2412.06646","title":"The Narrow Gate: Localized Image-Text Communication in Native Multimodal Models","created":"2024-12-09","cited_by_count":4,"area":null},{"id":"2507.03683","title":"On the rankability of visual embeddings","created":"2025-07-04","cited_by_count":4,"area":null},{"id":"2407.05118","title":"SHINE: Saliency-aware HIerarchical NEgative Ranking for Compositional Temporal Grounding","created":"2024-07-06","cited_by_count":3,"area":null},{"id":"2405.18774","title":"LLaMA-Reg: Using LLaMA 2 for Unsupervised Medical Image Registration","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2411.10019","title":"Towards Utilising a Range of Neural Activations for Comprehending Representational Associations","created":"2024-11-15","cited_by_count":2,"area":null},{"id":"2508.12919","title":"7Bench: a Comprehensive Benchmark for Layout-guided Text-to-image Models","created":"2025-08-18","cited_by_count":2,"area":null},{"id":"2512.17178","title":"ABE-CLIP: Training-Free Attribute Binding Enhancement for Compositional Image-Text Matching","created":"2025-12-19","cited_by_count":2,"area":null},{"id":"2407.08934","title":"Compositional Structures in Neural Embedding and Interaction Decompositions","created":"2024-07-12","cited_by_count":1,"area":null},{"id":"2410.01865","title":"Simplifying complex machine learning by linearly separable network embedding spaces","created":"2024-10-02","cited_by_count":1,"area":null},{"id":"2507.04754","title":"Intervening to Learn and Compose Causally Disentangled Representations","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2510.09790","title":"Mapping Semantic & Syntactic Relationships with Geometric Rotation","created":"2025-10-10","cited_by_count":1,"area":null},{"id":"2511.01932","title":"Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models","created":"2025-11-02","cited_by_count":1,"area":null},{"id":"2512.10669","title":"Learning by Analogy: A Causal Framework for Composition Generalization","created":"2025-12-11","cited_by_count":1,"area":null},{"id":"2602.11448","title":"Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2512.18864","title":"Cross-modal Counterfactual Explanations: Uncovering Decision Factors and Dataset Biases in Subjective Classification","created":"2025-12-21","cited_by_count":0,"area":null},{"id":"2602.16545","title":"Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2605.11203","title":"How Far Does a Shared Linear Map Go? Probing Feature-Space Manipulability for Image Editing","created":"2026-05-11","cited_by_count":0,"area":null},{"id":"2605.31503","title":"How can embedding models bind concepts?","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2608.07353","title":"Geo-Spatial Concept Probing of Large Language Models: Abstraction, Compositionality, and Grounding","created":"2026-08-07","cited_by_count":0,"area":null}],"next_cursor":null}