{"total":19,"rows":[{"id":"2510.07077","title":"Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications","created":"2025-10-08","cited_by_count":84,"area":"mllm"},{"id":"2508.21046","title":"CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification","created":"2025-08-28","cited_by_count":74,"area":"mllm"},{"id":"2509.19012","title":"Pure Vision Language Action (VLA) Models: A Comprehensive Survey","created":"2025-09-23","cited_by_count":49,"area":"mllm"},{"id":"2601.03309","title":"VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models","created":"2026-01-06","cited_by_count":39,"area":"mllm"},{"id":"2510.25616","title":"Don't Blind Your VLA: Aligning Visual Representations for OOD Generalization","created":"2025-10-29","cited_by_count":35,"area":"mllm"},{"id":"2512.11218","title":"Seeing to Act, Prompting to Specify: A Bayesian Factorization of Vision Language Action Policy","created":"2025-12-12","cited_by_count":18,"area":"mllm"},{"id":"2602.15397","title":"ActionCodec: What Makes for Good Action Tokenizers","created":"2026-02-17","cited_by_count":18,"area":"mllm"},{"id":"2510.01711","title":"Contrastive Representation Regularization for Vision-Language-Action Models","created":"2025-10-02","cited_by_count":17,"area":"mllm"},{"id":"2602.10109","title":"ST4VLA: Spatially Guided Training for Vision-Language-Action Models","created":"2026-02-10","cited_by_count":9,"area":"mllm"},{"id":"2511.19878","title":"MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization","created":"2025-11-25","cited_by_count":7,"area":"mllm"},{"id":"2607.13429","title":"Generalizable VLA Finetuning via Representation Anchoring and Language-Action Alignment","created":"2026-07-15","cited_by_count":6,"area":"mllm"},{"id":"2606.12366","title":"APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies","created":"2026-06-10","cited_by_count":5,"area":"mllm"},{"id":"2609.29389","title":"Robo-Harness K1: Harnessing Robot-Use Agents via Perception Augmentation","created":"2026-09-24","cited_by_count":3,"area":"mllm"},{"id":"2605.24890","title":"QuoVLA: Quotient Space for Vision-Language-Action Models","created":"2026-05-24","cited_by_count":2,"area":"mllm"},{"id":"2609.06079","title":"LayerRoute: Action-Conditioned Mixture-of-Layers Routing for Vision-Language-Action Policies","created":"2026-09-05","cited_by_count":2,"area":"mllm"},{"id":"2604.16067","title":"AEGIS: Anchor-Enforced Gradient Isolation for Knowledge-Preserving Vision-Language-Action Fine-Tuning","created":"2026-04-17","cited_by_count":0,"area":"mllm"},{"id":"2605.29577","title":"Mitigating State Aliasing in Vision-Language-Action Models via Inverse Dynamics Learning","created":"2026-05-28","cited_by_count":0,"area":"mllm"},{"id":"2608.08904","title":"From Recovery to Drop-off: How Action Post-training Reduces a VLM's Late-Layer Depth Decodability","created":"2026-08-09","cited_by_count":0,"area":"mllm"},{"id":"2609.08224","title":"3DWay: Generalizing Robot Manipulation via 3D Consistent Waypoints","created":"2026-09-08","cited_by_count":0,"area":"mllm"}],"next_cursor":null}