VIEWCRAFT: Evolving What Multimodal Agents See and How They Read It
cs.CV
Submitted: 2026-10-11
Updated: 2026-10-11
Sources
- Dynamo: Dynamic Skill-Tool Evolution for Vision-Language Agents
- SkillOpt: Executive Strategy for Self-Evolving Agent Skills
- Agent Skills Should Go Beyond Text: The Case for Visual Skills
- MMSkills: Towards Multimodal Skills for General Visual Agents
- XSkill: Continual Learning from Experience and Skills in Multimodal Agents
- EpiCon: Collective Agent Learning through Co-Evolving Multimodal Memory
- Voyager: An Open-Ended Embodied Agent with Large Language Models
- Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding in GPT-4V
- Visual Sketchpad: Sketching as a Visual Chain of Thought for Multimodal Language Models
- Frame-Voyager: Learning to Query Frames for Video Large Language Models
- MementoGUI: Learning Agentic Multimodal Memory Control for Long-Horizon GUI Agents
- WikiSkill: Compiling Agent Experience into Persistent Knowledge for Skill Evolution
- MMCOMPOSITION: Revisiting the Compositionality of Pre-trained Vision-Language Models
- Video-Holmes: Can MLLM Think Like Holmes for Complex Video Reasoning?
- MC-LLaVA: Multi-Concept Personalized Vision-Language Model
- UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens
- Small-Large Collaboration: Training-efficient Concept Personalization for Large VLM using a Meta Personalized Small VLM
- Jarvis: Towards Personalized AI Assistant via Personal KV-Cache Retrieval
- M2A: Multimodal Memory Agent with Dual-Layer Hybrid Memory for Long-Term Personalized Interactions
- PEARL: Personalized Streaming Video Understanding Model
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