Awesome Lists
Curated open-source research maps maintained under OpenEnvision, spanning multimodal modeling and visual-agent systems.
OpenEnvision
A structured survey for multimodal models, covering MLLMs, unified multimodal models, native multimodal models, and adjacent awesome lists.
Scope
Image-text centered multimodal modeling with annotations for video, audio, 3D, and omni extensions.
Focus
Architecture-first categorization, model taxonomy, and links to papers, code, demos, and related lists.
A curated research index for visual agents that perceive, ground, plan, act, create, and evaluate in visually grounded environments.
Scope
GUI agents, computer-use agents, embodied VLA systems, visual reasoning loops, and visual-agent safety.
Focus
Research taxonomy, reading pathways, benchmarks, environments, tools, and engineering resources.
Codebase
Open-source codebases where I participated as a contributor, supporting reproducible research infrastructure and community use.
OpenDCAI
Participated in OpenDCAI's unified world-model codebase, which standardizes model invocation, documents advanced world-model research, and integrates reusable pipelines for perception, reasoning, generation, and interaction.
Role
Participated as a contributor to the project rather than as the primary lead.
Focus
Unified framework design, method integration, documentation, and reusable scripts for advanced world-model research.
Knowledge Sharing Community
Community infrastructure for discovering, sharing, and curating high-signal AI research writing and optimization resources.
OpenEnvision
Knowledge-sharing community and discovery platform for curated AI research blogs, helping researchers find durable technical writing across models, agents, safety, efficiency, and world models.
Purpose
Curates high-quality AI research blogs and technical insights for researchers and builders.
Signals
Displays GitHub stars and live pageviews so the community footprint is visible beyond repository activity.
Knowledge-sharing community for large-scale optimization, helping researchers discover optimizer algorithms, compare training strategies, and follow practical resources for efficient deep learning.
Purpose
Collects optimizer-centric resources for large-scale machine learning, including algorithms, papers, tutorials, and implementation references.
Signals
Displays GitHub stars and live pageviews so both repository attention and website community traffic are visible.