News

Recent updates from my research, open-source work, and the DreamX team. This activity log connects papers, released code, product-facing AI systems, and hiring signals to DreamX's broader spatial-intelligence mission. Public releases remain available through the AMAP-ML GitHub organization.

Recent papers Agents, world models, spatial AI, and generative AI
Open releases 40+ reproducible DreamX projects
Product reach 300M+ daily users

Latest DreamX Updates

These items are curated from recent DreamX releases and paper/project updates, with the newest and most product-relevant signals first.

Selected GitHub Portfolio

The AMAP-ML GitHub organization hosts the DreamX open-source portfolio. The work is organized around three core problems — understand and predict, generate and simulate, and plan and act — supported by a shared foundation of spatial data, multimodal models, reinforcement learning, infrastructure, and evaluation. Selected flagship releases:

Agents
Agentic skill evolution from real interaction traces.
Reasoning RL
A minimalist group policy gradient baseline for model reasoning.
Computer-Use Agents
Verified long-horizon computer use through durable task state and a Manage-Execute-Audit loop.
World Models
A general-purpose interactive world model for controllable world simulation.
Generative AI
Scene-text editing for controllable visual asset generation.
Spatial AI
Route-planning agent evaluation grounded in real-world mobility scenarios.

Full release index: visit github.com/AMAP-ML for the complete repository list, pinned releases, project pages, and hiring notes.


2025


Earlier Highlights