Junlin Yang
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@junlin45300
Joined April 2024
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🚀 OpenRSI is a new open research series from
@FrontisAIfor concrete, testable progress toward recursive self-improvement (RSI). As its first project—and also my first work as first author—I’m proud to present OpenMLE: an open full-stack AI4AI system for autoresearch, where
Crazy
Introducing GEN-1.5, a one-shot learner. It can learn new tasks in a few seconds. Show it what to do, and it generalizes. This capability emerged from pretraining on physical data at scale, as a step towards our mission of building general intelligence for the physical world.
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Thank you JIQIZHIXIN!
Can one AI system build a better AI than itself, in a loop that never stops? Researchers from Horizon Research and
@Tsinghua_Unipresent Frontis-MA1. They built an open system called OpenMLE that acts like a gym for AI engineers. Instead of just learning from text, the AI
To me, MLS-Bench is very much in the “AI trains AI” direction: asking whether LMs can help improve AI systems in a setting that feels close to how AI research is actually done today, and testing that question carefully. Check out
@Lyubh22's thread:
Everyone is talking about self-evolving AI, or recursive self-improvement. The methods that built modern ML are the ones that keep working across settings and scales, yet no benchmark directly tests AI systems for that ability. Today, after months of cross-platform validation
Agent world modeling has long been a dream many of us believed in, but making it work was a massive challenge. This work is a systematic step toward world models as simulators and planning tools for agents. Seeing it finally come together is truly exciting. Congrats to the team🙌
📣📣 Meet Qwen-AgentWorld — a native language world model that simulates 7 agent environments (MCP, Search, Terminal, SWE, Web, OS, Android) within a single model. Environment modeling is the training objective from day one, not a post-hoc adaptation. 🤔 LLMs are trained to be
