Search papers, labs, and topics across Lattice.
Institute of Automation, Chinese Academy of Sciences
3
3
8
10
Omni-modal foundation models achieve self-improvement via RL compute scaled to 1M context, leveraging asynchronous training and agentic grading for enhanced intelligence.
Coding agents falter in collaborative settings, with a 7.7% drop in task resolution when users modify code, revealing a crucial gap in their adaptability.
LLMs can be made to reason much better by directly optimizing their pre-training output distribution, even before fine-tuning on specific tasks.
