4 papers
Learning to Discover at Test Time
Mert Yuksekgonul, Daniel Koceja, Xinhao Li +8
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We p…
End-to-End Test-Time Training for Long Context
Arnuv Tandon, Karan Dalal, Xinhao Li +11
We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Tra…
Goal-Directed Search Outperforms Goal-Agnostic Memory Compression in Long-Context Memory Tasks
Yicong Zheng, Kevin L. McKee, Thomas Miconi +3
How to enable human-like long-term memory in large language models (LLMs) has been a central question for unlocking more general capabilities such as few-shot generalization. Exist…
Thinking agents for zero-shot generalization to qualitatively novel tasks
Thomas Miconi, Kevin McKee, Yicong Zheng +1
Intelligent organisms can solve truly novel problems which they have never encountered before, either in their lifetime or their evolution. An important component of this capacity…