5 papers
Training AI Scientists to Replicate Research
Damon Falck, Samer Sabri, Anja Surina +8
The replicability of papers is a cornerstone of scientific knowledge, ensuring the reliability of existing results and providing a base for further experiments. The act of replicat…
Epiplexity Guided Data Selection and Generation for Out-of-Distribution Generalization
Ellen Su, Andres Potapczynski, Shikai Qiu +2
Modern systems are increasingly expected to transfer across tasks not specified during training. What data facilitates generalization in these new, unanticipated settings? One hypo…
SPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms
Alex Havrilla, Edward Hughes, Mikayel Samvelyan +1
Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large…
Neural Contextual Reinforcement Framework for Logical Structure Language Generation
Marcus Irvin, William Cooper, Edward Hughes +2
The Neural Contextual Reinforcement Framework introduces an innovative approach to enhancing the logical coherence and structural consistency of text generated by large language mo…
Cultural Evolution of Cooperation among LLM Agents
Aron Vallinder, Edward Hughes
Large language models (LLMs) provide a compelling foundation for building generally-capable AI agents. These agents may soon be deployed at scale in the real world, representing th…