6 papers
Open-world AI: a Tale of Three Requirements
Jianyu Zhang, Léon Bottou
The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covere…
LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent
Bince Qu, Wanli Li, Bo Pan +5
Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains constrained by two coupled chall…
A Single Character can Make or Break Your LLM Evals
Jingtong Su, Jianyu Zhang, Karen Ullrich +2
Common Large Language model (LLM) evaluations rely on demonstration examples to steer models' responses to the desired style. While the number of examples used has been studied and…
Memory Mosaics at scale
Jianyu Zhang, Léon Bottou
Memory Mosaics [Zhang et al., 2025], networks of associative memories, have demonstrated appealing compositional and in-context learning capabilities on medium-scale networks (GPT-…
Memory Mosaics
Jianyu Zhang, Niklas Nolte, Ranajoy Sadhukhan +2
Memory Mosaics are networks of associative memories working in concert to achieve a prediction task of interest. Like transformers, memory mosaics possess compositional capabilitie…
Fine-tuning with Very Large Dropout
Jianyu Zhang, Léon Bottou
It is impossible today to pretend that the practice of machine learning is always compatible with the idea that training and testing data follow the same distribution. Several auth…