6 papers
Harnessing Large Language Models to Compile Task-Relevant Context into Bayesian Optimisation
Zhongwei Yu, Sourabh Roy, Bin Cao +3
Incorporating rich task-relevant context, such as domain knowledge and external observations, is a key capability yet remains challenging for Bayesian optimisation (BO). Recently,…
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
Zhongwei Yu, Yan Song, Xue Yan +9
Scientific discovery often involves optimising expensive-to-evaluate objectives over vast, structured, and open-ended hypothesis spaces, such as molecules, protein sequences, and c…
ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents
Anjie Liu, Yan Song, Zhixun Chen +3
Tool-augmented vision-language agents can acquire external perceptual evidence through OCR, detection, segmentation, and other tools, but executing every proposed tool call is cost…
Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Zhongwei Yu, Rasul Tutunov, Alexandre Max Maraval +13
Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often w…
Memento-Skills: Let Agents Design Agents
Huichi Zhou, Siyuan Guo, Anjie Liu +14
We introduce \emph{Memento-Skills}, a generalist, continually-learnable LLM agent system that functions as an \emph{agent-designing agent}: it autonomously constructs, adapts, and…
Self-Verifying Reflection Helps Transformers with CoT Reasoning
Zhongwei Yu, Wannian Xia, Xue Yan +4
Advanced large language models (LLMs) frequently reflect in reasoning chain-of-thoughts (CoTs), where they self-verify the correctness of current solutions and explore alternatives…