43 citations · 51 across the 14 of their papers we have counts for
3 papers · 1 filter
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
Ha Minh Hieu, Tongyao Zhu, Do Xuan Long +1
LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while und…
When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
AI Collaboration, Jiaying Wu, Caleb Ziems +19
We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public…
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
Yuxi Xie, Anirudh Goyal, Wenyue Zheng +4
We introduce an approach aimed at enhancing the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process inspired by the successful s…