5 papers
Consilience for Verifier-Free Test-Time Scaling
Lecheng Kong, Like Hui, Haitao Mao +1
Test-time scaling often uses an external verifier, such as compilers and test cases in coding or trained value functions in robotics applications, to obtain high-quality rollouts.…
Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs
Lecheng Kong, Xiyuan Wang, Yixin Chen +1
Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like reaction prediction and retrosynthesis. Howe…
Dynamic Mixture-of-Experts for Incremental Graph Learning
Lecheng Kong, Theodore Vasiloudis, Seongjun Yun +2
Graph incremental learning is a learning paradigm that aims to adapt trained models to continuously incremented graphs and data over time without the need for retraining on the ful…
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
Lecheng Kong, Jiarui Feng, Hao Liu +4
Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text…
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
Jiarui Feng, Hao Liu, Lecheng Kong +3
In this report, we present TAGLAS, an atlas of text-attributed graph (TAG) datasets and benchmarks. TAGs are graphs with node and edge features represented in text, which have rece…