14 citations · 33 across the 10 of their papers we have counts for
4 papers · 1 filter
DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
Can Jin, Hongwu Peng, Mingcan Xiang +7
Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top- routing imposes a rigid sparsity pattern that ignores the int…
AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
Can Jin, Yang Zhou, Qixin Zhang +8
Test-time scaling strategies for Large Language Models predominantly rely on either reinforcement learning with sparse outcome rewards or search-based methods guided by static Proc…
From Hallucination to Reliability: Generative Modeling and the Structure of Scientific Inference
Charles Rathkopf
Generative AI is increasingly used in science, but is unavoidably prone to hallucination. I develop a reliabilist account of how generative AI nevertheless gives rise to new scient…
RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
Can Jin, Hongwu Peng, Anxiang Zhang +8
In an Information Retrieval (IR) system, reranking plays a critical role by sorting candidate passages according to their relevance to a specific query. This process demands a nuan…