1 citations · 2 across the 10 of their papers we have counts for
6 papers · 1 filter
Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning
Cai Zhou, Zekai Wang, Menghua Wu +6
While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute costs. These inefficiencies can be at…
Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation
Cai Zhou, Zijie Chen, Zian Li +7
Many generative tasks in chemistry and science involve distributions invariant to group symmetries (e.g., permutation and rotation). A common strategy enforces invariance and equiv…
On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond
Chenxiao Yang, Cai Zhou, David Wipf +1
Diffusion language models have recently emerged as a competitive alternative to autoregressive language models. Beyond next-token generation, they are more efficient and flexible b…
Learning Diffusion Models with Flexible Representation Guidance
Chenyu Wang, Cai Zhou, Sharut Gupta +4
Diffusion models can be improved with additional guidance towards more effective representations of input. Indeed, prior empirical work has already shown that aligning internal rep…
Thought calibration: Efficient and confident test-time scaling
Menghua Wu, Cai Zhou, Stephen Bates +1
Reasoning large language models achieve impressive test-time scaling by thinking for longer, but this performance gain comes at significant compute cost. Directly limiting test-tim…
FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model
ZiDong Wang, Zeyu Lu, Di Huang +4
\textit{Nature is infinitely resolution-free}. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing imag…