29 citations · 60 across the 60 of their papers we have counts for
67 papers
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations
Akshay Malhotra, Nicolas Zilberstein, Shahab Hamidi-Rad
As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with lit…
CompassPlay: Rewarding the Proposer for Where It Moves the Solver
Sophia Xiao Pu, Ximeng Sun, Jiang Liu +4
In self-play, a proposer generates verifiable tasks to train a solver. Proposer rewards often depend on the solver's success rate, but equally difficult tasks can differ in their t…
Instella-MoE Technical Report
Jiang Liu, Sudhanshu Ranjan, Prakamya Mishra +10
In this work, we introduce Instella-MoE, a fully open Mixture-of-Experts (MoE) language model with 16 billion total parameters and 2.8 billion active parameters per token, trained…
VEGAS: Human-Aligned Video Caption Evaluation via Gaze
Shenghui Chen, Po-han Li, Ximeng Sun +5
Vision-language models excel at video captioning, yet typically generate descriptions that fail to capture individual viewers' attention. We propose VEGAS (Video caption Evaluation…
Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation
Zekai Li, Ji Liu, Yiqing Huang +3
Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many steps are spent on redundan…
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
Zihao An, Taichi Liu, Ziqiong Liu +3
Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verified in parallel by the target m…