activity
20192026
most citedGraph Wavelet Neural Network

78 citations · 230 across the 36 of their papers we have counts for

collaborators
Showing 2025Show all

7 papers · 1 filter

cs.CL2025

Multi-Personality Generation of LLMs at Decoding-time

Rongxin Chen, Yunfan Li, Yige Yuan +2

Multi-personality generation for LLMs, enabling simultaneous embodiment of multiple personalization attributes, is a fundamental challenge. Existing retraining-based approaches are…

cs.CL2025

From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment

Bin Xie, Bingbing Xu, Yige Yuan +2

Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However,…

cs.AI2025

KnowCoder-V2: Deep Knowledge Analysis

Zixuan Li, Wenxuan Liu, Long Bai +13

Deep knowledge analysis tasks always involve the systematic extraction and association of knowledge from large volumes of data, followed by logical reasoning to discover insights.…

cs.LG2025★ 4 cited

InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning

Zixu Wang, Bingbing Xu, Yige Yuan +2

As an important graph pre-training method, Graph Contrastive Learning (GCL) continues to play a crucial role in the ongoing surge of research on graph foundation models or LLM as e…

cs.CL2025

Inference-time Alignment in Continuous Space

Yige Yuan, Teng Xiao, Li Yunfan +5

Aligning large language models with human feedback at inference time has received increasing attention due to its flexibility. Existing methods rely on generating multiple response…

cs.CL2025

Incentivizing Strong Reasoning from Weak Supervision

Yige Yuan, Teng Xiao, Shuchang Tao +4

Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on either reinforcement…

cs.CV2025★ 4 cited

MIGE: Mutually Enhanced Multimodal Instruction-Based Image Generation and Editing

Xueyun Tian, Wei Li, Bingbing Xu +3

Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them…