activity
20182026
most citedSingle-Shot Bidirectional Pyramid Networks for High-Quality Object Detection

11 citations · 15 across the 11 of their papers we have counts for

collaborators
Showing 2026Show all

6 papers · 1 filter

cs.CV2026

From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment

Aoting Zhang, Mingze Gao, Dongbao Yang +5

Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. Howe…

cs.CL2026

When Metrics Reward the Worst Translations: Internalizing Cultural Reasoning for Social Media Translation Evaluation

Yiwen Qiu, Linjuan Wu, Dingming Li +7

Automatic translation quality metrics trained on general-domain corpora systematically fail on social media content, where communicative intent is encoded in culturally loaded expr…

cs.CL2026

Beyond Rubrics: Exploration-Guided Evaluation Skills for Reward Modeling

Xing Yue, Linjuan Wu, Daoxin Zhang +2

Open-ended reward modeling requires judges that can follow subtle, domain-specific preferences when verifiable answers are unavailable. Existing rubric-based methods often address…

cs.CL2026

Beyond Literal Translation: Evaluating Cultural Effectiveness in Social Media UGC

Linjuan Wu, Ruiqi Zhang, Xinze Lyu +7

Social media platforms enable large-scale cross-lingual communication, but translating user-generated content (UGC) remains challenging due to its informal style, cultural referenc…

cs.LG2026

HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control

Xincheng Yao, Ruoqi Li, Cheng Chen +4

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Models (LLMs). However, the de f…

cs.CL2026

Pause or Fabricate? Training Language Models for Grounded Reasoning

Yiwen Qiu, Linjuan Wu, Yizhou Liu +9

Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are incomplete, producing confi…