1 citations · 1 across the 8 of their papers we have counts for
11 papers
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
Kuan Zhang, Yukun Chen, Zhihao Yang +8
Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these…
Video-Arena: A Scalable Agentic Video Benchmark for Spatiotemporal Capability
Kuan Zhang, Yukun Chen, Zhihao Yang +7
Understanding and tracking a dynamic world are fundamental to visual intelligence. Existing video benchmarks provide limited insight into whether models can maintain a coherent und…
Learning Multimodal Embeddings with Evidence-Aligned Readout
Zirong Chen, Fuda Ye, Enjun Du +8
Multimodal large language models can expose task-relevant evidence through generation, but producing useful evidence does not by itself determine how it enters a retrieval embeddin…
SnapBench: Benchmarking Snap-and-Ask Multimodal Retrieval for Mobile Interactions
Zirong Chen, Fuda Ye, Kuan Zhang +7
Mobile AI acts as a visual oracle, empowering users to snap a picture of something and ask for information. Snap-and-ask retrieval is now one of the most common entry points for mo…
EviRank: Structured Relevance Evidence for Multimodal Image Re-ranking
Enjun Du, Siyi Liu, Zirong Chen +8
Real-world image search queries are multimodal and compositional: ``find this shirt in pink'' specifies an entity to retain, an attribute to modify, and context to ignore. Yet exis…
LEDGERMIND: Provenance-Constrained Multimodal Agentic Reasoning with a Structured Evidence Ledger
Enjun Du, Hange Zhou, Chenxu Du +4
Multimodal agents for visual question answering increasingly operate as multi-step trajectories that interleave perception, retrieval, and reasoning, yet evaluation still largely r…
Patch the Distribution Mismatch: RL Rewriting Agent for Stable Off-Policy SFT
Jiacheng Wang, Ping Jian, Zhijie Liu +5
Large language models are commonly adapted to downstream tasks through supervised fine-tuning (SFT), but substantial distribution mismatch between downstream supervision and a mode…
What Should I Cite? A RAG Benchmark for Academic Citation Prediction
Leqi Zheng, Jiajun Zhang, Canzhi Chen +13
With the rapid growth of Web-based academic publications, more and more papers are being published annually, making it increasingly difficult to find relevant prior work. Citation…
LogicPuzzleRL: Cultivating Robust Mathematical Reasoning in LLMs via Reinforcement Learning
Zhen Hao Wong, Jingwen Deng, Runming He +7
Large language models (LLMs) excel at many supervised tasks but often struggle with structured reasoning in unfamiliar settings. This discrepancy suggests that standard fine-tuning…
EVQAScore: A Fine-grained Metric for Video Question Answering Data Quality Evaluation
Hao Liang, Zirong Chen, Hejun Dong +1
Video question-answering (QA) is a core task in video understanding. Evaluating the quality of video QA and video caption data quality for training video large language models (Vid…
MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts
Hao Liang, Linzhuang Sun, Minxuan Zhou +7
With the rapid progress of Multimodal LLMs, evaluating their mathematical reasoning capabilities has become an increasingly important research direction. In particular, visual-text…