2 citations · 2 across the 12 of their papers we have counts for
12 papers
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
Jingbo Zhou, Yusai Zhao, Qi Bao +12
Large language model (LLM) agents are increasingly expected to assist users in completing tasks. However, existing benchmarks provide limited support for evaluating whether agents…
Clarify-Then-Search: A Clarification Benchmark for Deep Search with End-to-End Nugget Restoration
Deqiang Huang, Jingbo Zhou, Xinjiang Lu +3
Deep search is brittle on underspecified user queries: missing constraints such as time, location, scope, or definitions can lead to retrieval drift and incomplete answers. We intr…
Eureka-Audio: Triggering Audio Intelligence in Compact Language Models
Dan Zhang, Yishu Lei, Jing Hu +10
We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to 18 times larger across a broad range…
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning
Shuaiyi Nie, Siyu Ding, Wenyuan Zhang +7
Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redu…
OmegaUse: Building a General-Purpose GUI Agent for Autonomous Task Execution
Le Zhang, Yixiong Xiao, Xinjiang Lu +12
Graphical User Interface (GUI) agents show great potential for enabling foundation models to complete real-world tasks, revolutionizing human-computer interaction and improving hum…
Extending RLVR to Open-Ended Tasks via Verifiable Multiple-Choice Reformulation
Mengyu Zhang, Siyu Ding, Weichong Yin +2
Reinforcement Learning with Verifiable Rewards(RLVR) has demonstrated great potential in enhancing the reasoning capabilities of large language models (LLMs). However, its success…
RoboOmni: Proactive Robot Manipulation in Omni-modal Context
Siyin Wang, Jinlan Fu, Feihong Liu +11
Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many s…
Fusion to Enhance: Fusion Visual Encoder to Enhance Multimodal Language Model
Yifei She, Huangxuan Wu
Multimodal Large Language Models (MLLMs) have made significant progress in bridging visual perception with high-level textual reasoning. However, they face a fundamental contradict…
LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
Junhong Wu, Jinliang Lu, Zixuan Ren +4
Human cognition naturally engages with abstract and fluid concepts, whereas existing reasoning models often rely on generating discrete tokens, potentially constraining their expre…
StarVid: Enhancing Semantic Alignment in Video Diffusion Models via Spatial and SynTactic Guided Attention Refocusing
Yuanhang Li, Qi Mao, Lan Chen +5
Recent advances in text-to-video (T2V) generation with diffusion models have garnered significant attention. However, they typically perform well in scenes with a single object and…
CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Xiaoshuai Song, Muxi Diao, Guanting Dong +13
Large language models (LLMs) have demonstrated significant potential in advancing various fields of research and society. However, the current community of LLMs overly focuses on b…