2 citations · 2 across the 12 of their papers we have counts for
12 papers
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…
Revisiting the Reliability of Language Models in Instruction-Following
Jianshuo Dong, Yutong Zhang, Yan Liu +4
Advanced LLMs have achieved near-ceiling instruction-following accuracy on benchmarks such as IFEval. However, these impressive scores do not necessarily translate to reliable serv…
RAGFort: Dual-Path Defense Against Proprietary Knowledge Base Extraction in Retrieval-Augmented Generation
Qinfeng Li, Miao Pan, Ke Xiong +6
Retrieval-Augmented Generation (RAG) systems deployed over proprietary knowledge bases face growing threats from reconstruction attacks that aggregate model responses to replicate…
U2F: Encouraging SWE-Agent to Seize Novelty without Losing Feasibility
Wencheng Ye, Yan Liu
Large language models (LLMs) have shown strong capabilities in software engineering tasks, yet most existing LLM-based SWE-Agents mainly tackle well-defined problems using conventi…
Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
Jingbo Wang, Sendong Zhao, Haochun Wang +2
The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique experti…
Semantic Surgery: Zero-Shot Concept Erasure in Diffusion Models
Lexiang Xiong, Chengyu Liu, Jingwen Ye +2
Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery…
Agent4S: The Transformation of Research Paradigms from the Perspective of Large Language Models
Boyuan Zheng, Zerui Fang, Zhe Xu +13
While AI for Science (AI4S) serves as an analytical tool in the current research paradigm, it doesn't solve its core inefficiency. We propose "Agent for Science" (Agent4S)-the use…
When Does Multimodality Lead to Better Time Series Forecasting?
Xiyuan Zhang, Boran Han, Haoyang Fang +11
Recently, there has been growing interest in incorporating textual information into foundation models for time series forecasting. However, it remains unclear whether and under wha…
Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluation
Yaxuan Kong, Yiyuan Yang, Shiyu Wang +7
Understanding time series data is fundamental to many real-world applications. Recent work explores multimodal large language models (MLLMs) to enhance time series understanding wi…
Improving Generated and Retrieved Knowledge Combination Through Zero-shot Generation
Xinkai Du, Quanjie Han, Chao Lv +5
Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Lan…
Continual Test-Time Adaptation for Object Detection with Adaptive Monitoring and Randomized Restoration
Shilei Cao, Juepeng Zheng, Yan Liu +5
Real-world application models are commonly deployed in dynamic environments, where the target domain distribution undergoes temporal changes. Continual Test-Time Adaptation (CTTA)…
Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation
Xiao Song, Jiafan Liu, Yun Li +3
Radiology Report Generation (RRG) draws attention as a vision-and-language interaction of biomedical fields. Previous works inherited the ideology of traditional language generatio…