Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
The Chinese University of Hong Kong（香港中文大学）；University of International Relations（国际关系大学）；Tencent Jarvis Lab（腾讯Jarvis实验室）；Westlake University（西湖大学）；Ministry of Education Key Laboratory of High Confidence Software Technologies, CUHK（教育部高可信软件技术重点实验室，香港中文大学）
Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current research primarily focuses on retrieval quality, often overlooking the critical ''integration bottleneck'': even when relevant documents are retrieved, LLMs frequently fail to utilize them effectively due to conflicts with their internal parametric knowledge. In this paper, we argue that implicitly resolving this conflict in a single generation pass is suboptimal. We introduce GuarantRAG, a framework that explicitly decouples reasoning from evidence integration. First, we generate an ''Inner-Answer'' based solely on parametric knowledge to capture the model's reasoning flow. Second, to guarantee faithful evidence extraction, we generate a ''Refer-Answer'' using a novel Contrastive DPO objective. This objective treats the parametric Inner-Answer as a negative constraint and the retrieved documents as positive ground truth, forcing the model to suppress internal hallucinations in favor of external evidence during this phase. Finally, rather than naive concatenation or using the DPO trained model directly, we propose a joint decoding mechanism that dynamically fuses the logical coherence of the Inner-Answer with the factual precision of the Refer-Answer at the token level. Experiments on five QA benchmarks demonstrate that GuarantRAG improves accuracy by up to 12.1% and reduces hallucinations by 16.3% compared to standard and dynamic RAG baselines.
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts
Seoul National University（首尔大学）；Ewha Womans University（梨花女子大学）；NAVER Cloud（NAVER云）；KAIST AI（韩国科学技术院AI学院）；NAVER AI LAB（NAVER AI实验室）；Hanyang University（汉阳大学）
LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation
Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University（航空信息安全与可信计算重点实验室，教育部，武汉大学计算机科学与工程学院）；Lehigh University（莱斯大学）；Monash University（墨尔本大学）；City University of Hong Kong（香港城市大学）；CNCERT/CC（国家互联网应急中心）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Search Team, WeChat, Tencent Inc.（微信搜索团队，腾讯公司）
FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation
School of Informatics, Xiamen University（厦门大学信息学院）；Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；The Hong Kong Polytechnic University（香港理工大学）；Migu Meland Co., Ltd（Migu Meland公司）；Soochow University（苏州大学）；Singapore Management University（新加坡管理大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
Ping An Technology (Shenzhen) Co., Ltd., China（平安科技（深圳）有限公司，中国）；University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）
School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）；School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Information Management, Wuhan University（武汉大学信息管理学院）；School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机学院）
CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation
School of Computation, Information, and Technology, Technical University of Munich（计算、信息与技术学院，慕尼黑技术大学）；Graduate Institute of International and Development Studies, Geneva（国际与发展研究研究生院，日内瓦）；JPMorgan AI Research（摩根大通人工智能研究）
