MROP: Mask-Region Optimized Purification Against Backdoor Attack in Deep JSCC
本文针对深度联合信源信道编码中的后门攻击问题，提出了一种无需重新训练模型的掩码区域优化净化方法(MROP)，有效降低了攻击成功率。
0 citationsRead paper
Selected work
Representative Papers
CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG
Traditional RAG systems struggle with information redundancy and noise when processing long contexts, and coarse-grained block-level KV cache reuse fails to simultaneously achieve low prefill latency and high answer accuracy. This work proposes a fine-grained RAG approach that identifies query-relevant semantic units—termed “information nuggets”—through a two-stage retrieval process, then integrates their sliced KV representations with block-level context to construct a compact, semantically focused context representation. The method introduces an offline fine-grained KV cache reuse mechanism, which, under standard fast prefill latency constraints, improves average F1 by 5.3% on LongBench multi-hop question answering tasks while significantly reducing computational overhead, thereby advancing beyond the current Pareto frontier of efficiency and accuracy in RAG systems.
0 citationsRead paper
SPARK: Self-Play with Asymmetric Reward from Knowledge Graphs
This work addresses the challenge of automatically generating trustworthy multi-hop reasoning questions from scientific literature, where relationships among multimodal elements are often implicit and difficult to verify. To this end, it introduces knowledge graphs into a self-play framework for scientific documents, constructing a unified graph to generate multi-hop relational questions and providing verifiable reward signals grounded in structured factual knowledge. By leveraging an information asymmetry mechanism, a single small-scale vision-language model alternately assumes the roles of questioner and answerer during training. The proposed approach significantly outperforms text-only self-play baselines on both public benchmarks and a newly curated cross-document multi-hop question answering dataset, with performance gains becoming more pronounced as the number of reasoning hops increases.
0 citationsRead paper
Recent publications
Latest Papers
MROP: Mask-Region Optimized Purification Against Backdoor Attack in Deep JSCC
本文针对深度联合信源信道编码中的后门攻击问题，提出了一种无需重新训练模型的掩码区域优化净化方法(MROP)，有效降低了攻击成功率。
0 citationsRead paper
CoinRAG: Contextualized Information Nugget KV Cache Reuse for Long-Context RAG
Traditional RAG systems struggle with information redundancy and noise when processing long contexts, and coarse-grained block-level KV cache reuse fails to simultaneously achieve low prefill latency and high answer accuracy. This work proposes a fine-grained RAG approach that identifies query-relevant semantic units—termed “information nuggets”—through a two-stage retrieval process, then integrates their sliced KV representations with block-level context to construct a compact, semantically focused context representation. The method introduces an offline fine-grained KV cache reuse mechanism, which, under standard fast prefill latency constraints, improves average F1 by 5.3% on LongBench multi-hop question answering tasks while significantly reducing computational overhead, thereby advancing beyond the current Pareto frontier of efficiency and accuracy in RAG systems.
0 citationsRead paper
SPARK: Self-Play with Asymmetric Reward from Knowledge Graphs
This work addresses the challenge of automatically generating trustworthy multi-hop reasoning questions from scientific literature, where relationships among multimodal elements are often implicit and difficult to verify. To this end, it introduces knowledge graphs into a self-play framework for scientific documents, constructing a unified graph to generate multi-hop relational questions and providing verifiable reward signals grounded in structured factual knowledge. By leveraging an information asymmetry mechanism, a single small-scale vision-language model alternately assumes the roles of questioner and answerer during training. The proposed approach significantly outperforms text-only self-play baselines on both public benchmarks and a newly curated cross-document multi-hop question answering dataset, with performance gains becoming more pronounced as the number of reasoning hops increases.
0 citationsRead paper