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Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China, School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China
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LLMs can significantly boost multi-table entity matching by cleverly coordinating attributes, embedding entities, and pruning noise.
By dynamically injecting frequency-aware n-gram features, X-GRAM achieves state-of-the-art accuracy with smaller embedding tables, offering a practical path to scaling memory-augmented architectures.
Canary tokens turn the tables on RAG extraction attacks, offering a plug-and-play runtime defense that detects leakage attempts with negligible performance overhead.
Fragmented retrieval in long-term conversational agents is solved by HyperMem, which uses hypergraphs to model high-order associations between memories, achieving state-of-the-art performance.
