Combine these methods for a superior RAG system: implement LUFY for long-term memory based on psychological importance like arousal, and CoopRAG to handle uncertainty and rerank using layer contrasts. Add Reasoning-Trace RAG to resolve conflicts via explicit logic traces and adjudication, then deploy HiFi-RAG for efficient hierarchical content filtering. Together, they create robust, conflict-aware, and psychologically grounded applications.
Since large language models (LLMs) have a tendency to generate factually inaccurate output, retrieval-augmented generation (RAG) has gained significant attention as a key means to mitigate this downsi...
Retrieval-Augmented Generation (RAG) in open-domain settings faces significant challenges regarding irrelevant information in retrieved documents and the alignment of generated answers with user inten...
Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external evidence, but fails when retrieved sources conflict or contain outdated or subjective information. Prior work addr...
While Retrieval-Augmented Generation (RAG) has shown promise in enhancing long-term conversations, the increasing memory load as conversations progress degrades retrieval accuracy. Drawing on psycholo...
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