Q-RAG trains embedders via RL for multi-step retrieval and reports state-of-the-art results on BabiLong and RULER benchmarks for contexts up to 10M tokens.
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GAM decouples event-level memory encoding from topic-level consolidation in LLM agents using hierarchical graphs to reduce interference and improve long-term coherence and retrieval.
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.
citing papers explorer
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Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
Q-RAG trains embedders via RL for multi-step retrieval and reports state-of-the-art results on BabiLong and RULER benchmarks for contexts up to 10M tokens.
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GAM: Hierarchical Graph-based Agentic Memory for LLM Agents
GAM decouples event-level memory encoding from topic-level consolidation in LLM agents using hierarchical graphs to reduce interference and improve long-term coherence and retrieval.
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All Relations Lead to Rome: Automated Knowledge Graph Creation and Question Generation
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.