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Large language model (LLM) agents repeatedly load reusable content, such as skills, documents, and memory entries, into the current context. Re-encoding this content for every request wastes computation. Position-independent caching (PIC) alleviates this by encoding each artifact independently and reusing its key-value...
This work proposes HyperReCo, a framework for retrieving and connecting evidence with a hypergraph neural network (HyperGNN), and introduces Gradient-Guided Hyper-Path Decoding (GGHD), which uses gradient attribution to interpret the learned interactions and translate them into explicit hyper-paths that help LLMs combi...
LADDER is a novel framework that bridges diffusion language modeling with GraphRAG through graph-guided parallel decoding through graph-guided parallel decoding, and proposes an event-driven self-clocking retrieval, inspired by the key insight that 88% of target entities emerge early in the partially denoised state.
LGM is presented, a novel neuro-symbolic framework that shifts long-term memory disentanglement into a continuous latent space and significantly outperforms state-of-the-art baselines in capturing both explicit and implicit preferences while enabling personalized responses.
This work formalizes a Wiki Graph schema that seamlessly bridges fine-grained structures with dense contexts, maintaining explicit topologies alongside continuous semantics and engineer an infrastructural NCCL boundary exchange protocol that hoists static partition indices and leverages fixed-shape GPU-to-GPU collectiv...
HyperRAG is introduced, a novel framework in the Hyper-bolic space that captures both explicit entity-based links and implicit query-aware connections and consistently outperforms existing baselines.
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