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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...
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.
Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented generation. While graphs have shown reliable advantages in providing structured evidence, the sparse graph representations naturally restrict machine readability and semantic...
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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