AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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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.
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...
The Causal-Generative Dual-Judge (CGDJ) is introduced, auditing World Model Consistency from two perspectives, and analysis exposes audio-visual misalignment, where models verbalize correct causal logic more reliably than they render it, challenging the"world simulator"narrative.
The key observation is that although expert trajectories are scarce, high-quality final artifacts such as literature reviews, analyst reports and legal judgments, are abundant in pre-training data and can be viewed as compressed traces of the evidence-seeking processes that produced them.
Latent-OPD is proposed, which augments OPD with trajectory-level latent distillation and introduces a progressive teacher-lookahead strategy, which aligns middle-to-late student layers with increasingly deeper teacher layers, establishing Latent-OPD as a highly effective approach to frame-efficient video reasoning.
Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles, demonstrating that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.
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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