AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Diffusion-based world models enable high-quality interactive environment generation but suffer from substantial inference overhead due to repeated Transformer evaluations during denoising. Existing caching methods mainly exploit temporal redundancy at the feature or token level, leaving the underlying mathematical stru...
Vision language models (VLMs) incur substantial inference cost because every visual token is processed by the attention and MLP projections of every decoder layer, even when token-specific visual computation is unnecessary at many depths. We introduce Patch-to-Prune (P2P), inspired by Mechanistic Interpretability, a tr...
Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, whereas uniform temporal compression severely dilutes fine-grained acoustic and visual evidence. We introduce LEAP, a framework where the model retrieves its own evidence witho...
This paper proposes a vision for telemetry-informed adaptive compression in edge RAG, grounded in experimental evidence, and argues for runtime policies that dynamically manage compression, guided by workload features and edge telemetry.
A three-stage fine-tuning curriculum applied to Qwen3-27B is described that is designed to progressively specialize the model for the C-to-Rust (C2Rust) translation task, and the resulting model is evaluated using the agentic, static-analysis-guided verification framework of SACTOR.
Recent work has shown that large language models (LLMs) exhibit strong numerical sequence modeling capabilities and show promise in time-series prediction. While LLMs display in-context learning capabilities, the mechanisms with which they accomplish time-series prediction remain unclear. Specifically, whether they tru...
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