AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work proposes NOSA, a trainable sparse attention mechanism natively designed for KV cache offloading that explicitly constrains the volume of CPU-GPU KV transfers, thereby achieving low communication overhead and high decoding throughput.
Downstream RLHF experiments additionally show that using DRM as the training-time reward leads to improved policy performance, directly validating the practical benefit of diffusion-based reward modeling for RLHF training.
A*-Thought-V2 is presented, a geometric dynamics of LLM guided framework that models CoT as a hidden-state trajectory and replaces hard deletion with an explicit-implicit interleaved latent architecture that reflects broader soft targets that encourage richer step-level feature learning.
Results show that simulator-grounded reflection is more reliable than unverified self-revision, while memory anchors agents to early designs and broad tree search explores without converging, pointing to mechanism redesign rather than parameter inference as the central bottleneck.
On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its algorithmic behavior, leaving the role of training data unclear. We examine this role at the data-minimal limit by training on a single query. One-shot OPD keeps impro...
StudyBench is introduced, a controlled physics benchmark that directly measures how efficiently a self-evolution method converts training material into capability, and turns self-evolution progress from an open-ended pursuit into a measurable target for future research.
REFACT is an adaptive fact-restatement citation framework that enables LLMs to determine when contextual grounding is needed and selectively restate source facts at appropriate levels of detail for reliable reasoning.
Group-relative residual calibration can incorporate verifier outcomes without discarding dense token-level guidance, and demonstrates that group-relative residual calibration can incorporate verifier outcomes without discarding dense token-level guidance.
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