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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Shu Wang

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Book Open access Sep 2026

To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems

Disaggregated LLM serving separates prefill and decode into distinct node pools, interposing a network fabric between the moment a key-value (KV) cache is computed and the moment it is consumed. This architectural shift invalidates a core assumption of classical cache policies: that the cost of a miss is simply recomputation on the same device. In disaggregated systems, a miss triggers both recomputation on a prefill node and a network transfer of the resulting KV block to the decode node—costs that differ by an order of magnitude and depend on prefix length, model width, and fabric bandwidth. Meanwhile, admitting a block to the global KV pool requires an additional transfer at compute time, so a poorly chosen keep decision wastes both memory and bandwidth even before reuse occurs. We present KVLearn, a learning-based retention framework that makes keep/evict decisions as first-class cost-optimization choices in disaggregated LLM serving. KVLearn consists of three components: (i) a lightweight Prefix Reuse Predictor (PRP) that estimates reuse probability from structural and temporal prefix features without touching model weights; (ii) a Cost-Aware Retention Score (CARS) that translates reuse probability into a keep/admit signal by accounting for per-block recompute, transfer, and storage costs; and (iii) an Adaptive Threshold Controller (ATC) that adjusts the admission threshold online using closed-loop feedback from observed hit rates and memory pressure. We integrate KVLearn into a globally disaggregated serving topology and evaluate it on both text and multimodal workloads, where image/video-derived tokens create large, expensive-to-recompute KV blocks under heterogeneous reuse distributions. KVLearn reduces end-to-end time-to-first-token (TTFT) by up to 56% vs. No-Cache (recompute-only), up to 38% vs. LRU-Pool, and up to 33% vs. Mooncake-style disaggregated baselines. Inter-node KV transfer volume is cut by up to 53% vs. LRU-Pool. On MM-Session, throughput stays within ~5% of oracle. Our code implementation of KVLearn is available at https://github.com/FastLM/KVLearn.

Dong Liu, Yanxuan Yu, Eric Jiang et al. · 0 citations
Preprint Jul 2026

RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification

Class imbalance poses a fundamental challenge in risk-sensitive applications such as fraud detection and medical diagnosis, where minority-class samples are scarce yet critical for accurate classification. Existing oversampling methods generate synthetic samples to rebalance class distributions; however, they often produce large numbers of low-quality candidates that distort decision boundaries or introduce artifacts, leading to overfitting and degraded generalization. In this work, we introduce RUBRIC, a generator-agnostic filtering framework that formulates synthetic sample selection as a quality-over-quantity optimization problem. RUBRIC ranks candidates using a realism-utility trade-off: realism is quantified by a learned discriminator that distinguishes real samples from synthetic samples, while utility captures proximity to the decision boundary through a concave margin-based scoring function. We show that, under mild regularity conditions, the proposed filtering strategy monotonically tightens the generalization bound for margin-based classifiers by jointly reducing distribution shift and suppressing near-negative tail contributions. Through extensive experiments on credit-card fraud detection and other imbalanced benchmarks, we demonstrate that RUBRIC improves F1-macro and recall while maintaining comparable ROC-AUC across several generators. We also provide explicit lambda-sensitivity analysis to show how users can recover AUPRC when ranking quality is prioritized.

Yanxuan Yu, Dong Liu, Shu Wang et al. · 1 citation
Preprint Aug 2026

A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination

A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views, is proposed, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.

Wenxiao Zhao, Dong Liu, Kaiyi Xu et al. · 2 citations

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