Shared-Prefix KV Reuse Across Standard LoRA Adapters: Quality and Serving Tradeoffs
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
A common small-model deployment runs one shared backbone with several LoRA specialists that answer over the same context. Serving them naively re-prefills that shared context once per specialist. We s...
arXiv:2608.30963v1 Announce Type: cross Abstract: Modern large language model (LLM) serving systems increasingly operate over repeated or shared context, yet each model typically performs its own pre...
The paper introduces KVLearn, a learning‑based framework for managing key‑value (KV) cache retention in disaggregated large language model (LLM) serving systems. KVLearn comprises a Prefix Reuse Predictor that estimates reuse probability from prefix features, a Cost‑Aware Retention Score that translates this probability into keep/admit decisions based on recomputation, transfer, and storage costs, and an Adaptive Threshold Controller that tunes admission thresholds online. Integrated into a globally disaggregated serving topology, KVLearn achieves up to 56% faster time‑to‑first‑token and up to 53% reduction in inter‑node KV transfer compared to baseline caching strategies.
arXiv:2610.02233v1 Announce Type: cross Abstract: Cross-context KV-cache reuse predicts a shared segment's keys and values under a new prefix instead of recomputing them, and has been reported to do...
arXiv:2610. 10552v1 Announce Type: cross Abstract: Comparing parameter-efficient fine-tuning recipes under a single, shared learning rate is a common but flawed practice: when the arms being compared have very different trainable-parameter counts, a shared rate can simultaneously depress the larger arms' means and inflate their variance, manufacturing a large, seemingly multi-seed-significant advantage for the smallest arm that is not a real effect.
arXiv:2608. 01247v1 Announce Type: cross Abstract: Query-agnostic KV cache eviction compresses a context once and reuses the resulting cache for arbitrary future queries, but performance can collapse under tight budgets.