Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.30884

CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters

Yuhang Cao·Yanzhou Mu·Chunrong Fang·Zhenyu Chen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Large language models rely on KV caching to reduce repeated prefill computation in long context and interactive applications. As lightweight adapters evolve, cached states reflect earlier versions, so stale reuse distorts current model outputs, while complete affected suffix recomputation restores fidelity at substantial cost. We seek minimal recomputation that recovers current adapter behavior. Existing systems track token, context, or stable adapter identity, but neither represent caches from earlier adapter versions nor distinguish update propagation from the recomputation required for behavioral recovery. To address these gaps, we introduce CacheReforge, which represents stale KV caches as layerwise mixed-version objects. It combines per-layer adapter anchors, calibrated sensitivity, accumulated drift, and executable restart boundaries to select direct reuse, bounded recomputation, or complete affected-suffix recovery. We distinguish dependency depth from the functional recomputation horizon and use cumulative tail influence to characterize when bounded recovery preserves current-model behavior. We evaluate CacheReforge on Qwen2.5-1.5B and Qwen2.5-7B with continual LoRA updates, including 16K HotpotQA and 2WikiMQA workloads. CacheReforge reduces mean KL divergence by 92.4% relative to stale reuse, while recomputing only 5.44% of layers and reducing cache-maintenance time by 93.2% relative to fresh full prefill. These results show that version-aware recovery preserves model fidelity and most KV caching gains.

Explore related subjects

cs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuhang Cao, Yanzhou Mu, Chunrong Fang, Zhenyu Chen. 2026-09-25. CacheReforge: Bounded Recovery for Stale KV Caches under Evolving Adapters. https://arxiv.org/abs/2609.30884

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Efficient Constrained Graph Search for Post-hoc Error Correction in Binary Classifiers

We introduce a model-agnostic framework for constrained post-hoc error correction in binary classifiers. Given a frozen base classifier, the method searches for an interpretable conjunction of feature--threshold rules that corrects residual false-positive or false-negative errors while explicitly constraining newly introduced errors. The approach combines graph-based search over candidate rule paths, depth-dependent dynamic constraints, and a reduced-histogram procedure for efficient threshold evaluation. Unlike retraining or modifying the base classifier, the learned correction path operates on its predictions and can therefore be applied to arbitrary binary classifiers with suitable input features. Experiments on a large binary-classification problem demonstrate that the method can identify compact correction rules efficiently; for example, one configuration removes 90\% of false positives while sacrificing 5\% of true positives.

cs.LG↗
arXiv2026

Stochastic Bilevel Optimization with Heavy-Tailed Noise

This paper considers the smooth bilevel optimization in which the lower-level problem is strongly convex and the upper-level problem is possibly nonconvex. We focus on the stochastic setting where the algorithm can access the unbiased stochastic gradient evaluation with heavy-tailed noise, which is prevalent in many machine learning applications, such as training large language models and reinforcement learning. We propose a nested-loop normalized stochastic bilevel approximation (N$^2$SBA) for finding an $ε$-stationary point with the stochastic first-order oracle (SFO) complexity of $\tilde{\mathcal{O}}\big(κ^{\frac{7p-3}{p-1}} σ^{\frac{p}{p-1}} ε^{-\frac{4 p - 2}{p-1}}\big)$, where $κ$ is the condition number, $p\in(1,2]$ is the order of central moment for the noise, and $σ$ is the noise level. Furthermore, we specialize our idea to solve the nonconvex-strongly-concave minimax optimization problem, achieving an $ε$-stationary point with the SFO complexity of~$\tilde{\mathcal O}\big(κ^{\frac{2p-1}{p-1}} σ^{\frac{p}{p-1}} ε^{-\frac{3p-2}{p-1}}\big)$. All the above upper bounds match the best-known results under the special case of the bounded variance setting, i.e., $p=2$. We also conduct the numerical experiments to show the empirical superiority of the proposed methods.

cs.LG↗
arXiv2026

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting tasks via generative probabilistic modeling, while ensuring both efficiency and robustness. FLAME utilizes the Legendre Memory for strong generalization capabilities. By adapting variants of Legendre Memory, i.e., translated Legendre (LegT) and scaled Legendre (LegS), in the Encoding and Decoding phases, FLAME can effectively capture the inherent inductive bias within data and make efficient long-range inferences. To enhance the accuracy of probabilistic forecasting while remaining efficient, FLAME adopts a normalizing-flow-based forecasting head, which can model complex distributions over the forecasting horizon in a generative manner. Comprehensive experiments on three well-recognized benchmarks, including TSFM-Bench, ProbTS, and TFB, demonstrate that FLAME is a strong out-of-the-box tool for decision intelligence.

cs.LG↗