arxiv: 2604.10539 · v1 · submitted 2026-04-12 · 💻 cs.LG · cs.AI

Recognition: unknown

IceCache: Memory-efficient KV-cache Management for Long-Sequence LLMs

Ke Li, Martin Ester, Qitong Wang, Yuzhen Mao

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:42 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords KV cache managementlong-sequence LLMsmemory efficiencysemantic token clusteringPagedAttentioninference optimizationoffloading

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The pith

IceCache maintains 99 percent of full KV-cache accuracy on long tasks using just 256 tokens by clustering semantically related tokens.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces IceCache to manage the large memory demands of KV caches during long autoregressive generation in LLMs. It combines semantic token clustering with PagedAttention to group related tokens into contiguous memory blocks managed by a hierarchical structure. This lets most of the cache move to CPU while retaining only critical tokens on GPU for fast access. On the LongBench benchmark, a 256-token budget keeps 99 percent of the accuracy from a full cache. The method also delivers competitive or better speed and quality than prior offloading techniques while using just one quarter of the usual token budget.

Core claim

IceCache integrates semantic token clustering with PagedAttention by organizing semantically related tokens into contiguous memory regions managed by a hierarchical, dynamically updatable data structure. This enables more efficient token selection and better utilization of memory bandwidth during CPU-GPU transfers. On LongBench, a 256-token budget maintains 99% of the full KV cache accuracy, and the method achieves competitive or superior latency and accuracy while using only 25% of the KV cache token budget compared to prior offloading methods.

What carries the argument

Semantic token clustering integrated with PagedAttention through a hierarchical, dynamically updatable data structure that groups related tokens for efficient KV cache selection and transfers.

If this is right

Long-generation tasks such as chain-of-thought reasoning become practical on GPUs with limited memory.
KV cache memory use can be reduced to 25 percent of standard sizes while keeping latency and accuracy competitive.
CPU-GPU bandwidth is used more efficiently because semantically grouped tokens allow better transfer patterns.
The approach supports scaling inference to longer contexts without proportional memory growth.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The clustering could be combined with other compression techniques to achieve even smaller cache footprints.
The method may help maintain coherence in multi-turn dialogues where token relevance changes gradually.
Hardware with higher CPU-GPU bandwidth would amplify the latency gains from the improved transfer efficiency.
Similar hierarchical grouping ideas might apply to managing memory in other transformer components like activations.

Load-bearing premise

Semantic token clustering can reliably identify the tokens most important for future generation steps without introducing selection errors that compound over long autoregressive sequences.

What would settle it

A long chain-of-thought generation task where the clustering fails to retain early tokens needed for the correct final answer, causing accuracy to drop well below 99 percent of the full-cache baseline on LongBench.

Figures

Figures reproduced from arXiv: 2604.10539 by Ke Li, Martin Ester, Qitong Wang, Yuzhen Mao.

**Figure 1.** Figure 1: IceCache has the best trade-off between CUDA memory footprint and time-per-outputtoken (TPOT) on A100 at a 36k sequence length. Baselines are chosen to represent high-accuracy (Left) and memory-efficient (Right) methods. Recent studies (Zhang et al., 2024b; Tang et al., 2024; Xiao et al., 2023) have shown that, despite the growing size of the KV-cache, only a small subset of tokens contributes disproport… view at source ↗

**Figure 2.** Figure 2: Illustration of IceCache. (1) During the prefill stage, tokens are indexed into a hierarchical [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of DCI-tree and IceCache: The hierarchical data structure on the left visualizes [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

**Figure 5.** Figure 5: (a) Baseline serial workflow, where prefilling, offloading (OL), and indexing are executed strictly in sequence. (b) IceCache pipelining, where GPU prefilling overlaps with KVoffloading via PCIe and CPU-side DCI indexing. Once KVs of layer i (Li) arrive in CPU memory, Li-DCI-tree indexing progresses in parallel with GPU prefilling and offloading of the subsequent layer (Li+1). This results in signific… view at source ↗

**Figure 6.** Figure 6: Passkey retrieval accuracy of IceCache on Llama3.1-8B-Instruct. The horizontal axis [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: Latency comparison of IceCache and baseline methods on a 36k-token sequence. [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

**Figure 8.** Figure 8: Latency scaling across context lengths (150k, 200k, 250k and 300k) [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗

read the original abstract

Key-Value (KV) cache plays a crucial role in accelerating inference in large language models (LLMs) by storing intermediate attention states and avoiding redundant computation during autoregressive generation. However, its memory footprint scales linearly with sequence length, often leading to severe memory bottlenecks on resource-constrained hardware. Prior work has explored offloading KV cache to the CPU while retaining only a subset on the GPU, but these approaches often rely on imprecise token selection and suffer performance degradation in long-generation tasks such as chain-of-thought reasoning. In this paper, we propose a novel KV cache management strategy, IceCache, which integrates semantic token clustering with PagedAttention. By organizing semantically related tokens into contiguous memory regions managed by a hierarchical, dynamically updatable data structure, our method enables more efficient token selection and better utilization of memory bandwidth during CPU-GPU transfers. Experimental results on LongBench show that, with a 256-token budget, IceCache maintains 99% of the original accuracy achieved by the full KV cache model. Moreover, compared to other offloading-based methods, IceCache attains competitive or even superior latency and accuracy while using only 25% of the KV cache token budget, demonstrating its effectiveness in long-sequence scenarios. The code is available on our project website at https://yuzhenmao.github.io/IceCache/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

IceCache combines semantic clustering with a hierarchical structure on PagedAttention for KV cache offloading and reports 99% LongBench accuracy at 256 tokens, but the risk of compounding selection errors in long autoregressive runs is not clearly addressed.

read the letter

The main takeaway is that IceCache adds semantic token clustering and a dynamically updatable hierarchical data structure on top of PagedAttention to decide which KV states to keep on GPU versus offload. That specific combination is not in the prior offloading papers they reference, so the engineering integration counts as new. They also release code, which is useful for anyone who wants to test it directly. The reported result is that a 256-token budget keeps 99% of full-cache accuracy on LongBench while using only 25% of the token budget and matching or beating other offloading baselines on latency. That is a concrete, practical data point for long-sequence inference on limited hardware. The soft spots are mostly around missing controls and analysis. The abstract gives no variance numbers, no detail on how clustering thresholds were set, and no ablation that isolates clustering mistakes from other factors. The biggest open question is exactly the one the stress-test note flags: whether early mis-clustered tokens can shift later attention patterns and compound over many steps, especially on chain-of-thought tasks where prior methods already degrade. Without an oracle attention comparison or a breakdown by sequence length, the 99% figure is plausible but not yet convincing on robustness. This paper is for people who need to run long-context models on constrained GPUs and want a drop-in management strategy with measured trade-offs. A practitioner reader will get value from the numbers and the design description even if the error-propagation analysis is light. It is worth sending to peer review because the problem is real, the method is grounded in existing primitives, and the empirical claims are specific enough that referees can check them.

Referee Report

3 major / 2 minor

Summary. The paper proposes IceCache, a KV-cache management method for long-sequence LLMs that combines semantic token clustering with PagedAttention and a hierarchical dynamically updatable data structure. The central claim is that this enables efficient CPU-GPU token selection and transfers, allowing a 256-token budget (25% of full cache) to retain 99% of full-KV accuracy on LongBench while matching or exceeding the latency and accuracy of prior offloading baselines, especially on chain-of-thought tasks.

Significance. If the empirical results hold under rigorous controls, the work would be significant for practical long-context inference on memory-constrained hardware. The integration of semantic clustering for contiguous memory regions offers a plausible engineering improvement over purely attention-score or recency-based eviction. The public code release supports reproducibility and is a clear strength.

major comments (3)

[Abstract / §4] Abstract and experimental evaluation: the headline result (99% accuracy retention at 256-token budget) is reported without any mention of run-to-run variance, number of random seeds, or how the token-budget threshold and clustering hyperparameters were selected or tuned. This directly affects verifiability of the central accuracy claim.
[§3 / §4] §4 (Experiments) and §3 (Method): no ablation isolates the contribution of semantic clustering to error propagation across autoregressive steps, nor is there a comparison against an oracle attention-based selector. Given that the paper itself notes prior offloading methods degrade on CoT tasks, the absence of such controls leaves the weakest assumption (reliable identification of future-relevant tokens) untested and load-bearing for the long-sequence claims.
[§3.2] §3.2 (hierarchical structure) and PagedAttention integration: the manuscript provides no analysis of how cluster boundaries interact with page-level CPU-GPU transfers or whether mis-clustered tokens can force additional page faults that compound latency. This interaction is central to the claimed bandwidth-efficiency advantage.

minor comments (2)

[§3] Figure 2 (or equivalent architecture diagram) would benefit from explicit annotation of the dynamic update rules and cluster-to-page mapping to improve clarity of the hierarchical data structure.
[§3.1] Notation for the clustering objective and eviction policy could be formalized with a short equation or pseudocode; the current prose description leaves the exact similarity metric and update frequency ambiguous.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments on our manuscript. We address each of the major comments below and outline the revisions we will make to improve the clarity and rigor of the paper.

read point-by-point responses

Referee: [Abstract / §4] Abstract and experimental evaluation: the headline result (99% accuracy retention at 256-token budget) is reported without any mention of run-to-run variance, number of random seeds, or how the token-budget threshold and clustering hyperparameters were selected or tuned. This directly affects verifiability of the central accuracy claim.

Authors: We agree that reporting run-to-run variance and details on hyperparameter selection is essential for reproducibility and verifiability. In the revised manuscript, we will update the experimental section to include results averaged over three independent random seeds, along with standard deviations for the reported accuracy metrics. Additionally, we will add a description of how the 256-token budget was chosen (to represent 25% of the full cache capacity for the evaluated sequence lengths) and the process for selecting clustering hyperparameters, which involved a grid search on a held-out validation set from LongBench. A sensitivity analysis will also be included to show robustness. revision: yes
Referee: [§3 / §4] §4 (Experiments) and §3 (Method): no ablation isolates the contribution of semantic clustering to error propagation across autoregressive steps, nor is there a comparison against an oracle attention-based selector. Given that the paper itself notes prior offloading methods degrade on CoT tasks, the absence of such controls leaves the weakest assumption (reliable identification of future-relevant tokens) untested and load-bearing for the long-sequence claims.

Authors: We recognize the importance of isolating the effect of semantic clustering and providing stronger controls. In the revision, we will include new ablation experiments that compare IceCache against recency-based and random selection baselines within the same PagedAttention setup. These will quantify the impact on error accumulation over multiple autoregressive steps, with particular focus on chain-of-thought tasks. An exact oracle attention-based selector is not feasible to implement without incurring the full computational cost of the complete KV cache, as it would require access to future attention scores. Instead, we will benchmark against attention-score-based eviction strategies from existing literature to contextualize the benefits of semantic clustering. We will also provide an analysis of token relevance prediction accuracy and its effect on long-sequence performance. revision: partial
Referee: [§3.2] §3.2 (hierarchical structure) and PagedAttention integration: the manuscript provides no analysis of how cluster boundaries interact with page-level CPU-GPU transfers or whether mis-clustered tokens can force additional page faults that compound latency. This interaction is central to the claimed bandwidth-efficiency advantage.

Authors: We agree that a detailed examination of the interplay between semantic clusters and PagedAttention's paging mechanism is necessary to substantiate the efficiency claims. In the revised §3.2 and experimental results, we will incorporate an analysis of page fault occurrences and CPU-GPU transfer volumes for clustered versus unclustered token management. The hierarchical data structure is designed to preserve contiguity within clusters to reduce fragmentation and associated page faults; we will present empirical measurements demonstrating that mis-clustering effects are mitigated by the dynamic update mechanism, resulting in minimal additional latency overhead. revision: yes

Circularity Check

0 steps flagged

No circularity in claimed derivation or results

full rationale

The paper describes an engineering method (semantic clustering + hierarchical PagedAttention structure) whose performance is measured empirically on external benchmarks such as LongBench. No equations, first-principles derivations, fitted parameters renamed as predictions, or self-citation chains appear in the abstract or described approach. The central claim (99% accuracy retention at 256-token budget) is presented as an experimental outcome rather than a quantity forced by construction from the method's own inputs. This is the expected non-finding for an applied systems paper without mathematical reduction steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are stated in the abstract; the approach relies on standard assumptions about attention mechanisms and semantic similarity being useful for token importance.

pith-pipeline@v0.9.0 · 5541 in / 1078 out tokens · 23161 ms · 2026-05-10T15:42:32.182429+00:00 · methodology

discussion (0)

Reference graph

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As shown in Table 6, IceCache substantially outperforms PQCache while maintaining accuracies on par with the Full-KV baseline

using Llama-3.1-8B-Instruct with a 256-token budget. As shown in Table 6, IceCache substantially outperforms PQCache while maintaining accuracies on par with the Full-KV baseline. Table 6: Accuracy comparison on LongGenBench for Llama-3.1-8B-Instruct. Method Completion Rate Accuracy Once Accuracy Range Accuracy Periodic Avg. Accuracy Full KV 97.627 0.349 ...

2026