@inproceedings{chen-etal-2026-conduit,
title = "{CONDUIT}: A Unified Residual-Stream Restoration Framework for {KV} Cache Reuse in Vision-Language Models",
author = "Chen, Pengan and
Zheng, Kaisheng and
Hong, Liang and
Yi, Lixia and
Jiang, Jiyue and
Chen, Jiayang and
Wang, Yixuan and
Fan, Yimin and
Liu, Xinyuan and
Li, Jiayi and
Zhang, Zhanqiu and
Guo, Steven Y. and
Li, Yu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2266/",
pages = "48515--48535",
ISBN = "979-8-89176-462-0",
abstract = "Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exact-prefix reuse, however, fails when the same visual content appears under a changed prefix. Selective recomputation can recover quality under a small visual-token budget, but only when the right stale tokens are refreshed. Raw-attention selection can waste budget on high-attention tokens with small value-norm proxy scores and on query-irrelevant images. To address these failure modes, we propose CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration. Building on norm-weighted attention, CONDUIT ranks cached visual tokens using cached-key query attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection. With one image, the coefficient is one and the rule reduces to intra-image token selection. The method preserves model architecture and weights, adding only a single query-conditioned scoring pass at inference. At a 10{\%} refresh budget, CONDUIT achieves 97.0{\%}{--}99.5{\%} of the corresponding full-prefill five-dataset average across three VLM backbones and leads budgeted methods on average; on the MMLongBench-Doc latency subset, it uses 13.5{\%} of full-prefill FLOPs and achieves a 2.99{\texttimes} time-to-first-token speedup."
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<abstract>Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exact-prefix reuse, however, fails when the same visual content appears under a changed prefix. Selective recomputation can recover quality under a small visual-token budget, but only when the right stale tokens are refreshed. Raw-attention selection can waste budget on high-attention tokens with small value-norm proxy scores and on query-irrelevant images. To address these failure modes, we propose CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration. Building on norm-weighted attention, CONDUIT ranks cached visual tokens using cached-key query attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection. With one image, the coefficient is one and the rule reduces to intra-image token selection. The method preserves model architecture and weights, adding only a single query-conditioned scoring pass at inference. At a 10% refresh budget, CONDUIT achieves 97.0%–99.5% of the corresponding full-prefill five-dataset average across three VLM backbones and leads budgeted methods on average; on the MMLongBench-Doc latency subset, it uses 13.5% of full-prefill FLOPs and achieves a 2.99× time-to-first-token speedup.</abstract>
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%0 Conference Proceedings
%T CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models
%A Chen, Pengan
%A Zheng, Kaisheng
%A Hong, Liang
%A Yi, Lixia
%A Jiang, Jiyue
%A Chen, Jiayang
%A Wang, Yixuan
%A Fan, Yimin
%A Liu, Xinyuan
%A Li, Jiayi
%A Zhang, Zhanqiu
%A Guo, Steven Y.
%A Li, Yu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F chen-etal-2026-conduit
%X Vision-language models (VLMs) often answer new questions about recurring visual content, where reusing the key-value (KV) cache can avoid re-encoding expensive visual prefixes. Exact-prefix reuse, however, fails when the same visual content appears under a changed prefix. Selective recomputation can recover quality under a small visual-token budget, but only when the right stale tokens are refreshed. Raw-attention selection can waste budget on high-attention tokens with small value-norm proxy scores and on query-irrelevant images. To address these failure modes, we propose CONDUIT, a training-free refresh policy that unifies single- and multi-image reuse as residual-stream restoration. Building on norm-weighted attention, CONDUIT ranks cached visual tokens using cached-key query attention and an accessible pre-output cached-value-norm proxy, then applies empirical image-level relevance amplification before one global selection. With one image, the coefficient is one and the rule reduces to intra-image token selection. The method preserves model architecture and weights, adding only a single query-conditioned scoring pass at inference. At a 10% refresh budget, CONDUIT achieves 97.0%–99.5% of the corresponding full-prefill five-dataset average across three VLM backbones and leads budgeted methods on average; on the MMLongBench-Doc latency subset, it uses 13.5% of full-prefill FLOPs and achieves a 2.99× time-to-first-token speedup.
%U https://aclanthology.org/2026.findings-emnlp.2266/
%P 48515-48535
Markdown (Informal)
[CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models](https://aclanthology.org/2026.findings-emnlp.2266/) (Chen et al., Findings 2026)
ACL
- Pengan Chen, Kaisheng Zheng, Liang Hong, Lixia Yi, Jiyue Jiang, Jiayang Chen, Yixuan Wang, Yimin Fan, Xinyuan Liu, Jiayi Li, Zhanqiu Zhang, Steven Y. Guo, and Yu Li. 2026. CONDUIT: A Unified Residual-Stream Restoration Framework for KV Cache Reuse in Vision-Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48515–48535, Budapest, Hungary. Association for Computational Linguistics.