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UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models — arXiv:2609.32831

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.32831
License (as archived): CC0-1.0
Archived: 2026-10-07T22:36:53.562Z
SHA-256 of the archived text: bb06f5fdec25e05cd2d2ff40e36cbb4878e53b532223d7f4e4fb58538232ba5e
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How to cite

UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models — arXiv:2609.32831 — arXiv (en), https://arxiv.org/abs/2609.32831 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7e8dfeb7ea21 (Metraipolis archive dc_7e8dfeb7ea21, sha256 bb06f5fdec25e05cd2d2ff40e36cbb4878e53b532223d7f4e4fb58538232ba5e)

BibTeX

@misc{dc7e8dfeb7ea21,
  title = {UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models — arXiv:2609.32831},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.32831},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7e8dfeb7ea21; sha256 bb06f5fdec25e05cd2d2ff40e36cbb4878e53b532223d7f4e4fb58538232ba5e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.32831. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimodal applications. However, growing multimodal contexts make KV cache storage and access increasingly costly. Existing KV cache compression methods are typically tailored to specific tasks and single-modality caches, while overlooking changes in cache importance across tasks and timesteps. However, in unified multimodal models, each task involves multiple KV cache types, and both their composition and dynamics differ across tasks. As a result, a single compression policy overlooks task- and type-specific requirements, leading to the loss of critical information and degraded quality across tasks. Based on these findings, we propose UniCache, a training-free framework for task- and type-aware KV cache compression. UniCache identifies the cache segments activated by each task and assigns suitable compression policies through offline calibration. It coordinates their parallel execution under a shared storage budget through attention-guided allocation and task-aware temporal scheduling. Experiments show that UniCache achieves $5\times$ KV cache compression for understanding and editing and $2.5\times$ for generation with negligible quality loss, while increasing throughput by up to $1.78\times$ in long-context settings, significantly improving the practicality of scaling unified multimodal models to longer context.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.32831 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T22:14:27.780Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).