CIVIC is a path-consistent compact visual inference framework that reduces KV-cache memory to approximately one-third and end-to-end latency in VLMs while preserving accuracy via text-aligned KL distillation and adaptive spatial retention.
Harsh Jhamtani and Taylor Berg-Kirkpatrick
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.AI 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HybridKV reduces KV cache memory by up to 7.9x and speeds decoding by 1.52x in MLLMs with almost no performance loss by classifying heads into static and dynamic types and compressing them differently.
citing papers explorer
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CIVIC: End-to-End Sequence Compactness for Efficient Vision-Language Models
CIVIC is a path-consistent compact visual inference framework that reduces KV-cache memory to approximately one-third and end-to-end latency in VLMs while preserving accuracy via text-aligned KL distillation and adaptive spatial retention.
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HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference
HybridKV reduces KV cache memory by up to 7.9x and speeds decoding by 1.52x in MLLMs with almost no performance loss by classifying heads into static and dynamic types and compressing them differently.