TGV-KV uses text-vision budgeting, weighted ranking, and prioritised retention to evict KV cache in VLMs while retaining 99.2% accuracy at 5% budget on VizWiz-VQA.
Judge q: Trainable queries for optimized information retention in kv cache eviction.arXiv preprint arXiv:2509.10798
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
EchoKV compresses LLM KV caches by reconstructing missing components from partial data via inter- and intra-layer attention similarities, outperforming prior methods on LongBench and RULER while supporting on-demand full-cache inference.
Meta-Soft dynamically synthesizes targeted soft tokens from a learnable meta-library using Gumbel-Softmax and applies attention-flow integration to compress KV cache while attempting to preserve evicted context information.
citing papers explorer
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TGV-KV: Text-Grounded KV Eviction for Vision-Language Models
TGV-KV uses text-vision budgeting, weighted ranking, and prioritised retention to evict KV cache in VLMs while retaining 99.2% accuracy at 5% budget on VizWiz-VQA.
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EchoKV: Efficient KV Cache Compression via Similarity-Based Reconstruction
EchoKV compresses LLM KV caches by reconstructing missing components from partial data via inter- and intra-layer attention similarities, outperforming prior methods on LongBench and RULER while supporting on-demand full-cache inference.
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Meta-Soft: Leveraging Composable Meta-Tokens for Context-Preserving KV Cache Compression
Meta-Soft dynamically synthesizes targeted soft tokens from a learnable meta-library using Gumbel-Softmax and applies attention-flow integration to compress KV cache while attempting to preserve evicted context information.