CoRDS: Coreset-based Representative and Diverse Selection for Streaming Video Understanding

• URL: http://arxiv.org/abs/2605.14310v1
• Date: Thu, 14 May 2026 03:22:30 GMT
• Title: CoRDS: Coreset-based Representative and Diverse Selection for Streaming Video Understanding
• Abstract summary: Streaming video understanding with large vision-language models (VLMs) requires a compact memory that can support future reasoning over an ever-growing visual history.<n>A common solution is to compress the key-value (KV) cache, but existing streaming methods typically rely on local token-wises.<n>We propose to view KV-cache compression as a coreset selection problem, rather than scoring tokens independently for retention.
• Score: 30.287018710590257
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming video understanding with large vision-language models (VLMs) requires a compact memory that can support future reasoning over an ever-growing visual history. A common solution is to compress the key-value (KV) cache, but existing streaming methods typically rely on local token-wise heuristics, such as recency, temporal redundancy, or saliency, which do not explicitly optimize whether the retained cache is representative of the accumulated history. We propose to view KV-cache compression as a coreset selection problem: rather than scoring tokens independently for retention, we select a small subset that covers the geometry of the accumulated visual cache. Our method operates in a joint KV representation and introduces a bicriteria objective that balances coverage in key and value spaces, preserving both retrieval structure and output-relevant information. To encourage a more diverse retained subset, we further introduce an orthogonality-driven diversity criterion that favors candidates contributing new directions beyond the current selection, and connect this criterion to log-determinant subset selection. Across four open-source VLMs and five long-video and streaming-video benchmarks, our method improves over heuristic streaming compression baselines under a fixed cache budget. These results highlight that representative coreset selection offers a more effective principle, than token-wise pruning, for memory-constrained streaming video understanding.

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