arXiv (All)
open access
EntropyPrune: Matrix Entropy Guided Visual Token Pruning for Multimodal Large Language Models
Multimodal large language models (MLLMs) incur substantial inference cost due to the processing of hundreds of visual tokens per image. Although token pruning has proven effective for accelerating inference, determining when and where to prune remains largely heuristic. Existing approaches typically
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SinkPruner: Sink-Free Visual Token Pruning for Multimodal Large Language Models#820996
- Multi-Image Visual Token Pruning in Large Visual Language Models#820643
- S$^2$Prune: Spatially Structured Visual Token Pruning for Multimodal Large Language Models#821181
- An AI4AI Framework for Visual Token Pruning#974522
- Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs#1132117
Record · ID 974331
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive