C2-Routing cuts VLM visual tokens by routing crop or compress
With a 1,024-token cap already applied, the method reports 36.53% fewer FLOPs and a 0.77% mean relative score drop.
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With a 1,024-token cap already applied, the method reports 36.53% fewer FLOPs and a 0.77% mean relative score drop.
Self-supervised confidence fine-tuning cuts reasoning tokens by up to 25% without training models to stop early.
A survey catalogs inference-efficiency techniques for video and audiovisual LLMs, mapping bottlenecks in sampling, encoding, token reduction, and LLM decoding.
SelectHub launched DataGrout, an LLM cost and governance platform combining dynamic context pruning, symbolic inference and MCP gating, claiming about 60% token reduction.
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