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Latent Context Language Models achieve 16x input compression without accuracy loss
Researchers from NYU, Columbia, Princeton, and others introduce LCLMs, achieving 16x context compression and 8.8x faster inference with no accuracy loss.
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venturebeat.com
LLM context compression at 16x beats KV cache
LCLMs compress LLM context before decode — 8.8x faster at 16x compression, beating every KV cache method tested. Open-sourced by NYU and Columbia.
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cryptobriefing.com
Latent Context Language Models achieve 16x input compression without accuracy loss
Researchers from NYU, Columbia, Princeton, and others introduce LCLMs, achieving 16x context compression and 8.8x faster inference with no accuracy loss.