Unverified50% confidenceBenchmarkExact time
@Teknium's data shows that comparing Jev compression with ordinary summarization: 115K tokens retained versus 55K, recall 76% versus 79%, and under the same budget Jev ranking ties with the ordinary recency method (77.8 versus 77.8).
1
Sources
50%
Confidence
Short-term (~14 days)
Relevance
10/12/2026
First Seen
Valid until: 10/26/2026
Sources
Related Entities
Related Claims
UnverifiedJev supports up to 64K token context out of the box, whereas CLM-8B has only been tested and calibrated in the 2K to 8K context range, and representations beyond 8K have not been calibrated for the reference head.63% similarUnverifiedTraditional KV cache implementations pre-allocate contiguous GPU memory blocks of maximum length, resulting in an average of 60%–80% of pre-allocated memory being wasted.63% similarUnverifiedSparse activation reduces computational load rather than memory footprint; the total 78B parameters still require considerable VRAM or memory resources.62% similarVerifiedTaking LLaMA-2 70B as an example, when processing a 4096-token context with a batch size of 32, the KV Cache can consume over 80 GB of memory.62% similarUnverifiedThe author believes that using Jev for context compaction does not work, because compaction requires synthesizing a summary based on the full history and the 32K context is insufficient.61% similar
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