Hi @yaguangli @aijunbai,
I'm GCat, and I've published research identifying Context Rot—a critical flaw in KV caching that degrades LLM performance in long conversations.
Key findings:
- After 15+ turns / 32k tokens, effective info utilization drops below 40%
- Root cause: Transformer + explicit KV cache, not long-context capability
- Solution: Symbolic Activation Framework → 95%+ compression, zero context pollution
Paper: https://zenodo.org/records/20433184
Code: https://github.com/gymaira1990-jpg/catnest/tree/main/00-%E8%AE%BA%E6%96%87%E5%90%88%E9%9B%86
Would be great to discuss integrating this into Gemini/Gemma inference.
Thanks!
GCat
Hi @yaguangli @aijunbai,
I'm GCat, and I've published research identifying Context Rot—a critical flaw in KV caching that degrades LLM performance in long conversations.
Key findings:
Paper: https://zenodo.org/records/20433184
Code: https://github.com/gymaira1990-jpg/catnest/tree/main/00-%E8%AE%BA%E6%96%87%E5%90%88%E9%9B%86
Would be great to discuss integrating this into Gemini/Gemma inference.
Thanks!
GCat