Unverified50% confidenceEventExact time
The study validated on two models, DeepSeek-V2-Lite and Qwen3-30B-A3B.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
11/2/2026
First Seen
Valid until: 1/31/2027
Sources
Related Entities
Related Claims
UnverifiedThe experiments covered three different model families: Qwen14B, Qwen7B, and DeepSeek6.7B.69% similarUnverifiedDeepSeek-R1-Distill-Qwen-32B is based on Qwen-2.5-32B and was derived by distilling knowledge from the full 671B MoE architecture model.68% similarUnverifiedReLE's open-source model evaluation covers models including kimi-k2.6, deepseek-v4, Qwen3.6, llama4, GLM-5.1, gemma4, and mistral.68% similarUnverifiedData released by Liquid AI shows that LFM2.5-24B-A2B beats Qwen3.6-35B-A3B on tool-use benchmarks65% similarUnverifiedOn LongBench v1/v2 and MemoryAgentBench, NoteBank also reported improvements, covering everything from three 1.3B research models up to Mamba-2-8B and a trained Qwen3.5-9B.65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1125225API
curl https://kongchang.com/api/v1/knowledge/claims/1125225MCP
get_claim(id=1125225)