Unverified50% confidenceFactExact time
In actual measurements, the difference in dense vector similarity may be only 0.10 to 0.15, but the cross-encoder's score for text chunks containing engineering triples can jump by 4.0 to 6.0 logits.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/19/2026
First Seen
Valid until: 1/17/2027
Sources
Related Entities
Related Claims
Unverified千问3.8 27B的代码方向进步明显大于文本创作,剧本拆解能力相比3.6有提升但幅度有限69% similarUnverifiedAfter projecting three competitor copy samples into 1536-dimensional space using text-embedding-3-large, cosine similarity typically falls between 0.91 and 0.94.69% similarUnverifiedThe experimental results show that the paired token cosine similarity is only 0.221–0.484.65% similarUnverified通义千问3.6 27B在语义三次全对率和格式正确率上均领先Muse Glimmer64% similarUnverifiedThe AILegal team tried using the local embedding models nomic-embed-text and qwen3-embedding to handle standard paraphrases, and the similarity scores landed at top-3/top-4 with cosine gaps of only 0.001–0.03, at the noise level.63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1072551API
curl https://kongchang.com/api/v1/knowledge/claims/1072551MCP
get_claim(id=1072551)