Unverified50% confidenceReleaseExact time
The study proposes ∂-NO (del-NO), an architecture-agnostic plug-and-play derivative-enhanced neural operator that can provide learnable fractional-order derivative features for any neural operator backbone.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/15/2026
First Seen
Valid until: 1/13/2027
Sources
Related Entities
Related Claims
UnverifiedFrom-scratch implementations in teaching scenarios usually draw the boundary at the NumPy layer: using it only for tensor storage and basic operations, while all automatic differentiation, layer structures, and optimizers are still written by oneself.60% similarUnverified逻辑层与展示层分离的设计使得同一套推理能力可零成本适配新渠道，新增渠道只需新增格式化器而无需触动核心推理逻辑59% similarUnverifiedConversational generation based on large language models is the latest evolutionary form of no-code, further eliminating the cognitive barrier of understanding platform logic and component relationships compared to traditional no-code approaches.59% similarUnverified端到端加密（E2EE）或零知识架构与大语言模型推理需要访问明文Prompt的计算需求存在根本性矛盾57% similarUnverifiedZero将代码表示为结构化的图而非扁平的字符序列，AI操作的是节点和关系57% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1032962API
curl https://kongchang.com/api/v1/knowledge/claims/1032962MCP
get_claim(id=1032962)