Unverified50% confidenceSolutionExact time
The hill-climbing algorithm starts from a random rotor/plugboard configuration, scores the linguistic fitness of the decrypted result, and iteratively fine-tunes it; it works extremely well for long ciphertexts but is prone to getting trapped in local optima for short messages.
1
Sources
50%
Confidence
Long-term
Relevance
9/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedClassifiers are highly sensitive to the phrasing of requests; asking the model directly to find vulnerabilities is easily intercepted and routed, whereas describing the same task using more neutral programming-language framing often does not trigger restrictions.65% similarUnverified静态调用图通过对源代码或字节码进行词法/语法分析构建,速度快但对多态调用、反射等场景准确性有限64% similarUnverifiedPrompt optimization has two technical approaches: embedding-space optimization (soft prompts) and text-space optimization (hard prompts); soft prompts are not human-readable but have higher optimization efficiency, while hard prompts retain natural language form with readability and portability.63% similarUnverifiedPrefix caching can significantly improve inference efficiency when processing batch requests with the same prefix.63% similarUnverified传统推测解码算法无论场景难易都固定草拟5-6个字符,导致算力浪费63% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/940721API
curl https://kongchang.com/api/v1/knowledge/claims/940721MCP
get_claim(id=940721)