Unverified50% confidenceFactExact time
LLMs perform extremely poorly at directly generating valid Redcode code in a zero-shot manner, because there is almost no relevant content in their training data.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/8/2026
First Seen
Valid until: 1/6/2027
Sources
Related Entities
Related Claims
UnverifiedLarge models perform better when processing decompiled pseudocode (such as C-like pseudocode output by Ghidra), because the training corpus contains a large amount of high-level language code, while their ability to understand raw assembly instructions is relatively limited.68% similarUnverifiedLLM不理解代码语义，不执行代码、不验证API是否存在、不检查类型是否匹配，只是基于概率分布生成看起来最合理的文本序列65% similarUnverifiedPrompt injection exploits not a vulnerability in the code parser, but the model's own insufficient awareness of the boundary between instructions and data65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/988587API
curl https://kongchang.com/api/v1/knowledge/claims/988587MCP
get_claim(id=988587)