Unverified50% confidenceFactExact time
Recursive language models fundamentally eliminate the failure mode of cheating by using information outside the subtask through context isolation, and can only learn truly recursive rules.
1
Sources
50%
Confidence
Long-term
Relevance
9/21/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedCurrent large language models lack real-world perception interfaces and causal reasoning capabilities, and are essentially doing pattern matching rather than intent understanding76% similarVerifiedLarge language models are prone to forgetting early information or hallucinating during long conversations, which in programming manifests as fabricating non-existent APIs or generating logically incorrect code.75% similarUnverifiedThe core of linguistic illegibility attacks lies in exploiting the form of expression itself to create blind spots in understanding, with attackers deceiving the defensive layers outside the model rather than the model itself.75% similarVerified提示注入的根本原因在于大语言模型缺乏区分指令层与数据层的原生机制74% similarUnverifiedCross-lingual jailbreaking exploits the asymmetry between a model's concept space and its language space, as the learning of safety-refusal behavior is highly language-specific.74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/938138API
curl https://kongchang.com/api/v1/knowledge/claims/938138MCP
get_claim(id=938138)