Unverified50% confidenceOpinionExact time
The paper's authors believe that a training objective that scores each sample independently cannot constrain the policy to maintain consistent behavior across different interfaces, which is the root cause of the Agent interface fragility problem.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedA code of conduct alone cannot resolve AI safety issues; whether a model deceives humans is often the result of the combined effect of training data, optimization objectives, and deployment environment.75% similarUnverified多智能体辩论通过硬性约束每个agent的立场使其无法轻易妥协,从机制设计层面绕开RLHF的训练缺陷74% similarUnverified作者认为CodeAct没能占据主流的核心原因是制度性摩擦,包括模型训练偏向、通信协议假定、MCP设计局限、工具链依赖和安全沙箱挑战73% similarUnverifiedRelying solely on model-side safety training is considered insufficient to address the risks posed by agents in real-world environments73% similarUnverifiedThe author noted that some benchmark tasks use synthetic data, that the benchmarks are exploratory in nature, and that they do not constitute proof of universal superiority over direct training methods.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1037939API
curl https://kongchang.com/api/v1/knowledge/claims/1037939MCP
get_claim(id=1037939)