Unverified50% confidenceOpinionExact time
The core challenge facing GEO lies in its opacity, as the training data composition and inference weights inside LLMs are not publicly disclosed.
1
Sources
50%
Confidence
Long-term
Relevance
10/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedSWE-bench has limitations including the risk of training data leakage, a gap in dependency complexity between the evaluation sandbox and production environments, and an inability to reflect code maintainability and security.69% similarUnverifiedVulnerabilities from the training phase can manifest in covert ways during the deployment phase, and relying solely on deployment-level safety measures is far from sufficient.69% similarUnverifiedThere is an inherent tension in dangerous capability assessments between the coverage, update frequency, and confidentiality of test sets: overly public test sets become incorporated into training data and lose effectiveness, while overly confidential tests lack peer-review credibility.68% similarUnverifiedContext confusion produces narrow misalignment, where misalignment is concentrated in specific domains semantically close to the training data, rather than a broad degradation of the model's overall behavior.68% similarUnverifiedLLM在面对开放性问题时倾向于输出训练数据中频率最高的安全表达,即听起来正确但信息密度极低的通用结论67% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/978484API
curl https://kongchang.com/api/v1/knowledge/claims/978484MCP
get_claim(id=978484)