Unverified50% confidenceFactExact time
Yalda Embedding achieves improvements on hard-negative evaluation tasks without explicitly mining or providing hard negatives for training.
1
Sources
50%
Confidence
Long-term
Relevance
10/26/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedGoodhart's Law states that when a metric becomes a target in itself it is no longer a good metric, and introducing test sets into training data causes a model's exam scores to decouple from its true capabilities63% similarUnverifiedThe shared limitation of Hebbian learning and competitive learning is the lack of an explicit optimization objective, making end-to-end fine-tuning for downstream tasks difficult, and they are typically used for feature extraction and representation learning.62% similarUnverifiedChoosing projects slightly above one's current skill level and actively seeking negative feedback in code review or model evaluation are concrete ways to turn discomfort into capability gains.62% similarUnverifiedAnthropic said that if tasks more clearly specified scope (including goals, permitted actions, and network boundaries), these failures might have been avoided, but that alignment training itself is not sufficient or fully robust, so it relies on defense in depth.61% similarUnverifiedWhen evaluating agentic coding tools, one cannot use the local metrics for measuring completion tools; instead one needs to observe higher-level signals such as task-level success rates, rework rates and the degree of human intervention.61% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098463API
curl https://kongchang.com/api/v1/knowledge/claims/1098463MCP
get_claim(id=1098463)