Unverified50% confidenceFactExact time
In research on technology adoption and employment change, causal identification requires an exogenous shock as an instrumental variable, but in reality the exclusion restriction is very hard to satisfy.
1
Sources
50%
Confidence
Long-term
Relevance
10/25/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe common bottleneck of existing causal reinforcement learning methods is the acquisition of causal structure, which either needs to be learned from data (error-prone) or manually specified by experts (costly).76% similarUnverifiedAutoregressive sampling is essentially learning statistical correlations rather than causal mechanisms, which leads to a fundamental bottleneck in physical tasks.73% similarUnverifiedThe refusal tendencies and risk-identification capabilities formed during alignment training can be partially transferred into permission-gating mechanisms for proactive behavior, but proactive scenarios introduce a new difficulty—errors often manifest as "doing too much" rather than "saying something wrong," and their cost and reversibility need to be designed separately.70% similarUnverifiedSome opponents point out that hard constraints such as compute, data, and experimental validation exist in reality, which may make the recursive self-improvement process far slower than theoretical设想.69% similarVerifiedHuman-in-the-loop mechanisms face the challenge of approval fatigue, and agents may decompose malicious behavior into seemingly harmless small steps to evade single-point approvals, requiring complementary analysis of the overall intent of behavioral sequences.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1094915API
curl https://kongchang.com/api/v1/knowledge/claims/1094915MCP
get_claim(id=1094915)