Unverified50% confidenceDecision RuleExact time
When there is a systematic and unobservable difference between the deployment environment and the training environment, the robust contextual MDP is a modeling tool worth considering.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedEvaluating within the training distribution easily overestimates model capability, while out-of-distribution benchmarks require the model to generalize to unseen scene and interaction combinations, better exposing the gap between 'looking right' and 'being physically right'.74% similarUnverifiedThe core assumption of the research is that shared training data and similar architectures cause more capable LLMs to exhibit higher behavioral consistency with one another, thereby generating systemic risk72% similarUnverifiedFoundation models for agent tasks differ markedly in training objectives from models for general conversation; the former place more emphasis on tool calling, multi-step planning, error recovery and the ability to use environmental feedback, and their training data often contains a large number of stateful interaction trajectories.72% similarUnverified如果模型能理解行为准则背后的深层原因,即使面对训练中从未遇到的全新场景也能做出合理的行为选择,实现可泛化的对齐72% similarUnverifiedAchieving endogenous alignment requires stronger control over the training and pretraining environment in order to influence the model's ontological development.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1042051API
curl https://kongchang.com/api/v1/knowledge/claims/1042051MCP
get_claim(id=1042051)