Verified65% confidenceFactExact time
The way Decision-1 is used is to give it a fixed set of candidate options, and it returns calibrated probability values for each option in a single inference pass.
3
Sources
65%
Confidence
Long-term
Relevance
10/10/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedDecision-1 receives a state description and a fixed set of candidate answers, returns calibrated probability values for each option, and outputs structured JSON rather than free text.80% similarVerifiedDecision models focus on answering yes/no judgments, multiple-choice questions, and probability-scored rating-type problems.79% similarUnverifiedThe decision logic for choosing a prompting pattern is: zero-shot for simple deterministic tasks, few-shot when a specific format or style is needed, CoT or ToT for complex reasoning, RAG when external information is needed, ReAct or Agent for multi-step tool calling, and optimization + evaluation + version management as a trio for high-risk production scenarios.76% similarUnverifiedThe decision layer provides typed probabilistic decisions, each decision carrying its own uncertainty and escalation flag, and at runtime it can choose to hand off rather than guess when uncertain.74% similarUnverifiedWity-1 is a "reasoning decision model" whose strategy is to reason only to the point of sufficiency, stopping when the LLM's internal probability distribution indicates that an answer has been reached.72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/994495API
curl https://kongchang.com/api/v1/knowledge/claims/994495MCP
get_claim(id=994495)