Unverified50% confidenceFactExact time
When the Agent receives a compound request, it triggers multiple rounds of a ReAct-style loop: the model first outputs its first round of reasoning to determine that weather data is needed, issues a call request for weather_tool(city="Bangalore"), and after the SDK executes that function it feeds the result back to the model to continue reasoning.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe core logic of AI weather models is to treat the initial atmospheric state as token input to the model, predict the next state (e.g., 6 hours later), and continue the derivation autoregressively, similar to the token-by-token generation of LLMs.65% similarUnverifiedLLM 与 Lean 结合的典型流程是模型生成候选证明策略，Lean 即时反馈该策略是否使目标状态合法推进，实现生成-验证循环65% similarUnverifiedFor customer service, shopping guide, and notification-type reply models, what is needed is the behavioral pattern of directly giving the answer; once a large number of reasoning-on samples are mixed into the training data, the model will treat first reasoning at length and then answering as the task specification.65% similarUnverifiedAgents invoke tools, access external data, perform multi-step reasoning, and dynamically adjust their behavior based on environmental feedback, which differs from how static models operate65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1023175API
curl https://kongchang.com/api/v1/knowledge/claims/1023175MCP
get_claim(id=1023175)