Unverified50% confidenceFactExact time
Self-reflection is achieved by having the model evaluate the quality of its own output and iteratively improve it; the Reflexion framework demonstrates that an agent can learn from failed attempts and adjust its strategy in the next iteration.
1
Sources
50%
Confidence
Long-term
Relevance
8/22/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedReflexion框架让模型评估自己输出的质量并进行迭代修正78% similarUnverifiedSupervised fine-tuning trains models to imitate correct answers labeled by humans, while reinforcement learning fine-tuning allows models to learn optimal strategies through trial and error.75% similarVerifiedModern agents typically operate based on the ReAct (Reasoning + Acting) paradigm, where the model first reasons about the current state and then decides which tool to call.74% similarUnverifiedThe core technology of Builder mode adopts the 'ReAct' (Reasoning + Acting) paradigm, in which the model first performs reasoning at each step, then decides the next action, and then observes the execution result to form a closed-loop feedback.74% similarUnverifiedTool calling allows models to dynamically invoke external functions during inference and incorporate execution results into the context of subsequent reasoning, forming a closed loop of perception, decision-making, and execution.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/786975API
curl https://kongchang.com/api/v1/knowledge/claims/786975MCP
get_claim(id=786975)