Unverified50% confidenceFactExact time
In classic paradigms such as ReAct, tool-call decisions are made by the same large model, and when the task chain is long the loop can run dozens to hundreds of rounds, with latency and cost accumulating linearly.
1
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50%
Confidence
Long-term
Relevance
10/21/2026
First Seen
Sources
Mouse-1: An Efficient Decision Model Built for AI Agents, 163x Cheaper
producthuntProductHunt Daily10/21/2026
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Related Claims
UnverifiedAgents in the ReAct framework need to repeatedly perform think–act–observe cycles to complete tasks, and a moderately complex task may require 5 to 20 or more iterations.80% similarUnverifiedThe cost of ReAct is that each loop adds one model call, with latency and token consumption growing linearly with the number of steps, so real systems typically set a maximum number of iterations and early-termination conditions.77% similarUnverifiedIn actual engineering, a maximum number of iterations (Max Iterations) is typically set for the ReAct loop to prevent the Agent from entering an infinite loop or consuming excessive resources77% similarUnverified推理-行动循环（ReAct Loop）可以持续多轮，AI分析状态、决定调用工具、获取结果后再规划下一步直至任务完成77% similarUnverifiedEach step of the iterative loop consumes API call quota for the large model, and costs can accumulate quickly for complex tasks.77% similar
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