Unverified50% confidenceFactExact time
Mouse-1 outputs a probability distribution rather than natural language text, and its output layer is constrained to the predefined action space of Agent frameworks.
1
Sources
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
Related Entities
Related Claims
UnverifiedThis experiment directly converted the model's output action probabilities into game operations, rather than generating text.68% similarUnverifiedMouse-1's core idea is to separate decision-making from reasoning: it does not handle complex logical reasoning or code generation, but instead quickly returns probability values based on the Agent's state and the available options.68% similarUnverifiedMouse-1's "separation of decision and reasoning" belongs to the same engineering approach of "using small models for high-frequency subtasks" as mixture-of-experts (MoE) routing and speculative decoding.67% similarUnverified神经网络输出连续概率分布而符号推理引擎需要离散结构化输入，两者接口设计（神经-符号接口问题）至今没有公认最优解62% similarUnverifiedSilent failures in LLM Agents stem from the fact that language models are fundamentally probabilistic text generators and cannot truly distinguish between describing an action and executing an action.60% similar
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