Unverified50% confidenceFactExact time
The Bayesian tool graph models the call dependency relationships between tools as a directed probabilistic graph, where nodes represent tools and edges represent the conditional probability of calling B after calling A.
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50%
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Long-term
Relevance
10/5/2026
First Seen
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Related Claims
UnverifiedAdding schema validation at the tool-calling stage constrains the uncertainty of model outputs to a controllable range.76% similarUnverifiedThe Bayesian tool graph simulator can estimate the impact on subsequent success probability of choosing tool X instead of Y at a given step without actual execution, thereby generating counterfactual contrastive samples.74% similarUnverified贝叶斯网络通过有向无环图显式建模变量间的条件依赖关系，适合捕捉因果链条72% similarVerifiedTool 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.71% similarUnverifiedChain-of-Thought prompting encourages the model to reason step by step, few-shot prompting helps the model understand the task format by providing examples, and System Prompts are used to set the model's role and behavioral constraints.70% similar
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