MATU quantifies uncertainty in LLM multi-agent systems by turning reasoning trajectories into embedding matrices, stacking runs into a tensor, and decomposing it to separate sources of variability.
For the Eigv(Agr), we use the final answer or every conversation to compute the entailment ma- trix, resulting in two different variants: Eigv(Agr)- answer and Eigv(Agr)-whole
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Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition
MATU quantifies uncertainty in LLM multi-agent systems by turning reasoning trajectories into embedding matrices, stacking runs into a tensor, and decomposing it to separate sources of variability.