MMEnterprise-Bench: Evaluating Enterprise Agents over Evolving Multimodal Records
Enterprise agents answer questions over workplace records that evolve through team collaboration. As information is shared, corrected, and revised across documents, chats, and figures, multiple versions of the same information accumulate in the workspace. Reliable question answering therefore requires agents to reason over sufficient evidence, resolve cross-source and temporal
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Enterprise agents answer questions over workplace records that evolve through team collaboration. As information is shared, corrected, and revised across documents, chats, and figures, multiple versions of the same information accumulate in the workspace. Reliable question answering therefore requires agents to reason over sufficient evidence, resolve cross-source and temporal
This benchmark addresses a critical challenge for enterprise AI agents: reasoning over evolving, multimodal data with temporal and cross-source conflicts. For ML engineers, understanding how to build and evaluate agents capable of handling such complexity is crucial for deploying reliable and robust AI solutions in real-world business environments.