Enterprise Agents Must Do More Than Retrieve—They Must Judge Which Records to Trust

Introduction

An enterprise knowledge base is rarely a clean, static collection of facts. A project decision may first appear in a chat, later be formalized in a document, and then be revised in a chart or a new version of a report. When these records disagree, an enterprise agent is not solving a simple lookup problem. It must trace evidence, understand how records relate to one another, and decide whether the available material actually supports a conclusion.

MMEnterprise-Bench is designed to evaluate precisely this setting. Built from simulated collaboration projects, it contains 7,140 textual and visual records and 1,613 questions. The benchmark asks whether multimodal agents can reason over a workspace whose information changes as people share, correct, and revise it.

Key points

• Evidence retrieval is only the starting point. The benchmark covers evidence reasoning, conflict resolution, and unanswerability. Agents must combine documents, chats, figures, and other record types rather than match a phrase in a single file.
• Conflicts have multiple forms. Two sources may report different versions of the same fact, while a later record may modify an earlier one. Choosing the most recent item mechanically is not enough; the agent must inspect the surrounding context and the relationship between records.
• Multiple images create an additional challenge. When relevant information is distributed across several visual records, the model must first assemble the pieces before drawing an inference. This is substantially harder than answering from one image or page.
• Abstention is part of reliability. If the workspace does not contain enough evidence, a trustworthy system should say that the answer cannot be established instead of filling the gap with a plausible guess.
• More retrieval does not guarantee better reasoning. Increasing the retrieval budget or using a larger retriever can improve evidence recall, but the gain does not consistently translate into answer accuracy. The bottleneck may be interpretation rather than discovery.

What the evaluation shows

The authors evaluate 10 proprietary and open-source multimodal LLMs. Even the strongest systems struggle with cross-source and temporal conflicts, as well as questions that require reasoning across multiple images. The finding matters because enterprise-agent quality cannot be summarized by retrieval recall or ordinary question-answering accuracy alone. A system must also explain how evidence supports an answer, distinguish competing versions, and avoid overclaiming when the record is incomplete.

Why it matters

For enterprise developers, the benchmark points to the importance of preserving source, version, and time information in knowledge systems. Retrieval should not return isolated snippets without the context needed to judge their status. For model researchers, promising directions include conflict detection, evidence ranking, cross-modal synthesis, and calibrated uncertainty—not simply larger contexts or more retrieved items.

The broader lesson is that an agent that can answer is not automatically an agent that can be trusted. A dependable workplace assistant must separate conclusions supported by evidence from facts with competing versions and questions that the available records cannot resolve.

Source: Hugging Face Daily Papers