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Multimodal Knowledge Agent (MKA) Showcase Examples

Tests whether AI agents can interpret complex visual files, synthesize information across documents, and produce finished professional deliverables. Its 10 public tasks span distinct domains and require multimodal perception and multi-step tool use.

Introduction

Many professional tasks are visual in ways that plain-text benchmarks miss: a key measurement may sit in a schematic, a map, or a chart, while the final answer must be delivered as a usable report or spreadsheet. This showcase makes that gap inspectable with real, multi-file workspaces rather than isolated image questions.

What Sets It Apart
  • Each task combines multiple source files, including visual artifacts whose meaning depends on layout and domain context. This tests whether an agent can perceive and cross-reference evidence, not just extract text.
  • Tasks call for tool use and standalone deliverables such as spreadsheets, slide decks, and reports. Success therefore includes turning analysis into professional work products.
  • The public release covers 10 roles across 10 domains, while the broader evaluation set contains 200 tasks. The showcase lets researchers inspect the task format, but it is not a substitute for access to the private scored set.
  • Expert rubrics emphasize factual accuracy (75%) alongside visual design (25%), making presentation quality part of the evaluation rather than an afterthought.
Who It’s For—and the Trade-off

A good fit for researchers building or evaluating agents that work with visual documents, technical diagrams, and multi-file tasks. It is less suitable for measuring general language ability or comparing systems on a large, fully public test set: only 10 examples are released for inspection, and results on them may not represent performance on the private benchmark.

Information

  • Websitehuggingface.co
  • OrganizationsGoogle DeepMind, Artificial Analysis
  • AuthorsRohan Doshi, Ben Caine
  • Published date2026/11/10

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