TOBench: A Task-Oriented Omni-Modal Benchmark for Real-World Tool-Using Agents
Learn to evaluate tool-using agents with TOBench, a benchmark for real-world omni-modal tasks, and improve their performance in professional workflows
- Build a tool-using agent using a framework like Python or TensorFlow to interact with external tools and process multimodal inputs
- Configure the agent to operate in a realistic professional workflow, such as data analysis or content creation
- Test the agent's performance using TOBench, evaluating its ability to interpret multimodal inputs, coordinate external tools, and produce a final result
- Apply the results of the evaluation to refine the agent's architecture and improve its performance in omni-modal tasks
- Compare the performance of different tool-using agents using TOBench to identify best practices and areas for improvement
AI researchers and engineers working on tool-using agents can benefit from TOBench to evaluate and improve their models' performance in realistic professional workflows. This benchmark can help them identify areas for improvement and optimize their agents' abilities to interpret multimodal inputs and coordinate external tools.
💡 TOBench provides a comprehensive evaluation framework for tool-using agents, bridging the gap between benchmark settings and real-world professional workflows
🤖 Evaluate tool-using agents with TOBench, a new benchmark for real-world omni-modal tasks! 📊
Key Takeaways
Learn to evaluate tool-using agents with TOBench, a benchmark for real-world omni-modal tasks, and improve their performance in professional workflows
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Abstract:
arXiv:2605.16909v1 Announce Type: new Abstract: Tool-using agents are increasingly expected to operate across realistic professional workflows, where they must interpret multimodal inputs, coordinate external tools, inspect intermediate artifacts, and revise their actions before producing a final result. Existing benchmarks, however, often evaluate tool use, computer use, and multimodal reasoning in isolation, leaving a gap between benchmark settings and end-to-end omni-modal tool use in the rea
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