arXiv:2606.19965v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly expected to act on visual information, yet the same scene may require different actions under different task contexts. How reliably can a model turn the same visual evidence into the action required by the current context? To answer this question, we introduce \textsc{ROSE} (\textbf{R}eference-conditioned \textbf{O}ddity and \textbf{S}ymbolic \textbf{E}xecution), a controlled benchmark that holds the visual scene fixed while varying region constraints and required symbolic outputs. Throu
The rapid advancement of MLLMs necessitates more robust benchmarking for their practical application in diverse contexts.
Improving the ability of MLLMs to reliably translate visual information into context-dependent actions is crucial for the development of deployable, autonomous AI systems.
The introduction of a new benchmark like ROSE provides a standardized method to evaluate and drive progress in perception-to-action capabilities of multimodal models, closing a critical gap in MLLM development.
- · AI researchers
- · Multimodal model developers
- · AI application sectors
- · Models with poor contextual understanding
- · Developers relying on heuristic-based action policies
Improvements in MLLM architectures to better handle context-dependent actions will accelerate.
More reliable autonomous AI agents will emerge, capable of nuanced task execution in varying environments.
The integration of such highly capable agents could lead to significant automation advancements across industries, potentially impacting labor markets.
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Read at arXiv cs.AI