arXiv:2602.02465v2 Announce Type: replace-cross Abstract: Frontier models are transitioning from multimodal large language models (MLLMs) that merely ingest visual information to unified multimodal models (UMMs) capable of native interleaved generation. This shift has sparked interest in using intermediate visualizations as a reasoning aid, akin to human mental imagery. Central to this idea is the ability to form, maintain, and manipulate visual representations in a goal-oriented manner. To evaluate and probe this capability, we develop MentisOculi, a procedural, stratified suite of multi-step
Frontier models are progressing from basic multimodal ingestion to unified models capable of interleaved generation, creating a critical need to evaluate reasoning capabilities akin to human mental imagery.
This research introduces a new benchmark to rigorously test AI's ability to form, maintain, and manipulate visual representations, which is fundamental for advanced AI reasoning and autonomous function.
The development of 'MentisOculi' provides a standardized and procedural method to assess the critical 'mental imagery' aspect of AI, influencing the development direction of future multimodal AI models.
- · AI researchers
- · Multimodal AI developers
- · AI ethics and safety organizations
- · AI models lacking strong visual reasoning
- · Companies relying on superficial AI multimodal capabilities
The benchmark will highlight current limitations in AI's reasoning with mental imagery, guiding future model improvements.
Improved visual reasoning capabilities could accelerate the development of more robust AI agents and embodied AI.
Advanced AI agents with human-like mental imagery could profoundly impact automation across complex, dynamic environments, reducing the need for explicit step-by-step instructions.
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Read at arXiv cs.LG