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This study examines whether active exploration helps adults overcome the 'conjunctive handicap' in causal reasoning, comparing human performance to LLMs in a blicket detector task. Results show that active exploration improves conjunctive reasoning in adults, though some gaps remain, and LLMs approach human accuracy but explore less efficiently.
SpatialWorld is a unified benchmark for evaluating interactive spatial reasoning in multimodal agents across diverse real-world tasks, revealing that even the strongest models achieve low task success rates.
Introduces Target Viewpoint Reproduction (TVR) task and TVRBench benchmark for evaluating foundation models' ability to actively adjust 3D viewpoints to match target images. Experiments reveal significant limitations in current open and closed-source models, with a unified post-training framework boosting success rates from ~12% to ~51%.
Introduces ESI-BENCH, a comprehensive benchmark for embodied spatial intelligence built on OmniGibson, covering 10 task categories and 29 subcategories. Experiments show active exploration substantially outperforms passive approaches, with failures mainly due to action blindness rather than perception, revealing a metacognitive gap in models compared to humans.