SPHINX: A Synthetic Environment for Visual Perception and Reasoning
We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, charts, icons, and geometric primitives, each paired with verifiable ground-truth solutions, enabling both precise evaluation and large-scale dataset construction. The benchmark covers 25 task types spanning symmetry detection, geometric transformations, spatial reasoning, chart interpretation, and sequence prediction. Evaluating recent large vision-language models (LVLMs) shows that even state-of-the-art GPT-5 attains only 51.1% accuracy, well below human performance. Finally, we demonstrate that reinforcement learning with verifiable rewards (RLVR) substantially improves model accuracy on these tasks and yields gains on external visual reasoning benchmarks, highlighting its promise for advancing multimodal reasoning.
69280993243b2216fb75ce47
Md Tanvirul Alam
claimed_verified
2025-11-27T20:39:46.813Z
620c1a977af55d45f5519914
/avatars/4d2fb7c1a5ad5dabdb8888fa2fe72e65.svg
Tanvirul Alam
xashru
user
xashru
69280993243b2216fb75ce48
Saksham Aggarwal
69280993243b2216fb75ce49
Justin Yang Chae
69280993243b2216fb75ce4a
Nidhi Rastogi
SPHINX: A Synthetic Environment for Visual Perception and Reasoning
We present Sphinx, a synthetic environment for visual perception and reasoning that targets core cognitive primitives. Sphinx procedurally generates puzzles using motifs, tiles, charts, icons, and geometric primitives, each paired with verifiable ground-truth solutions, enabling both precise evaluation and large-scale dataset construction. The benchmark covers 25 task types spanning symmetry detection, geometric transformations, spatial reasoning, chart interpretation, and sequence prediction. Evaluating recent large vision-language models (LVLMs) shows that even state-of-the-art GPT-5 attains only 51.1% accuracy, well below human performance. Finally, we demonstrate that reinforcement learning with verifiable rewards (RLVR) substantially improves model accuracy on these tasks and yields gains on external visual reasoning benchmarks, highlighting its promise for advancing multimodal reasoning.
Sphinx, a synthetic environment for visual perception and reasoning, evaluates large vision-language models and demonstrates that reinforcement learning with verifiable rewards improves model accuracy on diverse tasks.
synthetic environment visual perception reasoning cognitive primitives procedural generation motifs tiles charts icons geometric primitives ground-truth solutions dataset construction symmetry detection geometric transformations spatial reasoning chart interpretation sequence prediction large vision-language models GPT-5 reinforcement learning verifiable rewards multimodal reasoning