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cs.AI2025
TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models
Shima Imani, Seungwhan Moon, Lambert Mathias +2
Reliable mathematical and scientific reasoning remains an open challenge for large vision-language models. Standard final-answer evaluation often masks reasoning errors, allowing s…
cs.AI2025
SymPyBench: A Dynamic Benchmark for Scientific Reasoning with Executable Python Code
Shima Imani, Seungwhan Moon, Adel Ahmadyan +3
We introduce, a large-scale synthetic benchmark of 15,045 university-level physics problems (90/10% train/test split). Each problem is fully parameterized, supporting an effectivel…
cs.AI2025
PRiSM: An Agentic Multimodal Benchmark for Scientific Reasoning via Python-Grounded Evaluation
Shima Imani, Seungwhan Moon, Adel Ahmadyan +3
Evaluating vision-language models (VLMs) in scientific domains like mathematics and physics poses unique challenges that go far beyond predicting final answers. These domains deman…