Computer Science > Computation and Language
arXiv:2603.08910 (cs)
[Submitted on 9 Mar 2026]
Title:SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation
Authors:Hexuan Wang, Yaxuan Ren, Srikar Bommireddypalli, Shuxian Chen, Adarsh Prabhudesai, Rongkun Zhou, Elina Baral, Philipp Koehn
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Abstract:We introduce SciTaRC, an expert-authored benchmark of questions about tabular data in scientific papers requiring both deep language reasoning and complex computation. We show that current state-of-the-art AI models fail on at least 23% of these questions, a gap that remains significant even for highly capable open-weight models like Llama-3.3-70B-Instruct, which fails on 65.5% of the tasks. Our analysis reveals a universal "execution bottleneck": both code and language models struggle to faithfully execute plans, even when provided with correct strategies. Specifically, code-based methods prove brittle on raw scientific tables, while natural language reasoning primarily fails due to initial comprehension issues and calculation errors.
| Comments: | |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.08910 [cs.CL] |
| (or arXiv:2603.08910v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08910
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View a PDF of the paper titled SciTaRC: Benchmarking QA on Scientific Tabular Data that Requires Language Reasoning and Complex Computation, by Hexuan Wang and Yaxuan Ren and Srikar Bommireddypalli and Shuxian Chen and Adarsh Prabhudesai and Rongkun Zhou and Elina Baral and Philipp Koehn
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