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    <abstract>Reliable mathematical and scientific reasoning remains an open challenge for large vision–language models (VLMs). Standard final-answer evaluation often masks reasoning errors, allowing silent failures to persist. To address this gap, we introduce TRACE (Transparent Reasoning And Consistency Evaluation), a framework for analyzing, diagnosing, and improving reasoning in VLMs. At its core, TRACE leverages Auxiliary Reasoning Sets (ARS), compact sub-question–answer pairs that decompose complex problems, evaluate intermediate steps through consistency-based metrics, and expose failures overlooked by standard evaluation. Our experiments show that consistency across ARS is linked to final-answer correctness and helps pinpoint the reasoning steps where failures arise, offering actionable signals for model improvement.</abstract>
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%0 Conference Proceedings
%T TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models
%A Imani, Shima
%A Moon, Seungwhan
%A Mathias, Lambert
%A Zhang, Lu
%A Damavandi, Babak
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F imani-etal-2026-trace
%X Reliable mathematical and scientific reasoning remains an open challenge for large vision–language models (VLMs). Standard final-answer evaluation often masks reasoning errors, allowing silent failures to persist. To address this gap, we introduce TRACE (Transparent Reasoning And Consistency Evaluation), a framework for analyzing, diagnosing, and improving reasoning in VLMs. At its core, TRACE leverages Auxiliary Reasoning Sets (ARS), compact sub-question–answer pairs that decompose complex problems, evaluate intermediate steps through consistency-based metrics, and expose failures overlooked by standard evaluation. Our experiments show that consistency across ARS is linked to final-answer correctness and helps pinpoint the reasoning steps where failures arise, offering actionable signals for model improvement.
%U https://aclanthology.org/2026.eacl-long.166/
%P 3611-3625

Markdown (Informal)

[TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models](https://aclanthology.org/2026.eacl-long.166/) (Imani et al., EACL 2026)

• TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models (Imani et al., EACL 2026)

ACL

• Shima Imani, Seungwhan Moon, Lambert Mathias, Lu Zhang, and Babak Damavandi. 2026. TRACE: A Framework for Analyzing and Enhancing Stepwise Reasoning in Vision-Language Models. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3611–3625, Rabat, Morocco. Association for Computational Linguistics.