@inproceedings{alazraki-etal-2026-agentcoma,
title = "{A}gent{C}o{M}a: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios",
author = "Alazraki, Lisa and
Chen, Lihu and
Brassard, Ana and
Stacey, Joe and
Rahmani, Hossein A. and
Rei, Marek",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.380/",
pages = "8383--8410",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current compositional benchmarks testing these skills tend to focus on *either* commonsense or math reasoning, whereas LLM agents solving real-world tasks would require a combination of *both*. In this work, we introduce an **Agent**ic **Co**mmonsense and **Ma**th benchmark (AgentCoMa), where each compositional task requires a commonsense reasoning step *and* a math reasoning step. We test it on 61 LLMs of different sizes, model families, and training strategies. We find that LLMs can usually solve both steps in isolation, yet their accuracy drops by nearly 30{\%} on average when the two are combined. This is a substantially greater performance gap than the one we observe in prior compositional benchmarks that combine multiple steps of the same reasoning type. In contrast, non-expert human annotators can solve the compositional questions and the individual steps in AgentCoMa with similarly high accuracy. Furthermore, we conduct a series of interpretability studies to better understand the performance gap, examining neuron patterns, attention maps and membership inference. Our work underscores a substantial degree of model brittleness in the context of mixed-type compositional reasoning and offers a test bed for future improvement."
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<abstract>Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current compositional benchmarks testing these skills tend to focus on *either* commonsense or math reasoning, whereas LLM agents solving real-world tasks would require a combination of *both*. In this work, we introduce an **Agent**ic **Co**mmonsense and **Ma**th benchmark (AgentCoMa), where each compositional task requires a commonsense reasoning step *and* a math reasoning step. We test it on 61 LLMs of different sizes, model families, and training strategies. We find that LLMs can usually solve both steps in isolation, yet their accuracy drops by nearly 30% on average when the two are combined. This is a substantially greater performance gap than the one we observe in prior compositional benchmarks that combine multiple steps of the same reasoning type. In contrast, non-expert human annotators can solve the compositional questions and the individual steps in AgentCoMa with similarly high accuracy. Furthermore, we conduct a series of interpretability studies to better understand the performance gap, examining neuron patterns, attention maps and membership inference. Our work underscores a substantial degree of model brittleness in the context of mixed-type compositional reasoning and offers a test bed for future improvement.</abstract>
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%0 Conference Proceedings
%T AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
%A Alazraki, Lisa
%A Chen, Lihu
%A Brassard, Ana
%A Stacey, Joe
%A Rahmani, Hossein A.
%A Rei, Marek
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F alazraki-etal-2026-agentcoma
%X Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current compositional benchmarks testing these skills tend to focus on *either* commonsense or math reasoning, whereas LLM agents solving real-world tasks would require a combination of *both*. In this work, we introduce an **Agent**ic **Co**mmonsense and **Ma**th benchmark (AgentCoMa), where each compositional task requires a commonsense reasoning step *and* a math reasoning step. We test it on 61 LLMs of different sizes, model families, and training strategies. We find that LLMs can usually solve both steps in isolation, yet their accuracy drops by nearly 30% on average when the two are combined. This is a substantially greater performance gap than the one we observe in prior compositional benchmarks that combine multiple steps of the same reasoning type. In contrast, non-expert human annotators can solve the compositional questions and the individual steps in AgentCoMa with similarly high accuracy. Furthermore, we conduct a series of interpretability studies to better understand the performance gap, examining neuron patterns, attention maps and membership inference. Our work underscores a substantial degree of model brittleness in the context of mixed-type compositional reasoning and offers a test bed for future improvement.
%U https://aclanthology.org/2026.acl-long.380/
%P 8383-8410
Markdown (Informal)
[AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios](https://aclanthology.org/2026.acl-long.380/) (Alazraki et al., ACL 2026)
ACL