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    <abstract>Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them. A single misread cell or incorrect operation can silently produce a plausible but wrong result. We introduce MOCA-Agent, a market-of-claims code agent that replaces free-form multi-agent debate with claim-level verification. The system decomposes each question into typed atomic claims, asks specialist trader agents to buy or sell those claims, clears their orders into confidence-weighted accept/reject decisions, and synthesizes an executable Python program from market-supported evidence. A code-aware verifier then checks the program for execution, structural consistency, and common financial reasoning errors, with at most one market-aware repair round. Across ten public benchmarks spanning financial numerical reasoning, general tabular reasoning, ESG question answering, and multimodal chart reasoning, MOCA-Agent achieves strong performance using a fixed Qwen3.6-27B backbone, including 78.3% on FinQA, 76.0% on FinanceMath, 71.2% on MultiHiertt, 86.9% on ESGenius, and 85.6% average on FinChart-Bench. These results show that aggregating evidence at the level of atomic claims, rather than whole answers, improves robustness in high-stakes numerical reasoning.</abstract>
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%0 Conference Proceedings
%T MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning
%A Abdallah, Abdelrahman
%A Elmadany, AbdelRahim A.
%A Al Natour, Sameh
%A Cavusoglu, Hasan
%A Jatowt, Adam
%A Abdul-Mageed, Muhammad
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F abdallah-etal-2026-moca
%X Financial and tabular question answering requires more than fluent reasoning: answers must be grounded in the exact facts, formulas, units, signs, and scales that support them. A single misread cell or incorrect operation can silently produce a plausible but wrong result. We introduce MOCA-Agent, a market-of-claims code agent that replaces free-form multi-agent debate with claim-level verification. The system decomposes each question into typed atomic claims, asks specialist trader agents to buy or sell those claims, clears their orders into confidence-weighted accept/reject decisions, and synthesizes an executable Python program from market-supported evidence. A code-aware verifier then checks the program for execution, structural consistency, and common financial reasoning errors, with at most one market-aware repair round. Across ten public benchmarks spanning financial numerical reasoning, general tabular reasoning, ESG question answering, and multimodal chart reasoning, MOCA-Agent achieves strong performance using a fixed Qwen3.6-27B backbone, including 78.3% on FinQA, 76.0% on FinanceMath, 71.2% on MultiHiertt, 86.9% on ESGenius, and 85.6% average on FinChart-Bench. These results show that aggregating evidence at the level of atomic claims, rather than whole answers, improves robustness in high-stakes numerical reasoning.
%U https://aclanthology.org/2026.emnlp-main.478/
%P 10980-11009

Markdown (Informal)

[MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning](https://aclanthology.org/2026.emnlp-main.478/) (Abdallah et al., EMNLP 2026)

• MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning (Abdallah et al., EMNLP 2026)

ACL

• Abdelrahman Abdallah, AbdelRahim A. Elmadany, Sameh Al Natour, Hasan Cavusoglu, Adam Jatowt, and Muhammad Abdul-Mageed. 2026. MoCA-Agent: A Market-of-Claims Code Agent for Financial and Numerical Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 10980–11009, Budapest, Hungary. Association for Computational Linguistics.