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    <abstract>Inspired by the human expert thinking paradigm in operations research, this work introduces a new concept of reasoning tasks: Textual Constrained Optimization (TCO) problems. A TCO problem is characterized by a natural language description that implicitly specifies an underlying structured model with variables, constraints, and objectives. We propose a novel Syllogism-driven Textual Constrained Optimization Reasoning (STCOR) paradigm, driven by classical syllogistic logic. Unlike contemporary stepwise methods, our framework structures reasoning into three phases: meta-modeling, which acts as the major premise by retrieving a relevant class-driven prototype template; formalization, which serves as the minor premise by instantiating the template into an explicit logical model from textual queries; and solving, which derives the final answer as conclusion. To support the end-to-end implementation, we further develop a tri-level optimization algorithm TriRL.</abstract>
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%0 Conference Proceedings
%T STCOR: A Trilevel Syllogism-Driven Reasoning Framework
%A Yang, Keying
%A Wang, Hao
%A Jian, Chengtao
%A Yang, Kai
%Y Elaraby, Mohamed
%Y Hautli-Janisz, Annette
%Y Romberg, Julia
%Y Musi, Elena
%Y Ruggeri, Federico
%Y Lawrence, John
%S Proceedings of the 13th Workshop on Argument Mining and Reasoning
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-399-9
%F yang-etal-2026-stcor
%X Inspired by the human expert thinking paradigm in operations research, this work introduces a new concept of reasoning tasks: Textual Constrained Optimization (TCO) problems. A TCO problem is characterized by a natural language description that implicitly specifies an underlying structured model with variables, constraints, and objectives. We propose a novel Syllogism-driven Textual Constrained Optimization Reasoning (STCOR) paradigm, driven by classical syllogistic logic. Unlike contemporary stepwise methods, our framework structures reasoning into three phases: meta-modeling, which acts as the major premise by retrieving a relevant class-driven prototype template; formalization, which serves as the minor premise by instantiating the template into an explicit logical model from textual queries; and solving, which derives the final answer as conclusion. To support the end-to-end implementation, we further develop a tri-level optimization algorithm TriRL.
%U https://aclanthology.org/2026.argmining-1.1/
%P 1-4

Markdown (Informal)

[STCOR: A Trilevel Syllogism-Driven Reasoning Framework](https://aclanthology.org/2026.argmining-1.1/) (Yang et al., ArgMining 2026)

• STCOR: A Trilevel Syllogism-Driven Reasoning Framework (Yang et al., ArgMining 2026)

ACL

• Keying Yang, Hao Wang, Chengtao Jian, and Kai Yang. 2026. STCOR: A Trilevel Syllogism-Driven Reasoning Framework. In Proceedings of the 13th Workshop on Argument Mining and Reasoning, pages 1–4, San Diego, California, USA. Association for Computational Linguistics.