Text2Model: Modeling Copilots for Text-to-Model Translation

Serdar Kadioglu; Karthik Uppuluri; Akash Singirikonda

cs.AIApr 14, 2026

Text2Model: Modeling Copilots for Text-to-Model Translation

Authors: Serdar Kadioglu, Karthik Uppuluri, Akash Singirikonda

Organizations: AI Center of Excellence, Fidelity Investments · Department of Computer Science, Brown University

Abstract

There is growing interest in leveraging large language models (LLMs) for text-to-model translation and optimization tasks. This paper aims to advance this line of research by introducing Text2Model and Text2Zinc. Text2Model is a suite of copilots based on several LLM strategies with varying complexity, along with an online leaderboard. Text2Zinc is a cross-domain dataset for capturing optimization and satisfaction problems specified in natural language, along with an interactive editor with built-in AI assistant. While there is an emerging literature on using LLMs for translating combinatorial problems into formal models, our work is the first attempt to integrate both satisfaction and optimization problems within a unified architecture and dataset. Moreover, our approach is solver-agnostic unlike existing work that focuses on translation to a solver-specific model. To achieve this, we leverage MiniZinc's solver-and-paradigm-agnostic modeling capabilities to formulate combinatorial problems. We conduct comprehensive experiments to compare execution and solution accuracy across several single- and multi-call strategies, including; zero-shot prompting, chain-of-thought reasoning, intermediate representations via knowledge-graphs, grammar-based syntax encoding, and agentic approaches that decompose the model into sequential sub-tasks. Our copilot strategies are competitive, and in parts improve, recent research in this domain. Our findings indicate that while LLMs are promising they are not yet a push-button technology for combinatorial modeling. We contribute Text2Model copilots and leaderboard, and Text2Zinc and interactive editor to open-source to support closing this performance gap.

Figures & tables

Figure 1: Text2Model copilots leveraging the Text2Zinc dataset.

Figure 2: Distribution of problems across application domains.

Panel of Figure 3 — Figure 3: An example input with description, parameters, metadata, and output fields.

Panel of Figure 4 — Figure 4: An example MiniZinc model.

Panel of Figure 5 — Figure 5: A data instance (left) and the corresponding output produced by the MiniZinc model (right).

Figure 6: Text2Zinc Editor for dataset curation and validation with built-in AI Assistant.

	IndustryOr	Mamo-Easy	Mamo-Complex	Nl4Opt
Orlm Pass@1	38.0%	82.3%	37.4%	85.7%
Our CoT Pass@1 (from Table 2 )	49.0%	83.90%	39.34%	78.37%
Orlm Pass@2	40.0%	83.7%	49.8%	88.6%
Orlm Pass@4	44.0%	85.9%	56.9%	91.4%
Orlm Pass@8	49.0%	88.4%	72.1%	93.0%
Our Best Pass@5 (from Table 2 )	57.0%	84.97%	55.92%	78.37%

Table 4: Comparison with Orlm results from the original paper ( Huang et al. (2024a) )

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

	# LP	# MIP	# CP
NLP4LP	54	13	0
ComplexOR	25	12	0
LPWP	1101	0	0
Text2Zinc	64	31	15

Appendix

Table 5: Distribution of problem types across datasets (only displaying verified Text2Zinc instances).

copilot Strategy	LLM	Execution Accuracy (%)	Solution Accuracy (%)	#LLM Query
Single-Call Strategies
Baseline	GPT-4	32.73	17.27	1
Chain-of-Thought (CoT)	GPT-4o	60.91	34.55	1
Chain-of-Thought (CoT)	GPT-4	57.27	28.18	1
Multi-Call Strategies
Knowledge Graph	GPT-4	48.18	25.45	2

Appendix

Table 9: Experimental results with GPT-4 and GPT-4o comparing execution and solution accuracy across different prompting strategies and models on the Text2Zinc dataset. Underlined indicates best performance within each strategy and Bold indicates the overall best performance. We also report the number of LLM queries required.

Error Type	Commentary
Syntax Errors	These errors indicate issues with the MiniZinc code syntax. They may include unexpected tokens, missing semicolons, or incorrect use of language constructs. For example: Error: syntax error, unexpected where, expecting ’)’ .
Undefined Identifiers	These errors are due to the use of variables or identifiers that have not been declared or are out of scope. Example: Error: type error: undefined identifier ‘i’, did you mean ‘X’? .
Array and Indexing Issues	Errors related to improper use of arrays or indexing problems. This can include out-of-bounds errors or incorrect array dimensions. Example: Error: type error: initialisation value for ‘Downtime’ has invalid type-inst: expected ‘array[int] of int’, actual ‘array[int,int] of int’ .
Function Not Found Errors	These errors occur when attempting to use functions or predicates that do not exist in the current MiniZinc environment or have incorrect signatures. This can happen with missing imports or incorrect function names. Example: Error: type error: no function or predicate with this signature found: ‘..o(float)’ .
Variable Redefinition Errors	These errors happen when the same variable is assigned or defined multiple times inappropriately, violating MiniZinc ’s variable declaration rules. Example: Error: type error: multiple assignment to the same variable .
Flattening Errors	These errors occur during the model flattening phase when MiniZinc attempts to convert the high-level model into a lower-level representation. Often related to unbounded variables or overly complex expressions that cannot be properly linearized. Example: Error: flattening error: unbounded coefficient in linear expression .

Appendix

Table 10: Categorization of MiniZinc Errors

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Harnessing Language Models as Optimization Modeling Agents

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Large language models (LLMs) can translate natural-language optimization problems into solver programs, but reliably producing correct formulations and implementations remains challenging. Existing approaches improve optimization modeling through agentic workflows or task-specific post-training. This raises a practical question: can a lightweight harness using a single model role achieve competitive performance without additional training? We introduce OPTICYCLE and OPTISTATE, two lightweight inference harnesses for optimization modeling, and evaluate them on nine benchmarks comprising 2,634 problems using 11 Qwen variants spanning 0.6B-27B parameters. Our results show that execution-guided revision consistently improves macro accuracy over direct generation for all evaluated models, with gains of up to 49.46 percentage points. Despite their simplicity, both harnesses outperform six adapted agentic workflows in same-model comparisons and four evaluated post-trained optimization models at comparable model sizes, without additional training. Explicit formulation state provides further but model-dependent improvements, suggesting that additional structure is not universally beneficial.

Abstract

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