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ARXIV:2605.12154 · MULTIMODAL OPTIMIZATION · SUBMITTED 13 MAY · 20:59 UTC · FRESHNESS STALE
ARXIV:2605.12154MULTIMODAL OPTIMIZATIONSUBMITTED 13 MAY · 20:59 UTCFRESHNESS STALEZhong Li · Qi Huang · Yuxuan Zhu · Mohammad Mohammadi Amiri · Niki van Stein · Thomas Bäck · +3 at arXiv
A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs.
Opportunity summary
Pain A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are…
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entirely text-only.
ScienceToStartup currently rates this 6.0/10 on the public viability pass. The results show that the task remains far from solved: the best two models reach 52.1% and 51.3% pass@1, while on average across the…
Multimodal Optimization moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
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A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs.
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10.48550/arXiv.2605.12154A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs.
Abstract
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entirely text-only. This omits many optimization-modeling tasks that arise in operational practice, where requirements are described in text but instance information is conveyed through visual artifacts such as tables, graphs, maps, schedules, and dashboards. We introduce multimodal optimization modeling, a benchmark setting in which models must construct both a mathematical formulation and executable solver code from a text-and-visual problem specification. To evaluate this setting, we develop a solver-grounded framework that generates structured optimization instances, verifies each with an exact solver, and builds both the model-facing inputs and hidden reference files from the same verified source. We instantiate the framework as MM-OptBench, a benchmark of 780 solver-verified instances spanning 6 optimization families, 26 subcategories, and 3 structural difficulty levels. We evaluate 9 multimodal large language models (MLLMs), including 6 frontier general-purpose models and 3 math-specialized models, with aggregate, family-level, difficulty-level, and failure-mode analyses. The results show that the task remains far from solved: the best two models reach 52.1% and 51.3% pass@1, while on average across the six general-purpose MLLMs, pass@1 is 43.4% on easy instances and 15.9% on hard instances. All three math-specialized MLLMs solve 0/780 instances. Failure attribution shows that errors arise both when extracting instance data from text and visuals and when turning extracted data into solver-correct formulations and code. MM-OptBench provides a testbed for solver-grounded, decision-oriented multimodal intelligence.
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PROBLEM
A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entirely tex...
METHOD
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entire...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. The results show that the task remains far from solved: the best two models reach 52.1% and 51.3% pass@1, while on average across the six general-purpose MLLMs, pass@1 is 43.4% on easy instances and 15.9%...
WHY NOW
Multimodal Optimization moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
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A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entirely text-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Optimization modeling translates real decision-making problems into mathematical optimization models and solver-executable implementations. Although language models are increasingly used to generate optimization formulations and solver code, existing benchmarks are almost entirely text-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. The results show that the task remains far from solved: the best two models reach 52.1% and 51.3% pass@1, while on average across the six general-purpose MLLMs, pass@1 is 43.4% on easy instances and 15.9% on hard instances. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multimodal Optimization moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark for multimodal optimization modeling that evaluates LLMs on constructing optimization models from text and visual inputs.
Segment
Multimodal Optimization
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