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ARXIV:2603.07733 · OPTIMIZATION WITH LLMS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.07733OPTIMIZATION WITH LLMSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness.
Opportunity summary
Pain Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness. In contrast to formal datasets with a limited scope of parameters, our dataset included a…
This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The result showed that stronger model performed better reasonably.
Optimization with LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10.
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Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness.
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10.48550/arXiv.2603.07733Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness.
Abstract
This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances with large parameter sets, integrated with augmented data. It aimed to (1) provide an overview of LLMs' ability in large-scale problems, (2) offer suggestions to those who want to solve discrete optimization problems automatically, and (3) regard the performance as a benchmark for future research. These datasets included original, expanded and augmented datasets. Among these three datasets, the original and augmented ones aimed for evaluation while the expanded one may help finetune a new model. In the experiment, comparisons were made between strong and week models, CoT methods and No-CoT methods on various datasets. The result showed that stronger model performed better reasonably. Contrary to general agreement, it also showed that CoT technique was not always effective regarding the capability of models and disordered datasets improved performance of models on easy to-understand problems, even though they were sometimes with high variance, a manifestation of instability. Therefore, for those who seek to enhance the automatic resolution of discrete optimization problems, it is recommended to consult the results, including the line charts presented in the Appendix, as well as the conclusions drawn in this study for relevant suggestions.
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
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Viability
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Dimensions overall score 7.0
PROBLEM
Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete opti...
METHOD
This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The result showed that stronger model performed better reasonably.
WHY NOW
Optimization with LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances with large parameter sets, integrated with augmented data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
This work investigated the capabilities of different models, including the Llama-3 series of models and CHATGPT, with different forms of expression in solving discrete optimization problems by testing natural language datasets. In contrast to formal datasets with a limited scope of parameters, our dataset included a variety of problem types in discrete optimization problems and featured a wide range of parameter magnitudes, including instances with large parameter sets, integrated with augmented data.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. The result showed that stronger model performed better reasonably.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Optimization with LLMs moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Leverage Llama-3 and ChatGPT to solve discrete optimization problems with a new benchmark dataset and insights into Chain-of-Thought effectiveness.
Segment
Optimization with LLMs
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
Runnable path is not fully verified.
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
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missing
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0 references, 0 sources, 17% evidence coverage.
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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People
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ARTIFACTS
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DEFENSIBILITY
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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TIMELINE
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