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ARXIV:2601.21372 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2601.21372AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents.
Opportunity summary
Pain NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents.
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized large…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications.
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
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NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents.
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Paper Pack
10.48550/arXiv.2601.21372NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents.
Abstract
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents. Such methods are often brittle, complex and frequently generating syntactically invalid or non-executable code. NEMO instead centers on remote interaction with autonomous coding agents (ACAs), treated as a first-class abstraction analogous to API-based interaction with LLMs. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications. Because ACAs execute within sandboxed environments, code produced by NEMO is executable by construction, allowing automated validation and repair. Building on this, we introduce novel coordination patterns with and across ACAs, including asymmetric validation loops between independently generated optimizer and simulator implementations (serving as a high-level validation mechanism), external memory for experience reuse, and robustness enhancements via minimum Bayes risk (MBR) decoding and self-consistency. We evaluate NEMO on nine established optimization benchmarks. As depicted in Figure 1, it achieves state-of-the-art performance on the majority of tasks, with substantial margins on several datasets, demonstrating the power of execution-aware agentic architectures for automated optimization modeling.
Source availability
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents.
METHOD
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized l...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
In this paper, we present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implementations, operating collaboratively with users or autonomously. Existing approaches typically rely on specialized large language models (LLMs) or bespoke, task-specific agents.
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. This design enables the construction of higher-level systems around ACAs that structure, consolidate, and iteratively refine task specifications.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents 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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Concepts
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NEMO transforms natural language decision problem descriptions into executable mathematical optimization implementations using autonomous coding agents.
Segment
Agents
Adoption evidence
No public code link in the paper record yet
Commercial read
7.0/10 public viability
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proof status
unverified
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Evidence coverage
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stale
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passport absent
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Artifact maturity
GitHub and Hugging Face maturity payloads
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
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Evidence
0 references, 0 sources, 17% evidence coverage.
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Current read
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Evidence
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Write integration checklist from prototype path and target workflow.
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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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