pith:PVX36HTM
Text2Model: Modeling Copilots for Text-to-Model Translation
LLM copilots translate natural language into solver-agnostic MiniZinc models for both satisfaction and optimization problems.
arxiv:2604.12955 v3 · 2026-04-14 · cs.AI
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\usepackage{pith}
\pithnumber{PVX36HTMPCZG7C5GUWY3YYGI3H}
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Record completeness
Claims
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.
That LLM-generated MiniZinc models will be syntactically correct and semantically faithful to the natural-language specification for realistic combinatorial problems without requiring substantial human correction or solver-specific tuning.
Text2Model copilots and Text2Zinc dataset enable unified, solver-agnostic text-to-MiniZinc translation for both satisfaction and optimization problems using multiple LLM strategies.
Receipt and verification
| First computed | 2026-05-28T01:04:40.001454Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
7d6fbf1e6c78b26f8ba6a5b1bc60c8d9df982fbb0d7902687b8fef8d19cfe974
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/PVX36HTMPCZG7C5GUWY3YYGI3H \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7d6fbf1e6c78b26f8ba6a5b1bc60c8d9df982fbb0d7902687b8fef8d19cfe974
Canonical record JSON
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