Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling | Signal Canvas | ScienceToStartup

Page Freshness

Signal Canvas proof surface

Canonical route: /signal-canvas/type-aware-retrieval-augmented-generation-with-dependency-closure-for-solver-executable-industrial-optimization-modeling

stale

Proof freshness: stale
Proof status: unverified
Display score: 10/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

Agent Handoff

Canonical ID type-aware-retrieval-augmented-generation-with-dependency-closure-for-solver-executable-industrial-optimization-modeling | Route /signal-canvas/type-aware-retrieval-augmented-generation-with-dependency-closure-for-solver-executable-industrial-optimization-modeling

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/type-aware-retrieval-augmented-generation-with-dependency-closure-for-solver-executable-industrial-optimization-modeling

MCP example

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source_context

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Evidence Receipt

Route status: building

Claims: 12

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

PDF: https://arxiv.org/pdf/2603.03180v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T21:31:49.672Z

Signal Canvas receipt window

Watch and verify: Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

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Watchwatch

Subject: Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

Verdict

Preparing verified analysis

GitHub Code Pulse

No public code linked for this paper yet.

Claim map

Strong 12Mixed 0Weak 0

Evidencepartial
our method constructs a domain-specific typed knowledge base by parsing heterogeneous sources, such as academic papers and solver code, into typed units and encoding their mathematical dependencies in a knowledge graph.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Given a natural-language instruction, it performs hybrid retrieval and computes a minimal dependency-closed context, the smallest set of typed symbols required for solver-executable code, via dependency propagation over the graph.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
In the first case, our method generates an executable model incorporating demand-response incentives and load-reduction constraints, achieving peak shaving while preserving profitability; conventional RAG baselines fail.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
In the second case, it consistently produces compilable models that reach known optimal solutions, demonstrating robust cross-domain generalization; baselines fail entirely.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
Ablation studies confirm that enforcing type-aware dependency closure is essential for avoiding structural hallucinations and ensuring executability, addressing a critical barrier to deploying large language models in complex engineering optimization tasks.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
In the first case, our method generates an executable model incorporating demand-response incentives and load-reduction constraints, achieving peak shaving while preserving profitability; conventional RAG baselines fail.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
In the second case, it consistently produces compilable models that reach known optimal solutions, demonstrating robust cross-domain generalization; baselines fail entirely.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
The dependency on accurate extraction and parsing from heterogeneous sources may limit its universality.
Implicationmissing
Implication not extracted yet.
Verificationpartial
partial
Evidencepartial
In the first case, our method generates an executable model incorporating demand-response incentives and load-reduction constraints, achieving peak shaving while preserving profitability; conventional RAG baselines fail.
Implicationpartial
Directly stated in abstract with specific case examples where baselines failed
Verificationpartial
partial
Evidencepartial
our method constructs a domain-specific typed knowledge base by parsing heterogeneous sources, such as academic papers and solver code, into typed units and encoding their mathematical dependencies in a knowledge graph.
Implicationpartial
Explicitly described in abstract as core methodology
Verificationpartial
partial
Evidencepartial
Given a natural-language instruction, it performs hybrid retrieval and computes a minimal dependency-closed context, the smallest set of typed symbols required for solver-executable code, via dependency propagation over the graph.
Implicationpartial
Directly stated in abstract as key technical approach
Verificationpartial
partial
Evidencepartial
In the second case, it consistently produces compilable models that reach known optimal solutions, demonstrating robust cross-domain generalization; baselines fail entirely.
Implicationpartial
Explicitly stated in abstract with specific performance claim
Verificationpartial
partial

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Type-Aware Retrieval-Augmented Generation with Dependency Closure for Solver-Executable Industrial Optimization Modeling

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