ARXIV:2602.19439 · SUPPLY CHAIN OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

arXiv

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models. Supply chain optimization models frequently become infeasible because of modeling errors.

METHOD

Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors.

Full abstract

Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors. Diagnosis and repair require scarce OR expertise: analysts must interpret solver diagnostics, trace root causes across echelons, and fix formulations without sacrificing operational soundness. Whether AI agents can perform this task remains untested. Methodology/Results. OptiRepair splits this task into a domain-agnostic feasibility phase (iterative IIS-guided repair of any LP) and a domain-specific validation phase (five rationality checks grounded in inventory theory). We test 22 API models from 7 families on 976 multi-echelon supply chain problems and train two 8B-parameter models using self-taught reasoning with solver-verified rewards. The trained models reach 81.7% Rational Recovery Rate (RRR) -- the fraction of problems resolved to both feasibility and operational rationality -- versus 42.2% for the best API model and 21.3% on average. The gap concentrates in Phase 1 repair: API models average 27.6% recovery rate versus 97.2% for trained models. Managerial Implications. Two gaps separate current AI from reliable model repair: solver interaction (API models restore only 27.6% of infeasible formulations) and operational rationale (roughly one in four feasible repairs violate supply chain theory). Each requires a different intervention: solver interaction responds to targeted training; operational rationale requires explicit specification as solver-verifiable checks. For organizations adopting AI in operational planning, formalizing what "rational" means in their context is the higher-return investment.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Methodology/Results.

WHY NOW

Supply Chain Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainAI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.19439 · SUPPLY CHAIN OPTIMIZATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

arXiv

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Blocked on Code›Score3.0Evidence unverified

Opportunity summary

Pain AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors.

Full abstract

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Methodology/Results.

WHY NOW

Supply Chain Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score3.0

PainAI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.19439

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback39 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

3.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors.

RESULT

ScienceToStartup currently rates this 3.0/10 on the public viability pass. Methodology/Results.

WHY NOW

Supply Chain Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models. Supply chain optimization models frequently become infeasible because of modeling errors.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Methodology/Results.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Supply Chain Optimization moved forward this cycle; last verified April 2026. Public score 3.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Problem Definition. Supply chain optimization models frequently become infeasible because of modeling errors.

Materials

PDF linked

Markets

Supply Chain Optimization

Competitors

not indexed

Competitive landscape

AI-driven tool for diagnosing and repairing infeasible supply chain optimization models using large language models.

Segment

Supply Chain Optimization

Adoption evidence

No public code link in the paper record yet

Commercial read

3.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.19439 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(39)

AI Agents for Inventory Control: Human-LLM-OR Complementarity

2026J. Baek, Yao Fu et al.

Best Arm Identification with LLM Judges and Limited Human

2026Ruicheng Ao, Hongyu Chen et al.

PPI-SVRG: Unifying Prediction-Powered Inference and Variance Reduction for Semi-Supervised Optimization

2026Ruicheng Ao, Hongyu Chen et al.

Solver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research

2026Ruicheng Ao, David Simchi-Levi et al.

Large-Scale Optimization Model Auto-Formulation: Harnessing LLM Flexibility via Structured Workflow

2026Kuo Liang, Yuhang Lu et al.

Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs

2025Guiyao Tie, Zenghui Yuan et al.

Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management

2025Runze Zhang, Xiaowei Zhang et al.

AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager

2025Xuhua Zhao, Yuxuan Xie et al.

Large Language Models for Supply Chain Decisions

2025David Simchi-Levi, Konstantina Mellou et al.

Auto-Formulating Dynamic Programming Problems with Large Language Models

2025Chenyu Zhou, Jingyuan Yang et al.

Self-Correction Bench: Uncovering and Addressing the Self-Correction Blind Spot in Large Language Models

2025Ken Tsui

Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards

2025Wenlong Ji, Yihan Pan et al.

Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints

2025Ruicheng Ao, Gan Luo et al.

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

2025Adam Suma, Samuel Dauncey

TÜLU 3: Pushing Frontiers in Open Language Model Post-Training

2024Nathan Lambert, Jacob Daniel Morrison et al.

OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling

2024Zhicheng YANG, Yinya Huang et al.

ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling

2024Chenyu Huang, Zhengyang Tang et al.

OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models

2024Ali AhmadiTeshnizi, Wenzhi Gao et al.

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

2024Zhihong Shao, Peiyi Wang et al.

Chain-of-Experts: When LLMs Meet Complex Operations Research Problems

2024Ziyang Xiao, Dongxiang Zhang et al.

Showing 20 of 39 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkEngiAgent: Fully Connected Coordination of LLM Agents for Solving Open-ended Engineering Problems with Feasible Solutions

3.0

Prior WorkFrom Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling

3.0

Extension

Builds On ThisLarge Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

2.0

Commercially relevant

Higher ViabilitySolver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research

9.0

Higher ViabilityAI Agents for Inventory Control: Human-LLM-OR Complementarity

5.0

Higher ViabilityORPilot: A Production-Oriented Agentic LLM-for-OR Tool for Optimization Modeling

8.0

Higher ViabilityWhen Agents Fail to Act: A Diagnostic Framework for Tool Invocation Reliability in Multi-Agent LLM Systems

6.0

Higher ViabilityAgentRx: Diagnosing AI Agent Failures from Execution Trajectories

6.0

Higher ViabilityFrom AI Assistant to AI Scientist: Autonomous Discovery of LLM-RL Algorithms with LLM Agents

7.0

Higher ViabilityPHMForge: A Scenario-Driven Agentic Benchmark for Industrial Asset Lifecycle Maintenance

7.0

Conflicting

none indexed

Related Resources

What are the applications of causal discovery in supply chain optimization?(question)

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.19439
Route: /paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents
Active tab: read
Artifact: optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents/paper-pack
REST build passport API/api/v1/paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

stale

Proof freshness: stale
Proof status: unverified
Display score: 3/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

Canonical ID optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents | Route /paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

MCP example

{
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}

source_context

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  "benchmark_ref": null,
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}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Not build-ready: OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

/buildability/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

Ignoreblocked

Subject: OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

Paper ref

optirepair-closed-loop-diagnosis-and-repair-of-supply-chain-optimization-models-with-llm-agents

arXiv id

2602.19439

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

1310ae2ad370fc95e74da980fa83119983ba8278dd195002e23ac8c76fd98994

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

OptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

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Claim map

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References(39)

Related Papers

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Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline