ARXIV:2602.12631 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

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

arXiv

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when demand…

METHOD

Full abstract

Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when demand distributions shift or relevant contextual information is unavailable. Recent advances in large language models (LLMs) have generated interest in AI agents that can reason flexibly and incorporate rich contextual signals, but it remains unclear how best to incorporate LLM-based methods into traditional decision-making pipelines. We study how OR algorithms, LLMs, and humans can interact and complement each other in a multi-period inventory control setting. We construct InventoryBench, a benchmark of over 1,000 inventory instances spanning both synthetic and real-world demand data, designed to stress-test decision rules under demand shifts, seasonality, and uncertain lead times. Through this benchmark, we find that OR-augmented LLM methods outperform either method in isolation, suggesting that these methods are complementary rather than substitutes. We further investigate the role of humans through a controlled classroom experiment that embeds LLM recommendations into a human-in-the-loop decision pipeline. Contrary to prior findings that human-AI collaboration can degrade performance, we show that, on average, human-AI teams achieve higher profits than either humans or AI agents operating alone. Beyond this population-level finding, we formalize an individual-level complementarity effect and derive a distribution-free lower bound on the fraction of individuals who benefit from AI collaboration; empirically, we find this fraction to be substantial.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Contrary to prior findings that human-AI collaboration can degrade performance, we show that, on average, human-AI teams achieve higher profits than either humans or…

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

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

ARXIV:2602.12631 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

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

arXiv

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

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

Opportunity summary

Score5.0

PainDevelop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

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

Paper Pack

10.48550/arXiv.2602.12631

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

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

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 fallback43 indexednot indexed

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

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when demand distributions shift or relevant contextual information is unavailable.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Inventory control is a fundamental operations problem in which ordering decisions are traditionally guided by theoretically grounded operations research (OR) algorithms. However, such algorithms often rely on rigid modeling assumptions and can perform poorly when demand distributions shift or relevant contextual information is unavailable.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Contrary to prior findings that human-AI collaboration can degrade performance, we show that, on average, human-AI teams achieve higher profits than either humans or AI agents operating alone.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Agents moved forward this cycle; last verified April 2026. Public score 5.0/10.
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

Materials

PDF linked

Markets

Agents

Competitors

not indexed

Competitive landscape

Develop a hybrid human-AI decision-making platform for inventory control that enhances operational efficiency by integrating LLMs and OR algorithms.

Segment

Agents

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.12631 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(43)

Evaluating LLM-persona Generated Distributions for Decision-making

2026Jackie Baek, Yunhan Chen et al.

Ask, Clarify, Optimize: Human-LLM Agent Collaboration for Smarter Inventory Control

2025Yaqi Duan, Yichun Hu et al.

Assured Autonomy: How Operations Research Powers and Orchestrates Generative AI Systems

2025Tinglong Dai, David Simchi-Levi et al.

Large Language Newsvendor: Decision Biases and Cognitive Mechanisms

2025Jifei Liu, Zhi Chen et al.

Auto-Formulating Dynamic Programming Problems with Large Language Models

2025Chenyu Zhou, Jingyuan Yang et al.

Performance of LLMS on Stochastic Modeling Operations Research Problems: From Theory to Practice

2025Akshit Kumar, Tianyi Peng et al.

Algorithm Reliance: Fast and Slow

2025Clare Snyder, Samantha Keppler et al.

Human-Algorithm Collaboration with Private Information: Naïve Advice-Weighting Behavior and Mitigation

2025Maya Balakrishnan, Kris Johnson Ferreira et al.

Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents

2025Axel Backlund, Lukas Petersson

EconEvals: Benchmarks and Litmus Tests for LLM Agents in Unknown Environments

2025Sara Fish, Julia Shephard et al.

A No Free Lunch Theorem for Human-AI Collaboration

2024Kenny Peng, Nikhil Garg et al.

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

2024Chenyu Huang, Zhengyang Tang et al.

When combinations of humans and AI are useful: A systematic review and meta-analysis

2024Michelle Vaccaro, Abdullah Almaatouq et al.

Designing Algorithmic Recommendations to Achieve Human-AI Complementarity

2024Bryce Mclaughlin, Jann Spiess

Complementarity in human-AI collaboration: concept, sources, and evidence

2024Patrick Hemmer, Max Schemmer et al.

UCB-Type Learning Algorithms with Kaplan-Meier Estimator for Lost-Sales Inventory Models with Lead Times

2024Chengyi Lyu, Huanan Zhang et al.

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

2024Ali AhmadiTeshnizi, Wenzhi Gao et al.

A Decision Theoretic Framework for Measuring AI Reliance

2024Ziyang Guo, Yifan Wu et al.

Large Language Models for Supply Chain Optimization

2023Beibin Li, Konstantina Mellou et al.

Towards a Science of Human-AI Decision Making: An Overview of Design Space in Empirical Human-Subject Studies

2023Vivian Lai, Chacha Chen et al.

Showing 20 of 43 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 WorkLanguage Models Exhibit Inconsistent Biases Towards Algorithmic Agents and Human Experts

5.0

Extension

Builds On ThisOptiRepair: Closed-Loop Diagnosis and Repair of Supply Chain Optimization Models with LLM Agents

3.0

Builds On ThisAlign When They Want, Complement When They Need! Human-Centered Ensembles for Adaptive Human-AI Collaboration

3.0

Builds On ThisToward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks

3.0

Commercially relevant

Higher ViabilityAgentCollab: A Self-Evaluation-Driven Collaboration Paradigm for Efficient LLM Agents

7.0

Higher ViabilityDesign and Evaluation of Multi-Agent AI Oracle Systems for Prediction Market Resolution

6.0

Conflicting

Competing ApproachAI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval

5.0

Competing ApproachRequesting Expert Reasoning: Augmenting LLM Agents with Learned Collaborative Intervention

5.0

Competing ApproachRetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments

4.0

Competing ApproachPosition: agentic AI orchestration should be Bayes-consistent

2.0

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Owned Distribution

Subscribe to the weekly brief

Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2602.12631
Route: /paper/ai-agents-for-inventory-control-human-llm-or-complementarity
Active tab: read
Artifact: ai-agents-for-inventory-control-human-llm-or-complementarity

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/ai-agents-for-inventory-control-human-llm-or-complementarity/paper-pack
REST build passport API/api/v1/paper/ai-agents-for-inventory-control-human-llm-or-complementarity/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

{
  "contract_version": "paper-r2",
  "paper_id": "aafd1d6e-60cd-4a43-8864-898c032a93cc",
  "arxiv_id": "2602.12631",
  "canonical_route": "/paper/ai-agents-for-inventory-control-human-llm-or-complementarity",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "ai-agents-for-inventory-control-human-llm-or-complementarity",
  "endpoints": {
    "paper_pack": "/api/v1/paper/ai-agents-for-inventory-control-human-llm-or-complementarity/paper-pack",
    "build_passport": "/api/v1/paper/ai-agents-for-inventory-control-human-llm-or-complementarity/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/ai-agents-for-inventory-control-human-llm-or-complementarity

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
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

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

Canonical ID ai-agents-for-inventory-control-human-llm-or-complementarity | Route /paper/ai-agents-for-inventory-control-human-llm-or-complementarity

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/ai-agents-for-inventory-control-human-llm-or-complementarity

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2602.12631"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "AI Agents for Inventory Control: Human-LLM-OR Complementarity",
  "normalized_query": "2602.12631",
  "route": "/paper/ai-agents-for-inventory-control-human-llm-or-complementarity",
  "paper_ref": "ai-agents-for-inventory-control-human-llm-or-complementarity",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: AI Agents for Inventory Control: Human-LLM-OR Complementarity

/buildability/ai-agents-for-inventory-control-human-llm-or-complementarity

Watchwatch

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

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

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/ai-agents-for-inventory-control-human-llm-or-complementarity

Paper ref

ai-agents-for-inventory-control-human-llm-or-complementarity

arXiv id

2602.12631

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

280fa2856988da7baf010d0d159410a88ca260226518c475cc5a50d611c59129

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

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

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References(43)

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Brief

Experiment plan

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Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(43)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline