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arxiv: 2602.07342 · v2 · submitted 2026-02-07 · 💻 cs.AI

Recognition: 2 theorem links

· Lean Theorem

SupChain-Bench: Benchmarking Large Language Models for Real-World Supply Chain Management

Shengyue Guan , Yihao Liu , Lang Cao
Authors on Pith no claims yet

Pith reviewed 2026-05-16 06:46 UTC · model grok-4.3

classification 💻 cs.AI
keywords supply chain managementlarge language modelsbenchmarktool callingautonomous procedureslong-horizon orchestrationexecution reliability
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The pith

An SOP-free framework lets LLMs autonomously synthesize executable procedures for supply chain tool use and delivers the strongest consistent performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SupChain-Bench, a benchmark for testing large language models on supply chain tasks that demand long sequences of tool-based decisions grounded in domain procedures. Experiments across models reveal large gaps in reliable execution over these multi-step workflows. The authors propose SupChain-ReAct, which generates its own procedures instead of depending on fixed standard operating procedures, and this approach produces the most reliable tool-calling results. A reader would care because supply chain operations involve repeated planning where even small errors compound into major losses, and more dependable LLM agents could support automation in logistics and inventory management.

Core claim

SupChain-Bench reveals substantial gaps in execution reliability for LLMs performing long-horizon, multi-step orchestration in supply chain management. The proposed SupChain-ReAct framework autonomously synthesizes executable procedures for tool use without relying on standard operating procedures and achieves the strongest and most consistent tool-calling performance.

What carries the argument

SupChain-ReAct, an SOP-free framework that autonomously synthesizes executable procedures for tool use

Load-bearing premise

The tasks and standard operating procedures in SupChain-Bench accurately represent real-world supply chain workflows.

What would settle it

If SupChain-ReAct loses its performance edge when tested on supply chain tasks drawn directly from actual company operations rather than the benchmark scenarios, the claim of superior results would be falsified.

Figures

Figures reproduced from arXiv: 2602.07342 by Lang Cao, Shengyue Guan, Yihao Liu.

Figure 1
Figure 1. Figure 1: Overall composition of SupChain-Bench. The figure shows the distribution of annotated sam￾ples across three major functional domains of supply chain management: Logistics Collaboration & Cross￾Border, Fulfillment & Warehouse Operations, and Fi￾nance, Planning & Customs. Each domain is further decomposed into its constituent sub-tasks, highlighting the relative proportions of different operational activi￾ti… view at source ↗
Figure 2
Figure 2. Figure 2: The dataset construction pipeline of SupChain-Bench follows a four-stage quality-assurance process. First, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of function-calling questions by [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Overlaid histograms compare the number of tool calls per task for four models (gpt5, gemini-2.5-pro, [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Question generation prompt (part 1 of 2). [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Question generation prompt (continued). 23 [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Adversarial Review and Refinement. Based on the following analysis suggestions, regenerate the improved data: Original prompt: <original_prompt> {REPLACE_ORIGINAL_PROMPT} </original_prompt> Initial generated result: <initial_result> {REPLACE_INITIAL_RESULT} </initial_result> Modification suggestions: <modification_suggestions> {REPLACE_MODIFICATION_SUGGESTIONS} </modification_suggestions> Note that the ret… view at source ↗
Figure 8
Figure 8. Figure 8: Final Synthesis Prompt. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗
read the original abstract

Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supply chain management. However, supply chain workflows require reliable long-horizon, multi-step orchestration grounded in domain-specific procedures, which remains challenging for current models. To systematically evaluate LLM performance in this setting, we introduce SupChain-Bench, a unified real-world benchmark that assesses both supply chain domain knowledge and long-horizon tool-based orchestration grounded in standard operating procedures (SOPs). Our experiments reveal substantial gaps in execution reliability across models. We further propose SupChain-ReAct, an SOP-free framework that autonomously synthesizes executable procedures for tool use, achieving the strongest and most consistent tool-calling performance. Our work establishes a principled benchmark for studying reliable long-horizon orchestration in real-world operational settings and highlights significant room for improvement in LLM-based supply chain agents.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces SupChain-Bench, a unified benchmark for evaluating LLMs on real-world supply chain tasks that require long-horizon, multi-step tool orchestration grounded in standard operating procedures (SOPs). It reports substantial performance gaps across models and proposes SupChain-ReAct, an SOP-free framework that autonomously synthesizes executable procedures for tool use, claiming this yields the strongest and most consistent tool-calling performance.

Significance. If the empirical results hold under rigorous evaluation, the benchmark would provide a valuable, domain-grounded testbed for assessing LLM reliability in operational settings where long-horizon planning is essential. SupChain-ReAct's procedure-synthesis approach could offer a useful template for reducing dependence on hand-crafted SOPs in agent design.

major comments (1)
  1. [Abstract and Experiments] Abstract and Experiments section: The central claim that SupChain-ReAct achieves the strongest and most consistent tool-calling performance is stated without any quantitative results, success rates, error bars, model specifications, number of trials, or evaluation protocol details. This absence prevents verification of the magnitude of reported gaps or the statistical robustness of the superiority claim.
minor comments (2)
  1. [Method] Clarify the exact criteria used to measure success on long-horizon tasks (e.g., full completion vs. partial credit) and whether any SOP leakage occurs during autonomous procedure synthesis.
  2. [Experiments] Add a table or figure summarizing per-model performance across task categories to make the consistency claim concrete and comparable.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and recommendation for major revision. We address the single major comment point-by-point below and will incorporate the suggested clarifications to improve verifiability of our results.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: The central claim that SupChain-ReAct achieves the strongest and most consistent tool-calling performance is stated without any quantitative results, success rates, error bars, model specifications, number of trials, or evaluation protocol details. This absence prevents verification of the magnitude of reported gaps or the statistical robustness of the superiority claim.

    Authors: We acknowledge the concern. While the abstract is intentionally concise, we agree that key quantitative support should be included to substantiate the claims. In the revised manuscript we will update the abstract to report specific success rates for SupChain-ReAct versus baselines, list the evaluated models, and note the evaluation protocol. In the Experiments section we will add error bars, explicitly state the number of trials per condition, and provide a detailed description of the evaluation protocol (including how SOP grounding and long-horizon success are measured) so that the magnitude of gaps and statistical robustness of the superiority claim can be directly verified. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper presents an empirical benchmark (SupChain-Bench) and an SOP-free framework (SupChain-ReAct) evaluated through direct performance comparisons on tool-calling tasks. No equations, derivations, parameter fits, or self-citation chains appear in the abstract or described structure. All claims reduce to experimental results rather than any self-referential definition or imported uniqueness theorem, making the argument self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark paper with no mathematical derivations, fitted parameters, or postulated entities described in the abstract.

pith-pipeline@v0.9.0 · 5450 in / 1113 out tokens · 41879 ms · 2026-05-16T06:46:48.786277+00:00 · methodology

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matches
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supports
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Reference graph

Works this paper leans on

21 extracted references · 21 canonical work pages · 1 internal anchor

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    Applied Sciences, 15(14):8001

    Smart routing for sustainable supply chain net- works: An ai and knowledge graph driven approach. Applied Sciences, 15(14):8001. Elizabeth Fons, Rachneet Kaur, Soham Palande, Zhen Zeng, Tucker Balch, Manuela Veloso, and Svitlana Vyetrenko. 2024. Evaluating large language mod- els on time series feature understanding: A com- prehensive taxonomy and benchma...

    work page arXiv 2024

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    Large Language Model Agent: A Survey on Methodology, Applications and Challenges

    Large language model agent: A survey on methodology, applications and challenges.Preprint, arXiv:2503.21460. Mahdi Mostajabdaveh, Timothy T. Yu, Samarendra Chandan Bindu Dash, Rindranirina Ramamonjison, Jabo Serge Byusa, Giuseppe Carenini, Zirui Zhou, and Yong Zhang. 2025. Evaluating llm reasoning in the operations research domain with orqa.Preprint, arXi...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  3. [3]

    Identifier Extraction:The system first parses the user’s query to extract the primary trade_order_id

    work page

  4. [4]

    An order may be split into multiple fulfillments

    Order Information Retrieval:Using the trade_order_id, the system queries our database to retrieve all associated order iden- tifiers, including the fulfillment_id(s) and warehouse_order_id. An order may be split into multiple fulfillments

    work page

  5. [5]

    Status Check:For each fulfillment_id iden- tified in the previous step, the system queries its real-time status

    work page

  6. [6]

    customer initiated,

    Conditional Analysis:The subsequent actions are contingent upon the status returned: • If the status is cancelled, the system pro- ceeds to invoke two diagnostic tools: one to re- trieve the cancellation reason (e.g., "customer initiated," "out of stock") and another to get the specific cancellation error code. An op- tional tool may also be triggered to ...

    work page

  7. [7]

    Initial Generation:We prompted a capable model (o3-mini) to generate an initial question- answer pair based on the provided documents, prompt showed in Figure 5

    work page

  8. [8]

    Adversarial Review and Refinement:A sec- ond, advanced model (Claude 4 Sonnet) was tasked to act as a critic. It analyzed the gener- ated QA pair and provided structured feedback for improvement, focusing on enhancing clarity, 13 removing ambiguity, and increasing the cogni- tive complexity of the question, prompt showed in Figure 7

    work page

  9. [9]

    canceled

    Final Synthesis:A third, powerful model (Gemini 2.5 Pro ) then synthesized the final, polished question by integrating the feedback from the critic model, prompt showed in Figure 8. This multi-model approach proved highly effec- tive, as the resulting questions were consistently rated by human reviewers as having greater nuance and quality compared to tho...

    work page 2025

  10. [10]

    question

    "question": The stem, a string that contains the specific description of the question. The stem should raise a clear multiple-select question around the key concept (e.g., “Which of the following are...?”). The stem should be clearly expressed and concise, and should not include phrases like “according to the article.” Ask directly. Avoid overly absolute ...

    work page

  11. [11]

    options": Options, an array providing 4 options (A - D), with 1-4 correct answers and the rest being reasonable distractors. Each option is a dictionary with two fields,

    "options": Options, an array providing 4 options (A - D), with 1-4 correct answers and the rest being reasonable distractors. Each option is a dictionary with two fields, "key" and "text". The "key" field is the identifier for the option, and the "text" field is the textual description of the option. For example, {"key":"A","text":"Option A"}. ,→ ,→ ,→

    work page

  12. [12]

    answer": The answer, an array containing the strings of the correct answers, i.e., multiple of the 4 options (A - D). For example:

    "answer": The answer, an array containing the strings of the correct answers, i.e., multiple of the 4 options (A - D). For example: "answer": ["A","B"].,→

    work page

  13. [13]

    explanation

    "explanation": Explanation, a string providing a detailed explanation for the correct answers, explaining why the option is correct. The explanation should state the reasons for the correct options and common misunderstandings for the incorrect options. ,→ ,→ ## Classification and metadata

    work page

  14. [14]

    It can only be one of the following:,→

    "field": A string that defines the macro field or theme to which the question belongs. It can only be one of the following:,→

    work page

  15. [15]

    Fulfillment, Expression, Procurement, Returns and Waste, In-warehouse, Inventory, Merchandise, Merchant, Network, Planning, Finance and Operations, Permissions & Accounts, Logistics Collaboration, Other ,→ ,→

    work page

  16. [16]

    sub_field

    "sub_field": A string that provides a more detailed division of the main category

    work page

  17. [17]

    question_type

    "question_type": A string that explicitly indicates the form of the question as "multiple_choices".,→

    work page

  18. [18]

    "tags": An array of strings that provides a series of keywords related to the content of the question for searching and filtering.,→ ## Difficulty definition High-difficulty questions usually have one or more of the following characteristics:

    work page

  19. [19]

    Applying: Applying learned knowledge to new scenarios or solving practical problems

    work page

  20. [20]

    The question usually contains irrelevant information that needs to be filtered.,→

    Analyzing: Breaking information into different parts and exploring their relationships and structures. The question usually contains irrelevant information that needs to be filtered.,→

    work page

  21. [21]

    question

    Synthesizing: Answering requires a full understanding of the text and some reasoning ability to arrive at the answer.,→ Figure 5: Question generation prompt (part 1 of 2). 22 ## Output format validation All fields must be wrapped in double quotes, the JSON structure must be complete (e.g., brackets closed), and the output must be directly parseable as a J...

    work page