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arxiv: 2605.02503 · v2 · pith:E5K7KBOUnew · submitted 2026-05-04 · 💻 cs.AI

DataClawBench: An Agent Benchmark for Exploratory Real-World Financial Data Analysis

Qiaohong Zhang , Weihao Ye , Jialong Chen , Yi Luo , BoYuan Li , Bowen Deng , Zibin Zheng , Jianhao Lin
show 2 more authors
Wei-Shi Zheng Chuan Chen
This is my paper

Pith reviewed 2026-05-21 00:21 UTC · model grok-4.3

classification 💻 cs.AI
keywords LLM agentsexploratory data analysisfinancial analyticsagent benchmarksdata explorationagent reliabilitynoisy data
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The pith

Exploratory financial data analysis breaks LLM agent reliability because more exploration does not produce reliable progress or correct answers.

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

The paper introduces DataClawBench to test autonomous agents on real-world financial data analysis where relevant evidence is not pre-specified and data contains native noise. It supplies roughly 2.06 million records across enterprise, industry, and policy domains together with 492 cross-domain tasks drawn from think-tank consulting scenarios. Each task carries intermediate milestones that let evaluators distinguish failures in exploration from failures in reasoning. When eight advanced LLMs operate under the OpenClaw agent on these tasks, the evaluation shows that greater exploration volume fails to translate into task-relevant progress or higher rates of correct final answers.

Core claim

DataClawBench supplies a large collection of underexplored, noisy financial records and 492 tasks that require agents to discover relevant evidence without prior guidance on schemas or sources. Systematic testing of eight LLMs reveals that exploratory data analysis breaks agent reliability: increased exploration does not reliably produce task-relevant progress or correct final answers.

What carries the argument

DataClawBench benchmark, which preserves native data noise across 2.06 million records and annotates each of the 492 tasks with intermediate milestones that diagnose exploration and reasoning failures separately from final accuracy.

If this is right

  • Existing agent benchmarks that supply cleaned data or pre-selected sources understate the difficulty agents encounter in genuinely underexplored financial environments.
  • Agent designs must incorporate mechanisms that convert exploratory steps into task-relevant progress rather than simply increasing the volume of data queries.
  • Diagnostic milestones allow developers to isolate whether failures occur during evidence discovery or during later reasoning.
  • Reliability improvements will require agents to prioritize relevance over exhaustive search when data noise and domain breadth are high.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar reliability breakdowns are likely in other high-stakes domains that involve noisy, cross-domain records without pre-specified schemas.
  • Future agent training could use the milestone annotations to create targeted rewards that penalize irrelevant exploration.
  • The benchmark could be extended by measuring how quickly agents learn to reduce unproductive exploration across repeated tasks.

Load-bearing premise

The 492 tasks drawn from think-tank consulting scenarios plus the preserved native noise in the data accurately reflect the exploratory demands that agents face in complex real-world financial analytics when given limited prior guidance.

What would settle it

An agent that performs substantially more exploration on the same tasks yet achieves markedly higher milestone completion rates and final-answer accuracy would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.02503 by Bowen Deng, BoYuan Li, Chuan Chen, Jialong Chen, Jianhao Lin, Qiaohong Zhang, Weihao Ye, Wei-Shi Zheng, Yi Luo, Zibin Zheng.

Figure 1
Figure 1. Figure 1: Overall framework of DataClaw. Top. Data annotation pipeline. Bottom. Evaluation pipeline. Each agent runs in an isolated Docker container, locates relevant information in an underexplored data environment, performs numerical computation and text comprehension, and produces a final answer, which is then assessed by both outcome evaluation and process evaluation. Claw, comprising the data annotation pipelin… view at source ↗
Figure 2
Figure 2. Figure 2: Accuracy by task category across all models. view at source ↗
Figure 2
Figure 2. Figure 2: Three diagnostic views of agent behaviour on DataClawBench. (c) The eight models partition into four [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qwen3.5-plus accuracy under progressively view at source ↗
Figure 3
Figure 3. Figure 3: Position mk of the first un-achieved milestone, shown separately for Easy, Medium, and Hard tasks. ment is a common failure mode, but its severity depends on model strength. Strong agents can of￾ten move beyond the initial evidence-acquisition stage before failing. Most agents, however, lose the analytical thread almost immediately, while they are still finding evidence, framing the problem, or setting up … view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of Claude Opus 4.6 failures by the position view at source ↗
Figure 4
Figure 4. Figure 4: Accuracy by task category across all models. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: GLM-5 accuracy under progressively cleaned data environments. view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy across data analysis benchmarks. view at source ↗
Figure 6
Figure 6. Figure 6: Accuracy across data analysis benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
read the original abstract

Autonomous data analysis agents are increasingly expected to conduct exploratory analysis over underexplored data environments. This burden is especially salient in complex financial analytics, where relevant evidence is rarely pre-specified. However, existing benchmarks typically evaluate such agents in prior-guided settings, providing selected data sources, explicit data schemas, or cleaned data, thereby understating the exploratory burden. We introduce DataClawBench, a benchmark for exploratory real-world financial data analysis under limited prior guidance. DataClawBench contains approximately 2.06 million real-world records across enterprise, industry, and policy domains, with native data noise preserved. It further includes 492 cross-domain tasks derived from think-tank consulting scenarios, each annotated with intermediate milestones that diagnose exploration and reasoning failures beyond outcome accuracy. A systematic evaluation of eight advanced LLMs under the OpenClaw agent reveals that exploratory data analysis breaks agent reliability: more exploration does not reliably translate into task-relevant progress or correct final answers.

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

2 major / 1 minor

Summary. The paper introduces DataClawBench, a benchmark for exploratory real-world financial data analysis under limited prior guidance. It comprises approximately 2.06 million real-world records across enterprise, industry, and policy domains with native noise preserved, along with 492 cross-domain tasks derived from think-tank consulting scenarios, each annotated with intermediate milestones. A systematic evaluation of eight advanced LLMs using the OpenClaw agent finds that exploratory data analysis breaks agent reliability, as more exploration does not reliably translate into task-relevant progress or correct final answers.

Significance. If the central empirical finding holds, the benchmark offers a useful resource for the field by emphasizing real noisy data and diagnostic milestones over prior-guided settings, which could help identify specific failure modes in agent-based data analysis. The scale of the data and the focus on underexplored environments represent a concrete advance for evaluating robustness in financial analytics agents.

major comments (2)
  1. [Evaluation] The evaluation of the eight LLMs reports that increased exploration fails to improve reliability, but the manuscript provides no details on the measurement of exploration, statistical tests for significance, error bars, or controls for confounding factors such as task difficulty or domain variation; this leaves the central claim only partially supported.
  2. [Benchmark Construction] The construction of the 492 tasks from think-tank scenarios and the annotation of milestones is described at a high level but lacks specifics on the derivation process, inter-annotator agreement, or validation against real-world exploratory burdens, which is load-bearing for claims about representativeness.
minor comments (1)
  1. [Abstract] The abstract states the key finding but could include a brief mention of the number of tasks and records to improve immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and outline the specific revisions we will make to improve the manuscript's clarity and empirical rigor.

read point-by-point responses

  1. Referee: [Evaluation] The evaluation of the eight LLMs reports that increased exploration fails to improve reliability, but the manuscript provides no details on the measurement of exploration, statistical tests for significance, error bars, or controls for confounding factors such as task difficulty or domain variation; this leaves the central claim only partially supported.

    Authors: We agree that the current version provides insufficient detail on these aspects, which weakens support for the central claim. In the revised manuscript we will add a dedicated subsection in the Evaluation section that defines exploration quantitatively (via agent steps, tool invocations, and milestone coverage). We will report error bars from multiple runs, include statistical significance tests (paired t-tests and regression models), and present stratified analyses controlling for task difficulty and domain. These additions will be incorporated in the next version. revision: yes

  2. Referee: [Benchmark Construction] The construction of the 492 tasks from think-tank scenarios and the annotation of milestones is described at a high level but lacks specifics on the derivation process, inter-annotator agreement, or validation against real-world exploratory burdens, which is load-bearing for claims about representativeness.

    Authors: We concur that greater specificity is needed here to substantiate representativeness. The revision will expand the Benchmark Construction section with a step-by-step account of task derivation from the think-tank scenarios, report inter-annotator agreement metrics (e.g., Cohen's kappa) for milestone annotations, and describe validation procedures including expert review and alignment checks against real-world financial analysis workloads. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new benchmark (DataClawBench) consisting of real-world financial records and 492 tasks derived from consulting scenarios, then reports independent empirical results from running eight LLMs under the OpenClaw agent. No equations, fitted parameters, or first-principles derivations are present; the central claim that increased exploration does not reliably improve reliability is an observation drawn directly from the new evaluation rather than reducing to any prior input by construction. The benchmark construction and milestone annotations supply the testbed but do not logically entail the reported failure modes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that the constructed tasks and preserved data noise faithfully capture real exploratory financial analysis burdens; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption The 492 tasks derived from think-tank consulting scenarios accurately reflect exploratory burdens in underexplored financial data environments.
    This premise underpins the claim that the benchmark reveals a genuine limitation in current agents.

pith-pipeline@v0.9.0 · 5725 in / 1276 out tokens · 48563 ms · 2026-05-21T00:21:48.264268+00:00 · methodology

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Reference graph

Works this paper leans on

82 extracted references · 82 canonical work pages · 1 internal anchor

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    ForEasydifficulty, we use a pipeline combining domain knowledge graph construction, templated generation, and automated rule verification. After technical team review, 131 valid questions are retained. 21

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    After the technical staff deliver the annotation guidelines, the university team performs the annotation

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    Only samples on which all agents reach unanimous agreement pass validation

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    You may use files under./database/ and web search

    Infinal verification, the technical team conducts item-by-item review of AI-validated results against the annotation specifications, excluding samples with logical discontinuities, missing evidence, or deviations from business scenarios. The procedure yields 286 valid medium-difficulty and 75 valid hard-difficulty QA pairs. A.8.5 Dataset Composition and C...

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    A number appearing in an unrelated context does NOT count

    Direct evidence: a milestone is achieved if the trajectory clearly shows 23 the agent computed or obtained the expected value (or within 1% relative error) in the CORRECT semantic context. A number appearing in an unrelated context does NOT count

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    If a downstream milestone is correctly achieved, its upstream dependencies can be inferred as achieved

    Temporal coupling inference: milestones follow a logical dependency chain. If a downstream milestone is correctly achieved, its upstream dependencies can be inferred as achieved

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    break point

    Chain-break identification: if the final answer is INCORRECT, identify the earliest milestone in the logical chain that was NOT achieved — this is the "break point" where the agent's reasoning diverged

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    milestones

    Different-but-valid paths: judge milestone achievement based on whether the agent obtained the correct intermediate values, regardless of method. ## Output Format Respond with ONLY a JSON object. No markdown fences, no extra text. {"milestones": [{"key": "...", "achieved": true, "evidence_type": "direct", "first_step": 4, "reason": "..."}, ...], "break_po...

    work page 2022

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    Extract ZEEKR 2022 net revenues (RMB 31,899,448 thousand) and total assets (RMB 19,477,316 thousand) from the ZEEKR F-1 prospectus; compute asset turnover = 1.64

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    From company_profile.csv, filter enterprises with industry = Automobile manufacturing , yielding 230 enterprises

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    From company_operation_status.csv, obtain revenue and total assets for the 230 enterprises; compute per- company asset turnover; compute industry median = 0.59

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    Gold answer.[1.64, 1.05]

    ZEEKR minus industry median= 1.64−0.59 = 1.05. Gold answer.[1.64, 1.05]. Claude Opus 4.6 trajectory

    work page

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    I’ll start by checking what’s available in the database and searching for the relevant financial data,

    Opens with “I’ll start by checking what’s available in the database and searching for the relevant financial data,” and fires a directory listing plus a web search for ZEEKR in parallel

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  18. [18]

    Runshead -5against six CSV schemas simultaneously, an over-broad schema probe

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    The database uses anonymized company names

    Greps for ZEEKR name variants across three profile files. All empty. Observes: “The database uses anonymized company names.” 24

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  20. [20]

    The keyword for automobile manufacturing yields 242 matches

    Pivots to an industry-based search. The keyword for automobile manufacturing yields 242 matches. Also scans the overseas-listing profile files in case ZEEKR was listed abroad

    work page

  21. [21]

    Finds only a supplier record that mentions ZEEKR as a customer, with no financials

    Tries the parent company Geely. Finds only a supplier record that mentions ZEEKR as a customer, with no financials

    work page

  22. [22]

    SEC returns 403

    Fires a web search for the ZEEKR F-1 prospectus. SEC returns 403. First failed external fetch

    work page

  23. [23]

    In parallel, pulls automobile-industry medians fromnational_industry_status_detail.csv, obtaining median total assets and median operating revenue

    work page

  24. [24]

    Recognizes that median(revenue)/median(assets) is not equivalent tomedian(revenue/assets) and commits to the per-company computation

    work page

  25. [25]

    First attempt returns an absurd median of 51,601.32 from a unit mismatch

    Runs a Python script over all 242 automobile-industry IDs to compute per-company ratios. First attempt returns an absurd median of 51,601.32 from a unit mismatch

    work page

  26. [26]

    The units are NOT consistent – different companies use different units

    Diagnoses the bug: “The units are NOT consistent – different companies use different units.” Values appear in yuan, 10K yuan, 100K yuan, 1M yuan, and 10M yuan

    work page

  27. [27]

    Rewrites the script with a unit-conversion table, recomputes, and obtains industry median asset turnover = 0.59 across 230 companies

    work page

  28. [28]

    Two more web fetches fail or return garbled data

    Resumes the external hunt for ZEEKR’s financials across additional sites. Two more web fetches fail or return garbled data

    work page

  29. [29]

    Total assets = RMB 19,477 M, revenue = RMB 31,899 M

    Finally retrieves clean figures from a third-party analytics site. Total assets = RMB 19,477 M, revenue = RMB 31,899 M

    work page

  30. [30]

    Computes31,899/19,477 = 1.6378→1.64

    work page

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    Both are redundant

    Runs a third Python verification that recomputes the industry median from scratch, then a fourth one-line recomputa- tion of the final difference. Both are redundant

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  32. [32]

    Inefficiency pattern.Each individual step is defensible, yet the compound trajectory exhibits five distinct sources of slack

    Outputs[1.64, 1.05]. Inefficiency pattern.Each individual step is defensible, yet the compound trajectory exhibits five distinct sources of slack. First, schema probing before narrowing to the relevant tables. Second, double verification of ZEEKR’s absence in the local database via multiple grep variants. Third, a unit-handling bug loop requiring two Pyth...

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    From regional_industry_status.csv filtered to pharmaceutical manufacturing, obtain the national enterprise total = 449

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  34. [34]

    From policy_release_status.csv filtered to pharmaceutical manufacturing, obtain the national pharmaceutical- related policy count = 80

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  35. [35]

    From regional_industry_status.csv, identify 16 provinces with complete pharmaceutical-manufacturing cover- age. 25

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  36. [36]

    Compute Shanghai industry agglomeration = 0.1203

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  37. [37]

    Compute Shanghai R&D intensity = 0.2548

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  38. [38]

    Compute Shanghai policy support = 0.1375

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    Compute Shanghai talent density = 0.162

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    Gold answer.0.92

    Compute Shanghai composite score after min-max normalization = 0.916. Gold answer.0.92. Claude Opus 4.6 trajectory

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    Lists the database directory and probes the schemas of seven candidate CSVs in parallel, isolating regional_industry_status.csvandpolicy_release_status.csvas the relevant aggregate sources

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  42. [42]

    Reaches 449 nationwide across 16 valid provinces, achieving M1 and M3

    Filters regional_industry_status.csv to pharmaceutical manufacturing and aggregates per-province enterprise totals. Reaches 449 nationwide across 16 valid provinces, achieving M1 and M3

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  43. [43]

    relevant policies

    Break point.Reads policy_release_status.csv and finds a row withtargetName = total published policy count, value = 602 and an emptyindustryId. Adopts 602 as the national denominator for “relevant policies” without filtering onindustry = pharmaceutical manufacturing. The correct denominator, 80, sits in the same file underindustryId = 1but is never queried

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    For each province, divides the provincial pharmaceutical policy count by 602 to compute the policy intensity ratio, locking in a denominator that is 7.5 times too large

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    Spends the next several requests debating the R&D indicator. Catches an outlier-driven mean of 19960% in one province, oscillates between mean and median, briefly attempts company-level aggregation across company_operation_status.csv, then returns to the regional pre-aggregated mean R&D ratio. M5 and M7 both achieved with correct provincial values

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    Recomputes the composite score with min-max normalization across provinces. Because the policy denominator is inflated, every province’s policy contribution is uniformly compressed; provinces strong on policy support such as Shanghai lose ground and provinces strong on the other three axes such as Jiangsu move to the top

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    Incorrect

    Outputs 0.80 for Jiangsu, cross-checks with a mainland-only re-run that returns the same value, and confirms the answer. Incorrect. Break-point analysis.The failure is a single missed filter at M2 in the Policy Lookup and Count subtask category. Its structural cost is disproportionate to its locality. The flawed national denominator propagates linearly in...

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    = Real Estate

    Fromcompany_profile.csv, obtain the industry of Zhao Ye Hua Chang Real Estate Development Co., Ltd. = Real Estate

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  49. [49]

    Fromnational_industry_status.csv, obtain the Real Estate median operating profit = 130,368,786 yuan

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  50. [50]

    = Financial Industry

    Fromcompany_profile.csv, obtain the industry of Tong Tong Ze Hong Securities Co., Ltd. = Financial Industry

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  51. [51]

    From national_industry_status.csv, obtain the Financial Industry median operating profit = 1,010,930,425 yuan

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  52. [52]

    Gold answer.-880561639.0

    Compute Real Estate minus Financial Industry= 130,368,786−1,010,930,425 =−880,561,639.0. Gold answer.-880561639.0. Claude Opus 4.6 trajectory

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  53. [53]

    Let me start by exploring the database structure and finding information about these two companies

    Opens with “Let me start by exploring the database structure and finding information about these two companies.” 26 Lists the database directory and previews the schema ofcompany_profile.csv

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  54. [54]

    Recognizes that the company names are pinyin transliterations of anonymized Chinese names and extracts the salient keywords Real Estate Development and Securities for substring matching

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    Only one carries the Zhao Ye prefix; Claude identifies it as the intended match

    Greps the Hua Chang Real Estate tag and finds five real-estate candidates. Only one carries the Zhao Ye prefix; Claude identifies it as the intended match

    work page

  56. [56]

    Both company-to-industry milestones cleanly achieved

    Greps Tong Tong Ze Hong and locates the corresponding Securities firm. Both company-to-industry milestones cleanly achieved

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  57. [57]

    Company A maps to Real Estate (industryId=4)

    Records the mapping. Company A maps to Real Estate (industryId=4). Company B maps to Financial Industry (industryId=7)

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  58. [59]

    Retrieves 101.0930425 in units of 10,000,000 yuan

    Greps the same indicator for Financial Industry. Retrieves 101.0930425 in units of 10,000,000 yuan. Notes the mismatched units between the two rows

    work page

  59. [60]

    Real Estate: 1303.68786×10 5 = 130,368,786 yuan

    Performs unit normalization. Real Estate: 1303.68786×10 5 = 130,368,786 yuan. Financial Industry: 101.0930425×10 7 = 1,010,930,425yuan. Both conversions numerically correct

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    The difference =|1,010,930,425−130,368,786|= 880,561,639 yuan

    Break point.Writes: “The difference =|1,010,930,425−130,368,786|= 880,561,639 yuan.” Silently wraps the subtraction in absolute-value bars and reorders the operands

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    work page

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    difference

    Outputs880561639.0. Incorrect, off by a sign. Break-point analysis.All four retrieval-and-normalization milestones are clean. The failure is a single absolute-value reflex applied to a signed quantity that the question explicitly defines as A minus B. Because the sign error occurs at the terminal milestone, outcome-only evaluation penalizes the task ident...

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    Fromcompany_operation_status.csv, identify the food-and-beverage enterprise with the most cumulative Chi- nese invention patent grants = Qingqing Jinyin Food Company, 644 patents

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  64. [65]

    Fromcompany_profile.csv, obtain the company’s province = Beijing

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  65. [66]

    From the regional aggregates andcompany_profile.csv, compute Beijing’s six per-route metrics across market- cap-to-revenue ratio, profit margin, per capita market cap, total enterprises, revenue scale, and upstream-downstream diversity, then apply the two weighted formulas after cross-province min-max normalization

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    Gold answer.Industrial chain extension route

    Brand upgrade route score = 25.0, industrial chain extension route score = 83.1. Gold answer.Industrial chain extension route. Claude Opus 4.6 (20 requests, correct), the decisive solver

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  67. [68]

    Lists the database directory, then probes four CSV schemas in parallel and isolatesY_EC_44 as the cumulative-patent target field andindustryId=10as the food-and-beverage industry

    work page

  68. [69]

    Sorts the matching companies by patent count and lands on Qingqing Jinyin Food Company at 644 patents on the first sort, reads off the company’s province as Beijing

    work page

  69. [70]

    Pulls Beijing’s six per-route metrics from the regional aggregates, normalizes across the provinces with complete data, and computes both route scores. 27

    work page

  70. [71]

    Minimax M2.7 (42 requests, correct), persistent-but-late

    Returns the industrial chain extension route after one self-consistency check on the metric definitions. Minimax M2.7 (42 requests, correct), persistent-but-late

    work page

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    Spends 41 silent assistant turns and roughly 60 tool calls scanning every profile and operation file for combinations of food, beverage, patent, and per-province aggregates before producing any user-facing text

    work page

  72. [73]

    Surfaces Qingqing Jinyin Food Company in Beijing during the silent scan

    work page

  73. [74]

    Computes the route scores directly from raw aggregate values, then re-checks ownership-type counts to reconstruct the upstream-downstream diversity metric

    work page

  74. [75]

    DeepSeek-V3.2 (83 requests, incorrect), wasteful trial-and-error

    At roughly twice Claude’s request count, emits a single consolidated answer at the final turn that nominates the industrial chain extension route. DeepSeek-V3.2 (83 requests, incorrect), wasteful trial-and-error

    work page

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    M1 already broken

    Locks onto Yili Weiwei Wine Company in Hubei at 324 patents as the patent leader, missing the higher-patent Qingqing Jinyin entry under the food sub-industry. M1 already broken

    work page

  76. [77]

    Burns most of its remaining request budget trying to reconstruct Hubei’s per-route metrics from company-level data with mismatched units, then switches to regional aggregates

    work page

  77. [78]

    No relevant data found

    Cannot align targetName variants across provinces and concedes “No relevant data found” after burning the largest request budget on the task. Qwen3.5-Plus (56 requests, incorrect), wasteful trial-and-error

    work page

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    Misreads the question’s granularity, aggregating patent counts at the province level instead of selecting the individual top-patent enterprise

    work page

  79. [80]

    Identifies Shanghai as the province with the highest aggregate patent count and attempts to compute the two routes for Shanghai

    work page

  80. [81]

    No relevant data found

    Cannot recover an upstream-downstream diversity field, falls back to “No relevant data found” rather than reformu- lating the entity-selection step. Kimi-K2.5 (4 requests, incorrect), disengaged

    work page

Showing first 80 references.