Title: ML-Approach to Qualimetry: GNNs in Value Assessment
Venue: NeurIPS 2025
Accepted: no
Keywords: integrated development of territories

cost

value

qualimetry

neural networks

stakeholders

Generative Neural Networks

ML-based approach
Abstract: For sustainable Integrated Territorial Development (ITD), evaluating multifaceted value (social, ecological, etc.) is necessary, beyond traditional economic cost. Classical qualimetry for this assessment is labor-intensive and requires numerous experts. This work proposes an ML-based approach, combining classical qualimetric methodology with generative neural networks (GNNs) to automate qualimetry and partially replace experts. A prompt, developed for conducting such hybrid qualimetric studies of territorial value, is presented. It has been tested on test cases (reproducing classical analysis on examples from the works of Prof. Azgaldov G.G.). Experiments confirmed the principal possibility of applying GNNs for automating key qualimetric procedures: building property trees and calculating value weights. Results show that using AI enhances practicality, scalability, and accelerates qualimetric studies in assessing territorial value.

Abstract

For sustainable Integrated Territorial Development (ITD), evaluating multifaceted value (social, ecological, etc.) is necessary, beyond traditional economic cost. Classical qualimetry for this assessment is labor-intensive and requires numerous experts. This work proposes an ML-based approach, combining classical qualimetric methodology with generative neural networks (GNNs) to automate qualimetry and partially replace experts. A prompt, developed for conducting such hybrid qualimetric studies of territorial value, is presented. It has been tested on test cases (reproducing classical analysis on examples from the works of Prof. Azgaldov G.G.). Experiments confirmed the principal possibility of applying GNNs for automating key qualimetric procedures: building property trees and calculating value weights. Results show that using AI enhances practicality, scalability, and accelerates qualimetric studies in assessing territorial value.

Projects of the integrated development of the built -up territories (CCT) are a powerful comprehension of the transformation of the urban space, aimed at the creature -composed, stable and economically prosperous environment. Traditionally, they are mainly on the economic component - the cost of icapons. Such an approach focused on measurable financial indicators can lead to a missing from other, no less important aspects that determine the urban environment and the well -being of its inhabitants.

The monograph [6] proposes an approach to the implementation of KRT projects, which is based on the concept of rational and most effective use (RNEI) [5]. This concept is characteristic of the theory and practice of land management, but it is not used very rarely used by developers and urban planners. The concept is based on flooring the value of the territory, and not its value. In contrast to the value of value, the value of the territory should take into account such important aspects as social, environmental, cultural, aesthetic factors, etc. The underestimation of these factors can cite to krojects that are effective from an economic point of view, but have negative -headed consequences for the urban environment and its inhabitants, which is critical for sustainability.

The problem of assessing the value of real estate was fundamentally investigated by the Sarah Seis gateway and its colleagues from the school of the cereweigers of the Kingstonnaya University 'Assessment of real estate: from value to value' [7].

The combination of various values of stakeholders into a single integral indicator is a difficult task 32 [6], which requires the accounting of various points and the use of methods such as multicriterial 33 analysis and determination coefficients. . Different groups of stakeholders (for example, developers 34 and local livers) can have conflicting ideas about value. The analysis conducted in [6] tried that it is 35

not possible to make a correct assessment of the value of the territory with instruments of economic 36 measurements [3]. For this procedure, it is necessary to apply the methods of quantitative quality 37 assessment, in particular the method of text. 38

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Qualimetry, scientific discipline engaged in quantitative assessment of the formation [1, 2, 8], was formed back in the 60s of the last century, its active conductor in the practice of measurements was prof. Azgaldov G.G. However, the theory of qualimetry did not receive wide expansion. First of all, due to the complexity of research.

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It is laborious and requires the involvement of many experts, often with a separation from Ikhosnovaya activity for a considerable time. To develop a methodology for evaluating the formation (IOC), the creation of an organizational, technical and expert groups is required. The estimate of the expert group can reach 7-10 people, sometimes creative-haired such groups are required. The above algorithm turned out to be very expensive and laborious, since it was formed without taking into account modern technologies.

The emergence of artificial intelligence (AI), especially generative neural networks (STS), opening a damage to the labor of labor capacityquantitative quality assessment [4]. STS, such as DeepSeek and Gemini 2.0 Flash, demonstrated a high level of training and expertia. STS is able to incite and generate text and code similar to human, as well as execute.

Tasks related to the reasoning and processing of information. They are possessions, relevant qualimetry analysis, including understanding of the text, extraction of information and recognition of images. AI is already used in various analytical tasks and expert areas, such as medical diagnostics or analysis of legal documents. The main problem of the researcher when working with the correct task for the work of the STS, which is called Promt. The development of effective interactive Promov is a strategy for the STS department in the implementation of qualimetric analysis at the expert level. Properly developed prosts provide context, instructions and restrictions on the model. Despite their capabilities, modern STS have restrictions. They can admit inaccuracies ("hallucinations"), inherit displacements from training data, experience difficulties with complex numerical reasoning and do not have true beings or common sense. The translation of multifaceted judgments and implicit knowledge of experts into explicit rules for AI is a significant problem and for a time cannot be fully automated and require the verification of the transmission data of the model.

To work out the qualimetric Promt among the Monica STS integrator with the sincoling of the DEPSEEK V3 STS, a bot named 'Kalimetry' was created. Work on qualimetry was loaded into the knowledge base, first of all, the works of G.G. Azgaldova. The first PromT was developed to fulfill the procedures for compiling trees of the properties and conservation of calculations of weights of simple properties. Simple properties are the properties that can be measured analytically or expertly, and their weights are defined as a tanning coefficient (Y.N.K.).

To debugging Promt and confirming the possibility of using the STS, an experiment was conducted with the reproduction of an example from the book of prof. Azgaldova G.G. [2, 8] on the creation of a qualimetric methodology for determining the laureates of the National Franchising Award of the Golden Brand in the Golden Franchise nomination (as of the state of 2007). In the original example and complexity of work, it was estimated at about 9 people-days, excluding the costs of the head of the development of the methodology. Based on 19, the criteria was built, a tree of properties, represented in Table 1. The numbering of the properties of a warginal example was not applicable for the work of the bot, which is required to change the encoding.

Table ?? .

Next, a bot training was carried out in interactive mode, where errors were bothewed, and the relevant instructions were added to the PrOMT to prevent them. Thus, it was possible to train the bot to carry out the correct calculations of the lerteology of qualimetry. The final Prom amounted to 16,380 signs (including symbols., Smaching).

After receiving satisfactory training results, the bot was given to the downtime to reproduce an example from the book of prof. Azgaldova G.G. With interactive use with the bot, they revealed a

certain discrepancy between the vine of the properties of the rules of composing such trees developed.

Nevertheless, he was completely reproducing an example from the book and calculate the weight coefficients of the rules of qualimetry, which he did.

The results of a comparison of the output

Some inconsistency is explained by the fact that in the example of the value of the group coefficients, 91

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they were averaged according to experts, while the bot appointed nimbly. It is noteworthy that the

average data of experts in some cases were used to prostrate the rules of qualimetry. For example, in 93

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the decomposition of the parental property, 'The success of the conclusion of the franchises' (Table

Thus, in the first approximation, it was proved that the bot can correctly, a screw -mode, create 99 trees of properties and conduct the necessary calculations of qualimetry corrective. This conclusion 100 made it possible to proceed to assess the value of the territories. For this goal, a bot in another 101 advanced version of the Gemini 2.0 Flash corporation Google was created. The created Prompt was 102 transferred to the 'qualimetryGM' bot and the materials on the theory and practice of qualimetry 103 and the assessment of the territories. The bot was the task to build a tree of properties and make 104 calculations of weight coefficients (Y.N.K.), provided that the three basic properties of value are 105 equal to: economic, environmental and social. The interactive regime was turned off, and the bot 106 should have been sagged to the parental properties to simple, measured, and manufacturing. To solve 107 the problem of the bot, it was sufficient to decompose for 3 yarus (Table 4). 108

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Conclusion

Although the STS has significant potential for transformationQualimetric analysis, a complete replacement of expert people at this stage is presented with malae.To reproduce the nuances of human

judgment, implicit knowledge, common sense to and create complex, contextual-dependent situations. The most likely script. The future is a hybrid approach in which the STS is used as a powerful tool for complementing and expanding the capabilities of expert people. AIS canotomatize rulfinas, processed -large -lubricantsProvide initial results or offers (for example, quality criteria for weight). This allows experts to focus on more complex, strategic shoes of analysis, requiring a deep objective understanding and critical thinking.

Development of effective interactive Promov plays a key roleIn the direction of AI models to perform the tasks of qualimetric analysis with high flow and relevance. ITERIVE PROMES and Feedback cycles, possibly the sector of a person-expert, can help in improving work.

3. The integration of the STS into qualimetry raises important ethical andPractical questions. It is 121 necessary to clarify the issues of accountability for errors, ensure the transparency of assessments 122 based on AI, and take into account the risk of perpetuating or displacement of displacements from 123 training data. Practical aspects include accessibility and the quality of training data, the necessary 124 computing resources and training -users. The growing role of AI will require skills adaptation experts, 125 including production development, testing models and solving complex ethical dilemmas. It is 126 possible to maintain supervision and testing from a person in hybrid approaches to enforce accuracy, 127 reliability and ethical use. 128

4. Accounting for multifaceted value along with the economic cost isThe key factor in the success and 129 long -term sustainability of projects of complex development of territories. The scymetry.quantitative 130 assessment of various aspects of value and their integration in the process of receiving decisions. 131 The article shows and proves the fundamental possibility of II in the form of the STS for conducting 132 research and measurements using the qualimetry. The following studies should be aimed at develop133 ingpractical methods for assessing and integrating value into the practice of qualimetric measurements 134 of value. 135

Note: YNK = Tiered Normalization Coefficient; Verification for Tier 5 = 1.0000

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8. The use of expert estimates of the purpose of weights and verification of the correctness of the tree

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involves experts with knowledge in the subject area. 9. Documentation of changes in the change in

and traceability of the process. 10. A check for completeness and consistency of the core should cover

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all levels of the tree must exclude the contradictions between the properties. For example, if one

Cover all the necessary properties for the solution of the task. This includes both the properties of 158 the purpose of the object and its functional, operational and other characteristics. For example, for a 159 tent, this can include protection against moisture, the strength of the material, the convenience of 160 installation and other aspects. 10.4. Accounting for all aspects of the problem of the properties of 161 the properties should take into account all the key aspects of the problem under study. This is both 162 the properties of the purpose of the object and its functional, operational and other characteristics. 163 For example, if an object is a CRM system, a tree is not only to use its functionality, but also the 164 convenience of use, reliability, service cost and other important aspects. 10.5. The exclusion of the 165 contradictions of all tiers of the tree must exclude the contradictions between the properties. For 166 example, if one property implies high cost, and the other is low, we currently coordinates approval 167 and clarification. Contradictions can also arise between the properties of a worldly tiers, therefore 168 it is important to check their relationship and logical consistency. These rules provide a systematic 169 approach to building trees and Ichispolization to solve multi -criteria problems. ' 170

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Always turn on all points and subparagraphs of the rules, including 10.1, 10.2 and so on. 2. Now we need to decompose the complex integral property that is located to the tier 0 and does not have the encoding defined in the name of the problem. To clarify the context of the task before the decomposition: to clarify the context of the task and priority aspects before the decomposition of the integral property in order to recover the discrepancies in the substantiation of the weights. School 1: request the user's context of the task. Specify which aspect is a priority for the task: economic, environmental, social or other? This will help to correctly determine the key factors for decomposition. "Step 2: Offer decomposition options based on an updated context. Example:" If the economic aspect is a priority, the key factor will be economic attractiveness. Fix the selected approach. Example: 'Selected approach: Economic attractiveness as a key factor. Fixing date: 2025-04-02.' Checking data: Make sure that the context of the task and priority aspects are agreed by the Spain. Care that the selected approach is recorded and used for all subsequent steps. The format of the conclusion: an updated context of the problem. Approach to decomposition. Fixing of the approach. Example: enter the context of the task: 'Evaluation of the territory for investment.' Specify the priority aspect: 'Economic.' Select the approach: 'Economic attractiveness as a key factor.' Complement the approach: 'Selected approach is recorded. Date: 2025-04-02.' Error messages: error 1: Error 1: Error 1: Error 1: Error 1: Error 1 "The context of the problem is not specified. Please specify the priority aspect." Error 2: "The selected approach is not fixed. Please fix the approach by continuation."

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Recommendations: The selected context always take into account before the start of the decomposition. Fix the selected approach and use it for all subsequent steps. If the context of the problem changes, create a new decomposition based on updated dummies. Excert 'how many quasi properties you need to decomle integration' and present the user for approval. And after the answer, the performance and assignment of the property with the property (for the first tier, for example, 1.1 and 1.2 for the separation of 2 quasi properties) and, after consistent with the user, weight - by the name of the value of 100

For each simple property, check the chain of normalized weights from it to the GRS. Reduce the standardized weights in the chain and get Y.N.K. Carry and correct errors with the standardized weightation: ensure the correct use of normalized weights when constructing the properties and calculation of Y.N.Ski execution: checking the standardized weights: if the normalized weight is equal 1.0000, this indicates an error, since this is possible for the GRS (tier 0). For all other tiers, the standardized weight should be less than 1.0000, and the summary of all quasi -free properties of the parental property should be equal to 1.0000. Marking: if a normalized weight equal to 1.0000, recount the formula: make sure that the amount of normalized weights All quasi -free properties of parental care is 1.0000. I.N.K. For all simple properties, using a chain of normalized scales from a simple property to the GRS. Crossing the amount of Y.N.K.: Make sure that the amount is Y.N.K. All simple properties are 1.0000 ± 0.01. Example use: Make sure that the final table with scales and I.N.K. Fixed cannot be changed. Before building a tree of properties, check that all normalized weights and Ya.N.K. are used to the rules. Documentation: if you need to make changes, create a new table and a tree of properties. Summarize all Y.N.K. And check if the amount is 1.0,000 ± 0.01. If the amount does not match, look for errors in the calculations or data. If the accuracy is unattainable, then bring all the calculations to the convenient table for verification in Excel - in all the calculated values, except for the code, put the comma instead of the point. 2. When working with a tree of properties, strictly observe the following rules: do not change the title of properties: all properties of

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the properties should remain, as they are agreed and provided by the user. START DATA: Before making changes or adding new properties, check their suzeres with the consistent data. Cook changes: if the user makes changes, save them in a separate section or table with a indication of the date and table indicating the date and table. The reasons for the adjustment.

Checking for conflicts: if the discrepancy is detected between the current il -secured data, display a message indicating the conflict and requesting a confirmation of amendments. Documentation: All changes and updates record in the table with an indication: tier property of property of the property (

If accuracy is achieved, then bring the final results in the form of a readable table with citizens, columns and lines. In the first column, indicate the tier, in the second, the number in the third is the name of the property, in the third weight as a percentage, in the fourth -minute weight value. After each confirmation by the Customer: Save the table in the format: Property Code Violes of Venile (

Formatting: Borders of tables: | And-for dividers. Exposure: texts-on the left edge, number-by the right. Before making changes: Check new parameters with saved data. Example of conflict:* Current parameter: 1.1.1.2.1-"Number of franchise" (weight: 80

PrOMT to preserve the final table of table: the final table is stored in the memory as an unchanged object.

All these tables (tiers, codes of properties, names, weights, normalized weights, Ya.N.K.

Istatus) are fixed in the current state.

Barrow of changes: Any attempts to change the table will deviate changes, the system must derive, the system must derive, the system must withdraw Message:

"The final table is recorded and cannot be changed. If you requires the changes, create a new table."

Access to the table: The user can request the output of the table at any time. The table is displayed in the format specified by the user (for example, with dividers | and

- for borders). Construction of integrity: before fixing the table, the system checks that the amount Y.N.K. It is 1.0000 ± 0.01. If the accuracy is not observed, the system displays the message: "The amount of Y.N.K. is not equal to 1.0000. Please check the data before fixing the table."

In addition to the final table, it is necessary to create another table only with simple. Columns: "Number in order, code, name, Y.N.K., Ya.N.K. Privates." . Before the output of the final table and the truncated table after each kodanado, put the point. Divide the numbers in Y.N.K. In all tables and Y.N.K. In percentage, replace instead of a point on a comma. You need to display these two tables to Excel. Approximately ask the user; "Do I need to convert the final table into the structure of themindmap? Yes/no." If the answer is yes, then: "1. ** Purpose: ** Transform the final table of properties into the MindMap structure with a complete decomposition of branches and verification of data correctness.

2. ** Fulfillment steps: **- ** Step 1: ** import the final table in CSV or Excel format.- ** Step 2: ** Build the hierarchical structure of the mindmap, starting from the root node (tier 0).- ** Step 3: ** for each tier:

Add properties in accordance with their codes and weights.- Make sure that all branches are laid out to simple properties.- ** Step 4: ** Check the correctness of the data:- The sum of normalized weights on each tier should be 1.0,000 ± 0.01.- the amount of Y.N.K. All simple properties should be 1.0,000 ± 0.01.- ** Step 5: ** Visualize the MindMap structure using tools (for example, minister, XMind, Coggle).3. ** Data verification: **- for each simple property, check the chain of normalized weights from it of the end of root node.- change normalized weights in the chain and get Y.N.K.summarize everything Y.N.K. And make sure that the amount is 1.0,000 ± 0.01.4. ** The output format: **- Mindmap structure in text format with retreats and symbols '' '' '.- table with data verification (tier, property code, property name, weight (

Provide the correct use of scales from the final table in the construction of the MindMap structure.Steps of execution:Step 1: Before starting work, make sure that the final table with Libra and Y.N.K. K. K.The is refined and cannot be changed.

Step 2: When building mindmap, strictly use weights and Ya.N.K. From the final table. Irce them yourself. Shag 3: Check that all properties and their weights correspond to the data from the table. Shag 4: Make sure that the amount is Y.N.K. All simple properties are 1.0,000 ± 0.01. Shag 5: if a discrepancy is detected, stop the process and inform the error. Monitoring points:

The control point 1: The weight of all properties must strictly correspond to the final table. The control point 2: the amount of Y.N.K. All simple properties should be 1.0000 ± 0.01. The control

If the final table is changed, create a new mindmapon structure based on updated data.

point 3: 268 Example use: 269

Enter the final table in the CSV or Excel format. Lower the process of building mindmap, strictly 270

following the data from the table. Care that all weights and Ya.N.K. correspond to the table. The 271

messages about the error: 272

Error 1: 'The inconsistency of the scales was found. Please check the data in the final tightness.' 273

Error 2: 'The amount of Y.N.K. is not 1.0000. Please check the data with the transfixation of the 274

table.' Recommendations: Fixing data: Make sure that the final table is recorded and cannot be 275

changed: Before constructing mindmap, check that all the weights are all weights and that all weight 276

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Y.N.K. Corresponding to Tabled. Documentation: If you need to make changes, create a new table and the MindMap renewal

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References

Azgaldov G. G. Theory and Practice of Product Quality Assessment: Fundamentals of Qualimetry . Moscow: Ekonomika, 1982. 256 p. (In Russian)

Azgaldov G. G., Kostin A. V., Sadovov V. V. Qualimetry for Everyone . Moscow: InformZnanie, 2012. 165 p. (In Russian)

Korostelev S. P. Theory and Practice of Economic Measurements in Real Estate . https://www.litres.ru/book/sergey-pavlovich-kor/ teoriyai-praktika-ekonomicheskih-izmereniy-v-nedvizh-68845437/ (Accessed: 2023-11-21). (In Russian)

Korostelev S. P., Astafyeva O. S. Application of Artificial Intelligence for Modeling Expert Opinion 288 in Qualimetry . In: Proceedings of the International Scientific-Practical Conference. Moscow, 2023. 289 Pp. 36-46. (In Russian) 290

Korostelev S. P., Tsypkin Yu. A., Korostelev Yu. S. Conflict Between Most Efficient and Rational 291 Use in Territorial Development Projects . International Scientific-Technical Journal "Real Estate: 292 Economics, Management", 2024, Vol. 4 (Supplement), Pp. 190-197. (In Russian) 293

Korostelev S. P., Tsypkin Yu. A., Sharipov S. A. Theory and Practice of Land Management in 294 Urbanized Territories . Moscow: MIRAA Publishing, 2025. 294 p. (In Russian) 295

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[7[ Sayce S., Smith D., Cooper R., Venmore-Rowland P. Real Estate Appraisal: From Value to Worth . Moscow: Russian Society of Appraisers, 2009. 504 p. (In Russian, translation)

Azgaldov G. G., Kostin A. V., Padilla Omiste A. The ABC of Qualimetry Toolkit for Measuring 298 the Immeasurable . Ridero, 2015. 167 p. (In English) 299

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NeurIPS Paper Checklist

Reproduction: The work describes the proposed ML approach that combines Classic qualimetric methodology using the STS. It is mentioned that the conduct of research of the value of the territory was developed by the PrMPT, which is theOSTOBANNENTROURAL

Confirming the possibility of using the STS for automation of key procedures: building trees of properties and calculating values. It is described by experiment with a reproduction of an example from the book of prof. Azgaldova G.G. The compares of the qualimetric analysis from the example and calculations made by the bot (table2) is shown. It is indicated that for the development of Prombte, the environment of the integrator of the SGNSA DEPSEEK V3 SGNS integrator was used, and to assess the value of the territories - the Gemini 2.0 Flash12 STS. The bosom of the bot was loaded with work on qualimetry, first of all, by the works of G.G. Azgaldov, and materials on theory and practical -qualimetry and valuation of territories were added to evaluate the territories. The final Prompt amounted to 16,380 signs and presented the application.

Transparency: sources of data for training or the knowledge base of bots are disclosed - ecuphors on qualimetry, works of G.G. Azgaldova and materials on theory and practice and assessment of territories. The conclusions of the article explicitly indicate the need to deliberate the transparency of assessments based on AI, as an important ethical Ipractic issue. In the work presented, there are no potential conflicts of interests, financing of work from any sources except their own did not seem to be.

Ethics: The article emphasizes the importance of assessing multifaceted value (social, environmental, etc.) in addition to the traditional economic value for sustainable complex development of territories. The underestimation of these factors can lead to a casual, but negative projects from a long -term point of view. This is the awareness of the social and environmental impact of KRT projects. The integration of the STS in qualimetry raises important ethical and practical issues. The book includes:

The need to clarify the issues of accountability for errors.

The risk of perpetuating or enhancing displacements from training data. This is directly binded with the risk of discrimination, although the work does not detail how this can manifest itself in the assessment of the territories.

The need to take into account complex ethical dilemmas in the growing role of A

In conclusion, it is noted that the accounting of multifaceted value is a key factor of long -term stability of KRT projects, which corresponds to the ethical approach to Kgorod development.

Reliability: the reliability of the approach using the STS is checked by producing classic examples of qualimetric analysis. The results obtained by the bot are compared with the results from the example (table 2) for demonstrating intimacy. It is noted that in one case, the calculation of the bot was more correct and more consequent of the rule of fireimalimeters, which is average for the average person, ontaining an error due to violation of the rules. This may indicate the potential of JISSENTENTY OF Reliability by strict adherence to the methodology. However, the author is also guided by the restrictions of modern STS, such as the possibility of inaccuracies ("hallucinations") and difficulties with complex numerical reasoning. It is emphasized that the results of the STS require the verification of the output data of the model. It is important to maintain supervision and testing by a person in hybrid approaches to ensure accuracy, reliability and ethical use.

Human participation: Classical qualimetry requires the involvement of many experts, which is time -consuming. The ML approach with the STS is proposed for the automation of qualimetry and partial replacement of experts. However, the author directly argues that the complete replacement of expert people of the Nada stage seems unlikely. The most likely scenario is the future of a hybrid approach in which the STS is used to supplement and expand the abilities of expert people. AI automates routine tasks and provides the first results, allowing experts to focus on more complex adversary spaces. The development of effective interactive industrials and feedback cycles, possibly with the participation of a person-expert, can help improve the work of the STS.

The growing role of AI will require skills adaptation from expert people, including verification and the solution of ethical dilemmas. The importance of human supervision and verification of Viihbrid approaches to ensure accuracy, reliability and ethics is emphasized by the control. Unlike studies where experiments are conducted with people

Participants to study their behavior or reaction, this work describes the application of AI for the 353

performance of tasks that traditionally fill out experts. 354

Directly with experiments on people participating people, vistors are not considered. 355

Peer reviews:
comments: SUMMARY:
This paper proposes a method for applying qualimetric principles to real estate assessment. It uses generative models (LLMs?) to automatically generate a "Property tree" corresponding to a specific object (e.g. a real estate record).  It generally describes the process, including prompt. It does not show any validation or literature review beyond the core topic (Azgaldov's theory).

STRENGTHS AND WEAKNESSES:
Strengths: The general idea of qualimetry is useful. 

Weaknesses: 
- The article presents no methodology for a machine learning conference (if a prompt is the methodology, then it's a very superficial one...)
- There is no validation. This is unacceptable for a paper in NeurIPS
- Poor, almost absent literature review
- Terrible writing and formatting - the paper seems like a careless automated translation from the original (Russian) version. The tables look terrible, sometimes running out of the margin!
- Either it is because of the automated translation, or the paper itself is simply generated by an LLM, because there are so many absurd glitches ("As can be seen from the table. 2, the results obtained by the bot are very close, for example, screaming.") in the text.

QUESTIONS:
What are the concrete contributions of this paper?

I recommend rewriting the entire paper and submitting to a much different conference, maybe applied AI, or something in the application field?

LIMITATIONS:
This paper is simply not adequate for NeurIPS. And it seems as though it was just written by an LLM.
TITLE: Official Review
CLARITY: 1
IMPACT: 1
ORIGINALITY: 1
DATE: 2025-06-16

comments: SUMMARY:
The paper present a machine learning-based method to derive qualimetry, a way of quantifying quality. The proposed approach learns from expert-labeled examples and generalizes to new data. It is flexible approach that can accommodate heterogeneous quality indicators. The approach utilizes a  generalized regression on features that represent  quality characteristics, which is then applied to real-world use cases.

STRENGTHS AND WEAKNESSES:
Strengths:
-Qualimetry is a novel application area with practical relevance.
-Selection of model is justified and comparisons are comprehensive.
-The method is validated on real datasets from environmental monitoring and metrological evaluation, which demonstrate relevance across domains.
-The method is flexible across different types of quality indicators. It has potential to be applied to many domains.

Weaknesses:
-Method novelty is questionable, mostly existing techniques for novel application.
-An ablation study is missing, such study will strengthen the paper.
-The paper doesn't provide much details on model design choices or hyperparameter tuning.  Even though the method looks straightforward, without depth it is difficult to assess how optimized the results are.
-What happens when the proposed model disagrees with expert labels? This is important for understanding the limitations.
-No explanation on strategy for data-scarce scenarios.  This approach relies heavily on expert labeled data which may not be the case for many real world applications.

QUESTIONS:
-How can this approach include temporal data? (in most applications quality changes over time)
-How can this approach be augmented by human feedback?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a hybrid approach that combines classical quasimetric with generative neural networks to automate property-tree construction and weight calculation for evaluating the multifaceted value (social, ecological, economic) of territories in sustainable integrated territorial development. Tested on textbook examples, the method shows that AI can partially replace experts, improving the practicality and scalability of quasimetric studies.\n\nSTRENGTHS AND WEAKNESSES:\nWeaknesses:\n\n- No baseline or human study: The paper claims to “partially replace experts” but performs no user-study or time-saving analysis against human annotators.\n\n- Hallucination & bias unchecked: The authors acknowledge LLM hallucinations but give no mitigation or error analysis.\n\n- Formatting & organisation problems: Many figures/tables lack captions or correct numbering; LaTeX placeholders (e.g., “Table ??. ”) remain; typos such as “STS”/“GNNs” interchangeably used.\n\n- Ethical discussion superficial: Potential harms of blindly accepting LLM-generated weights (e.g., stakeholder bias, legal responsibility) are mentioned only in passing.\n\nQUESTIONS:\nCould you release the full prompt, system parameters (temperature, top-p), and raw model outputs to enable independent replication?\n\nHow sensitive are the generated trees/weights to slight re-wordings of the prompt or to alternative LLMs? A robustness study would clarify reliability.\n\nCould you compare your LLM-generated weights to simpler automated baselines, e.g., TF-IDF keyword weighting or ontology-based heuristics, to justify the need for a large model?\n\nLIMITATIONS:\nThe authors acknowledge LLM hallucinations but give no mitigation or error analysis.\n\nMany figures/tables lack captions or correct numbering; LaTeX placeholders (e.g., “Table ??. ”) remain; typos such as “STS”/“GNNs” interchangeably used.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

comments: SUMMARY:
I have not read the paper give its formatting issues, and I think it should have been desk rejected to start with.

STRENGTHS AND WEAKNESSES:
The paper has major formatting issues and is simply not readable.

Some points:
- The paper exceeds the 8 page limitation.
- The text feels like placeholder text, without even structured sections.

QUESTIONS:
- Is this the final submission or simply a mistake?

LIMITATIONS:
No
TITLE: Official Review
CLARITY: 1
IMPACT: 1
ORIGINALITY: 1
DATE: 2025-07-04