2507.03811

# Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts ††thanks: This work was funded by the authors’ individual grants from Kunumi. **Authors**: Gianlucca Zuin13, Saulo Mastelini23, Túlio Loures13, Adriano Veloso > 1Universidade Federal de Minas Gerais (UFMG), Brazil2Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo (ICMC-USP), Brazil3Kunumi, BrazilEmail: {gianlucca, saulo, tulio, adriano}@kunumi.com Abstract Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of formal hierarchies and informal networks, and the need to ask the right questions. To address this, we propose an agent-based framework leveraging large language models (LLMs) to iteratively reconstruct dataset descriptions through interactions with employees. Modeling knowledge dissemination as a Susceptible-Infectious (SI) process with waning infectivity, we conduct 864 simulations across various synthetic company structures and different dissemination parameters. Our results show that the agent achieves 94.9% full-knowledge recall, with self-critical feedback scores strongly correlating with external literature critic scores. We analyze how each simulation parameter affects the knowledge retrieval process for the agent. In particular, we find that our approach is able to recover information without needing to access directly the only domain specialist. These findings highlight the agent’s ability to navigate organizational complexity and capture fragmented knowledge that would otherwise remain inaccessible. Index Terms: Organizational Knowledge, Agent-based simulations, Large Language Models ©2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. I Introduction In current fast-paced and knowledge-driven organizations, efficient management and sharing of knowledge remain challenging tasks. While explicit knowledge is easily documented, tacit knowledge — rooted in personal experiences and expertise — often proves difficult to systematically capture and disseminate. Sociological theories of organizational structure, including Max Weber’s bureaucratic model [1] and Frederick Taylor’s scientific management [2], provide valuable insights into how both formal and informal systems influence the flow of information. Weber emphasized the efficiency of rigid hierarchies in structuring authority and information flow, while Taylor focused on the benefits of standardizing knowledge to boost productivity. However, both perspectives highlight inherent challenges: rigid hierarchies can create bottlenecks, while more flexible systems risk fostering knowledge silos. Organizational structures, whether hierarchical, flat, or matrixed, impact how knowledge is disseminated within a company. These structures define the pathways of interaction among employees and influence knowledge’s accessibility and retention. Research has shown that knowledge dissemination in cooperative learning networks resembles the spread of disease through a crowd [3, 4]. Thus, by drawing on sociology and network theory, we illustrate information flow within organizations using epidemic models. The works of Polanyi [5] and Nonaka and Takeuchi [6] further underline the complexity of capturing and sharing tacit knowledge. Polanyi’s assertion that “we can know more than we can tell” underscores the implicit nature of such knowledge, while Nonaka and Takeuchi’s SECI model demonstrates how organizations could convert tacit knowledge into explicit forms through socialization, externalization, combination, and internalization. On the other hand, Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks, including natural language understanding, translation, summarization, and creative writing [7]. Their proficiency in processing and generating human-like text has opened new possibilities in areas such as automated content creation, human-computer interaction, and educational technologies [8, 9]. In this study, we introduce a novel method that leverages an LLM-based agent to uncover and document tacit knowledge within organizational contexts. To validate our approach, we allow the agent to navigate virtual company hierarchies, engaging with simulated employees—also modeled as LLMs—to iteratively gather and refine details about the structure, purpose, and possible applications of a data table, particularly in the realm of machine learning tasks. <details> <summary>extracted/6597023/Figures/fluxo_conversation3.png Details</summary>  ### Visual Description ## Flow Diagram: Conversation Loop for Automotive Performance Table Description ### Overview The image is a flow diagram illustrating a conversation loop between a user (Miguel Torres), an employee (Sophia Chen), and an AI agent to improve the description of an "AutomotivePerformance" table. The diagram outlines the steps of question asking, decision-making, description parsing, scoring, and self-reflection within the loop. ### Components/Axes The diagram consists of the following components: 1. **Conversation Loop:** The central theme, represented by a loop connecting different stages. 2. **Question Asking:** The AI agent asks Miguel Torres about the columns 'VCLN', 'PMAX', 'FRF', 'UREV', and 'SYSTM' in relation to automotive performance, and the expected data types. 3. **Employee:** Miguel Torres states he lacks specific knowledge and recommends reaching out to Sophia Chen. 4. **Flow Decision-making:** The AI agent decides to switch to Sophia Chen based on Miguel's recommendation. * Suggested Action: switch * Suggested User: Sophia Chen * Reasoning: The current user, Miguel Torres, has indicated that he does not have specific knowledge about the requested columns in the AutomotivePerformance table and has recommended reaching out to Sophia Chen for detailed insights. This suggests that Sophia is likely to have the relevant information needed. 5. **Description Parsing:** The AI agent parses the current description of the "AutomotivePerformance" table. * AutomotivePerformance Columns: * VCLN: Unknown data type and purpose (no specific information provided) * PMAX: Unknown data type and purpose (no specific information provided) * FRF: Unknown data type and purpose (no specific information provided) 6. **Scoring + Critic:** The AI agent scores the description and provides a critique. * Critic: Score: 2 * The description of the AutomotivePerformance table is significantly lacking in detail. None of the columns have their data types or purposes specified, which is critical information for understanding the table's structure and content. Without this information, it is impossible to assess how the data can be used or what it represents. The mention of potentially obtaining further insights from Sophia Chen does not compensate for the lack of information provided in the description. 7. **Self-reflection:** The AI agent suggests improvements to the description. * To improve the description, the following elements should be added: 1. Clearly define the data types for each column (e.g., integer, float). 2. Provide a brief explanation of the purpose of each column, including what data it is expected to hold. 3. Include any relationships with other tables, if applicable, to give context on how this table fits into a larger database schema. 4. Mention any constraints, such as primary keys, foreign keys, or unique constraints, if they exist. 5. Add example values for each column to illustrate the kind of data that will be present. 8. **Chat history, current table description, critic with score and self-reflection suggestions are fed back to the agent when deciding the next question.** 9. **Initial Message:** "Hello Miguel Torres! I'm excited to work with you on building the table description for AutomotivePerformance. Let's get started!" 10. **Final Message:** "Thank you, Miguel Torres, for your help so far! I appreciate your guidance and will now reach out to Sophia Chen for further insights on the AutomotivePerformance table." ### Detailed Analysis or ### Content Details The flow starts with the AI agent initiating a conversation with Miguel Torres to gather information about the "AutomotivePerformance" table. When Miguel indicates a lack of specific knowledge, the agent decides to switch to Sophia Chen. The agent then parses the existing table description, scores it (Critic: Score: 2), and provides suggestions for improvement. The suggestions emphasize the need for clear data type definitions, purpose explanations, relationship context, constraint mentions, and example values for each column. The chat history, table description, critic score, and self-reflection suggestions are fed back to the agent to inform the next question. ### Key Observations * The AI agent relies on human input to gather information about the table. * The agent can identify gaps in the existing table description. * The agent provides specific and actionable suggestions for improving the description. * The agent uses a scoring system to evaluate the quality of the description. ### Interpretation The diagram illustrates an iterative process of refining a table description using a combination of human expertise and AI capabilities. The AI agent acts as a facilitator, guiding the conversation, identifying gaps in knowledge, and providing suggestions for improvement. The process highlights the importance of clear and comprehensive table descriptions for effective data understanding and utilization. The loop emphasizes the continuous nature of improvement, where feedback and self-reflection are used to refine the description iteratively. The reliance on human expertise (Sophia Chen) suggests that AI is not a replacement for human knowledge but rather a tool to augment and enhance it. </details> Figure 1: The agent iteratively builds knowledge and decides on its next course of action as it interacts with company employees in a conversation loop. Our simulation-based approach reflects the complexities of real-world organizations. Employees modeled as LLMs interact with the agent, responding to inquiries, redirecting questions, or providing insights on specific aspects of the data. After each interaction, the agent assesses its progress, generating critiques and suggestions for improvement before determining the next steps as illustrated in Figure 1. This iterative process not only addresses knowledge gaps but also simulates human-like reasoning [10, 11], allowing the agent to adapt to various organizational structures and communication styles. Rather than relying on a human to locate the appropriate domain specialist within an organization—an often difficult and time-consuming task—our method facilitates the crowdsourcing of this process using LLM agents, creating a comprehensive repository of organizational knowledge. To evaluate the effectiveness of our method, we conducted 864 simulations across diverse synthetic organizational structures, knowledge dissemination strategies, and data configurations. Our LLM agent achieved a success rate of 94.9% in acquiring complete knowledge about the queried tables. These simulations encompassed over 300,000 interactions, including self-reasoning steps, producing a dataset exceeding 45 million words. This dataset serves as a valuable resource for advancing research into LLM-agent interactions and the relationship between organizational structures and knowledge management practices. For access to the full simulation chat log and prompts, see the Code and Data Availability statement. II Related Work Max Weber’s seminal work on social and economic organization [1] offers key insights into the functioning of bureaucratic and hierarchical structures. A central focus of Weber’s work is in his concept of authority, which includes traditional authority, rooted in customs, and rational-legal authority, based on formal rules and laws. Both of these forms of authority are highly relevant to companies and economic organizations. Weber highlights bureaucracy as the primary model of rational-legal authority, emphasizing its efficiency, hierarchy, specialization, and reliance on impersonal procedures. Weber’s concept of Verstehen (German for “interpretive understanding”) stresses the need to understand social actions through the subjective meanings individuals give to their behaviors. Additionally, his use of “ideal types” of behavior as a methodological tool help analyze many social and economic phenomena. These theories form the basis for understanding the formal mechanisms that govern information flow within organizations. Many other works in organizational theory build upon Weber’s ideas to explore additional aspects of organizational behavior, power dynamics, and decision-making processes. For instance, Peter M. Blau’s theories of social exchange [12] emphasize the informal dynamics of interaction, where reciprocity and trust become critical in sharing tacit knowledge. Blau’s analysis reveals how interpersonal relationships complement formal structures, resulting in collaboration and knowledge transfer. Diefenbach and Sillince [13] discuss the interplay between formal and informal hierarchies, which is crucial for understanding how knowledge propagates. They highlight that formal hierarchies are founded on clearly delineated roles and command chains, whereas informal hierarchies emerge from social interactions and shared norms. Hedlund [14] introduced the concept of heterarchies, challenging traditional hierarchical assumptions and providing a framework to analyze knowledge flow in multinational corporations. This perspective aligns with the need to model both structured and unstructured interactions in simulations. Mihm et al. [15] explored hierarchical structures and search processes, highlighting the challenges of navigating complex organizational networks. Their study employs simulations to reveal how hierarchy impacts solution stability, quality, and speed in problem-solving, showcasing the importance of adapting organizational structures to meet search requirements effectively. Incorporating these social theory perspectives provides a realistic foundation for constructing synthetic company hierarchies that reflect both structured and dynamic relationships. As such, the parameter choices in our simulations are heavily influenced by these foundational works—from Weber’s concept of bureaucratic organizations to Blau’s and Diefenbach and Sillince’s emphasis on informal connections. These theoretical frameworks not only ground our models in established sociological principles, but complement the emerging computational approaches to simulating human behavior. Recent work demonstrates LLM’s ability to model complex social systems in realistic simulations. Park et al. [16] proposed generative agents that simulate human behavior through dynamic memory mechanisms, enabling decision-making in a sandbox setting inspired by the game The Sims. These agents autonomously perform tasks, establish social relationships, and adapt behaviors through experiential learning. Dai et al. [17] investigated LLM-driven social evolution under Hobbesian Social Contract Theory, illustrates LLMs’ capacity to simulate foundational theories of societal dynamics and demonstrating agents transitioning from conflict in a “state of nature” to cooperative social orders via emergent contractual agreements. Qian et al. [18] introduced ChatDev, a framework where role-based LLM agents collaborate through structured dialogues to complete software development phases. Collectively, these studies establish LLMs as tools for emulating social behaviors across individual, organizational, and societal scales. However, to our knowledge, no previous literature proposes a method for simulating the human behaviors of interest to this work in synthetic organizational structures, namely, knowledge extraction processes. Regarding LLM themselves, advances in prompting strategies have shown to enhance its reasoning capabilities. Prompt-chains [19] iteratively refine solutions through task-specific prompts, while least-to-most prompting [20] decomposes problems into sequential subproblems. Chain-of-thought prompting [21] improves logical inference by generating intermediate reasoning steps. These divide-and-conquer approaches increase precision and reliability, particularly for iterative problem-solving contexts. Further, techniques to introduce reasoning into LLMs have also been shown to improve results. The self-reflection paradigm [11] enables LLMs to evaluate and critique their own reasoning processes. Similarly, self-critique and self-scoring mechanisms [16] have proven effective in assessing the quality of information generated by LLMs, demonstrating the advantages of feedback and iterative refinement. Building on these strategies, our approach develops a robust agent system to extract tacit knowledge from human specialists, consolidate it into documents, and validate its efficacy. These works also show how LLM-based simulations closely mimic human interactions, making them a suitable surrogate for testing and refining such methods. III Proposed Approach The challenge of documenting tacit knowledge within an organization requires a process that accounts for both incomplete initial knowledge and the dynamic nature of organizational networks. In our proposed approach, we introduce an agent-based framework designed to gather and document knowledge about dataset tables through iterative interactions with employees. The agent starts with a limited understanding and progressively refines its knowledge by engaging with the organization. However, real-world case studies can be difficult to obtain. To evaluate the effectiveness of this methodology, we employ simulations of organizational knowledge dissemination, modeling both formal and informal networks. In this section, we describe the key aspects of our agent design and the simulations conducted to assess its performance. III-A Prompt-chaining to build an Agent Our approach employs a prompt-chaining technique to develop an LLM-based agent for documenting tacit knowledge. Specifically, we wish to build a description for a table known to the organization. The agent operates in a partially observable environment. While such an environment holds access to the current table description (the true knowledge $k^{*}$ ), each employee’s expertise and network connections are uncertain. The agent does, however, know a subset of the network: the company’s hierarchical structure. This scenario can be modeled using a framework inspired by a Markov Decision Process (MDP). Briefly, it consists of: - Knowledge States ( $\mathcal{K}$ ): Represent the agent’s current understanding of the knowledge domain. - Actions ( $\mathcal{A}$ ): Consist of the set of Actions that an agent can take. - Transition Model ( $P(k^{\prime}|k,a)$ ): Probabilistic changes in knowledge state due to the actions made and changes in the environment. - Reward Function ( $R(k,a)$ ): Measures the completeness and accuracy of the knowledge state given the actions. Formally, let $\mathcal{K}$ represent the set of possible states that define the agent’s current knowledge level, and $\mathcal{A}$ represent the set of actions or prompts the agent can take. The transition probability, $P(k^{\prime}|k,a)$ , defines the likelihood of transitioning from state $k$ to state $k^{\prime}$ after taking action $a$ . The reward function, $R(k,a)$ , quantifies the benefit of the action in a particular state, reflecting the knowledge acquired. The process terminates when the agent reaches a terminal state $k_{t}$ , where all relevant knowledge about the proposed application is acquired. This is measured by $R(k_{t},a)≥\epsilon$ , where $\epsilon$ represents the minimum score for a suitable table description. The agent’s goal is to maximize the accumulated reward by formulating an optimal policy $\pi^{*}$ , which guides its decisions implicitly. The agent begins with a basic understanding $k_{0}$ of the table or organizational knowledge, knowing only the table’s name and columns. As the agent interacts with employees, it refines its state $k$ by incorporating new information and identifying gaps. The actions ( $\mathcal{A}$ ) involve generating questions aimed at filling these gaps. The agent uses its current state to formulate questions that address specific gaps in its understanding. Additionally, the agent’s self-critical assessments, which include quality scores, explanations, and suggestions for improvement, guide it in this task. The Transition Model ( $P(k^{\prime}|k,a)$ ) accounts for the probabilistic nature of state transitions after an action is taken. Due to the variability in LLM outputs and employee responses, these transitions are non-deterministic. The LLM manages these uncertainties by dynamically adjusting itself and its strategies in response to the feedback it receives. The Reward Function ( $R(k,a)$ ) evaluates actions, measuring alignment between the agent’s understanding and the expected complete knowledge. While these components are not explicitly modeled in the agent framework, they describe how the LLM organizes and selects prompts. The key goal is to maximize the accumulated reward, which helps guide the agent toward more effective knowledge acquisition. The agent interacts with employees based on their position in the hierarchy, structured as a stack, and prefers to start with those at lower levels to avoid burdening their superiors. However, if an employee is mentioned during a conversation - for instance, someone who likely holds relevant information about the table - the agent updates its knowledge of the company structure and moves this person to the top of the stack. Once someone with at least partial knowledge is found, the agent can move from one recommended employee to another, severely trimming the search space. The entire process is structured as a sequence of prompts and tasks: 1. Greet the employee to establish rapport; 1. Formulate a question about the data table; 1. Process the employee’s response and update self internal knowledge, building a new table description; 1. Critique the new updated description through: 1. Scoring the new description to evaluate its quality; 1. Criticizing it to identify gaps or inconsistencies; 1. Suggesting areas for improvement. 1. Decide whether to continue the conversation with the current employee or switch to another. A core aspect of this process is the self-critical feedback loop. After each interaction, the agent integrates the updated description, critique, score, suggestions, and chat history as inputs for the next question-generation step. These inputs, combined with the agent’s internal knowledge, guide the formulation of the next question, focusing on areas in which the understanding is lacking. This step involves using a meta-prompt to assess the completeness and relevance of the acquired information and evaluate its current knowledge state critically. Figure 2 illustrates the prompt used during this self-critical step. The agent then proceeds to self-reflect over the generated critic in order to evaluate the next course of action. Self-critic prompt You will review the table description and assess how complete it is. Here’s how to give feedback: - Score: Rate the quality of the description from 0 to 10, with 10 being excellent. - Reasoning: Explain why you gave this score, including any areas for improvement. - Suggestions: If the description score is below 8, suggest what can be added or refined to improve it. Aim for a balanced approach: emphasize what’s good, but point out areas where we can improve. Focus on understanding the table deeply and capturing its essence in the description. Do not use * or other characters. Use the exact same format as the one above. To reach a score of 5, we should know mostly everything about all columns. This includes column names, their meaning, and data types. If we also know some example values, then we can reach even a 6 or a 7. To reach a score of 8, we should also have tacit knowledge. Good variables to use, meaningful interactions, and so on. Guesswork is not allowed. If the description’s author mentions that they are not very certain about something, i.e., they are guessing what a column (or columns) mean, the score should be automatically lower. Be critical and do not be lenient. To reach a high score, a description should be really good and complete. Not knowing all the data types of the columns immediately implies a low score. Task: Critique the completeness of the description. Current table description Figure 2: Meta-prompt used in the self-critical stage. The agent critically assesses the current table description while providing suggestions on aspects to improve. III-B Simulating company structures We simulate the dissemination of tacit knowledge across a synthetic company network using epidemic models [22]. Specifically, we employ the Susceptible-Infectious (SI) model with waning infectivity [23, 24]. In this context, knowledge about a dataset table is treated as a collection of “diseases”, where each column of the table represents a distinct “fact” (a unit of information). The dynamics of the SI model in this framework are defined as follows: - S (Susceptible): Individuals who are unaware of a specific fact in the table. - I (Infectious): Individuals who know a fact and can share it. The likelihood of sharing decreases with time. The dissemination process begins with a “patient zero”, an individual who possesses complete knowledge (i.e., all facts corresponding to all columns of the table). Knowledge spreads through the organizational network, which consists of two components. The Hierarchy Network represents the formal organizational structure, explicitly known to the LLM agent; and the Relationship Network is a hidden network of informal connections (e.g., frequent interactions among colleagues) that the agent infers through its interactions. Intuitively, employees who work under the same team know each other, which leads to the Hierarchy Network being a sub-graph of the more complete Relationship Network. Before allowing the LLM to traverse the network, we disseminate knowledge using the epidemic model across the complete Relationship Network. The key feature of this model is the waning infectivity, where an individual’s ability to share knowledge diminishes over time. To simulate this, the time-dependent transmission rate $\beta(t)$ is modeled as: $$ \beta(t)=\beta_{0}e^{-\gamma t} \tag{1} $$ where $\beta_{0}$ is the initial transmission rate, and $\gamma$ determines how quickly the ability to share knowledge wanes. This SI model is used to simulate the uneven distribution of knowledge within the relationship network. Since the ability to transmit knowledge quickly wanes, over time some employees will know specific facts about the dataset table (i.e., become “infected”), while others will remain unaware. As such, we consider both: - Knowledge Transfer: Susceptible individuals S become informed about a fact at a rate proportional to $\beta(t)SI$ , where $\beta(t)$ decays over time. - Knowledge Loss: Infected individuals gradually lose their ability to spread a specific fact as $\beta(t)$ decreases. The agent’s task is to traverse this network after convergence using the initially incomplete knowledge about the evaluated table and employee relationships, hold conversations, and reconstruct the full table documentation by piecing together the facts that have spread across different individuals. To simulate diverse organizational structures, we define key parameters that allow us to model companies ranging from startups to large multinational corporations, from highly formal bureaucracies to agile, informal networks. - Max Hierarchical Depth: Shallow hierarchies represent organizations inspired by Taylor’s principles of functional management, emphasizing task specialization, direct oversight, and higher interconnectivity among employees. These structures are often seen in manufacturing setups or flat startups. Deep hierarchies reflect Weber’s bureaucratic organization theory, characterized by formal authority, strict adherence to rules, and clear chains of command. These structures reduce interconnectivity, with knowledge flow restricted to well-defined channels. By adjusting the hierarchical depth, we can simulate a spectrum of companies, from flat startups fostering innovation to rigid bureaucracies prioritizing stability and control. - Number of Employees: This parameter spans small teams to large multinational corporations. Smaller organizations model the dynamics of startups, where knowledge is often centralized or informally shared. Larger organizations allow us to explore the complexities of scale, including silos, multi-layered decision-making, and formalized processes. We assume balanced tree-like structures as the hierarchy. Thus, we infer the branching factor $b_{d}$ (that is, the number of people working under each boss) as: $$ b_{d}\approx\sqrt[hierarchy\text{ }depth]{number\text{ }of\text{ }employees} \tag{2} $$ - Alpha and Decay: These parameters govern how knowledge spreads and wanes in the relationship network. Slow decay mimics organizations with stable knowledge-sharing practices. High decay represents environments where knowledge quickly becomes obsolete, such as fast-paced industries or competitive workplaces. In our simulation, decay is directly tied to how far the knowledge can spread from the initial patient zero, while alpha governs the probability of knowledge being shared. - Number of Informal Connections: Informal connections mimic agile or networked organizations with significant ad-hoc collaboration, enabling knowledge flow outside formal channels. A low number of informal connections represents traditional, hierarchical organizations, where interactions align strictly with reporting structures. By varying the balance between formal and informal connections, we can simulate environments from rigid bureaucracies to dynamic, innovation-driven teams. These parameters enable the simulation of various company types, including startups (flat, highly interconnected structures with informal knowledge-sharing dynamics), large multinationals (deep hierarchies, rigid formal connections, and slow diffusion of knowledge), or even agile organizations (high mean degrees, shallow hierarchies, and dynamic knowledge-sharing networks). As per Max Weber’s social theory, these simulations capture the dichotomy between traditional bureaucracies (emphasizing stability and control) and more modern, flexible organizations. IV Experiments To evaluate the effectiveness of our approach, we conducted experiments with various configurations of company structures, knowledge, and dissemination rates. Figure 3 illustrate an example of a synthetic organization. For each of the parameters discussed, we elected a range of possible values and iterate over all possible combinations. We assess the statistical significance of our measurements through a pairwise t-test with p-value $≤ 0.05$ over 3 repetitions of each simulated environment and agent’s interaction. In particular, we consider: - Hierarchical Depth: 2, 5, 10, 20 (from shallow to deep) - Number of Employees: 20, 75, 200 (small, medium, and large organizations) - Alpha: 0.1, 0.5 - Decay: 0.5, 0.8 - Number of Informal Connections: 0, 2.5, 5 (from formal to heavily informal) - Number of Table Columns: 5, 20 <details> <summary>extracted/6597023/Figures/kwoledgelevel_1_.png Details</summary>  ### Visual Description ## Tree Diagram: Hierarchical Structure with Color Gradient ### Overview The image is a tree diagram illustrating a hierarchical structure. The nodes are represented by circles, and the connections between nodes are shown with arrows. The color of each node varies, ranging from light red to dark red, suggesting a gradient or scale associated with each node. The nodes are labeled with numbers from 0 to 24. ### Components/Axes * **Nodes:** Represented by circles, each labeled with a unique number from 0 to 24. * **Edges:** Represented by arrows, indicating the hierarchical relationship between nodes. The arrows point from parent nodes to child nodes. * **Color Gradient:** The color of each node varies from light red to dark red. The color intensity appears to represent a value or attribute associated with each node. ### Detailed Analysis or Content Details * **Root Node:** Node 0 is at the top of the diagram and serves as the root. It is a light red color. * **Level 1 Nodes:** Node 0 branches into nodes 1, 2, and 3. Node 1 is a medium red color. Nodes 2 and 3 are light red. * **Level 2 Nodes:** * Node 1 branches into nodes 4, 5, 6, 7, and 8. Node 4 is dark red. Node 5 is medium red. Nodes 6 and 7 are dark red. Node 8 is dark red. * Node 2 branches into nodes 9 and 10. Both nodes are light red. * Node 3 branches into nodes 11 and 12. Node 11 is light red. Node 12 is medium red. * **Level 3 Nodes:** * Node 4 branches into nodes 13 and 14. Both nodes are medium red. * Node 5 branches into nodes 15, 16, 17, and 18. Nodes 15, 17, and 18 are light red. Node 16 is medium red. * Node 6 branches into nodes 19, 20, and 21. Nodes 19, 20, and 21 are light red. * Node 7 branches into nodes 22, 23, and 24. Nodes 22, 23, and 24 are light red. ### Key Observations * The color gradient suggests a quantitative or qualitative attribute associated with each node. Darker red nodes might represent higher values or greater importance. * The branching structure indicates a hierarchical relationship, where each parent node can have multiple child nodes. * The depth of the tree varies, with some branches extending to three levels (e.g., 0 -> 1 -> 4 -> 13) and others only to two levels (e.g., 0 -> 2 -> 9). ### Interpretation The tree diagram likely represents a system or process where nodes are related in a hierarchical manner. The color gradient could represent a metric, such as cost, frequency, or importance, associated with each node. The diagram could be used to visualize and analyze the structure and properties of the system, identifying key nodes and relationships. The nodes with darker red colors (4, 6, 7, 8) are likely more significant than the lighter red nodes. The diagram highlights the flow from the root node (0) down to the leaf nodes (13-24), showing the dependencies and connections within the system. </details> (a) Hierarchy and knowledge levels. <details> <summary>extracted/6597023/Figures/relationships_1_.png Details</summary>  ### Visual Description ## Graph Diagram: Network of Nodes ### Overview The image depicts a network graph consisting of 25 nodes, numbered 0 through 24. The nodes are represented as light green circles, and the connections between them are indicated by black lines. The graph appears to have a hierarchical structure, with node 0 at the top and nodes 13 through 24 at the bottom. ### Components/Axes * **Nodes:** 25 nodes, numbered 0 to 24, represented by light green circles. * **Edges:** Black lines connecting the nodes, indicating relationships or connections. * **Layout:** The nodes are arranged in a roughly hierarchical manner, with node 0 at the top and nodes 13-24 at the bottom. ### Detailed Analysis * **Node 0:** Located at the top-center of the graph. It is connected to nodes 1, 2, and 3. * **Nodes 1, 2, 3:** Located in the second row. Node 1 is connected to nodes 0, 4, 5, 6, 7, 8, 9, and 10. Node 2 is connected to nodes 0, 7, 8, 9, and 10. Node 3 is connected to nodes 0, 10, 11, and 12. * **Nodes 4, 5, 6, 7, 8, 9, 10, 11, 12:** Located in the third row. Node 4 is connected to nodes 1, 5, 13, and 14. Node 5 is connected to nodes 1, 4, 6, 14, 15, 16, and 17. Node 6 is connected to nodes 1, 5, 7, 17, 18, 19, 20, and 21. Node 7 is connected to nodes 1, 2, 6, 20, 21, and 22. Node 8 is connected to nodes 1, 2, 9, 22, and 23. Node 9 is connected to nodes 1, 2, 8, 10, 23, and 24. Node 10 is connected to nodes 1, 2, 3, 9, 11, and 24. Node 11 is connected to nodes 3, 10, and 24. Node 12 is connected to node 3. * **Nodes 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24:** Located in the bottom row. Node 13 is connected to node 4. Node 14 is connected to nodes 4 and 5. Node 15 is connected to node 5. Node 16 is connected to node 5. Node 17 is connected to nodes 5 and 6. Node 18 is connected to node 6. Node 19 is connected to node 6. Node 20 is connected to nodes 6 and 7. Node 21 is connected to nodes 6 and 7. Node 22 is connected to nodes 7 and 8. Node 23 is connected to nodes 8 and 9. Node 24 is connected to nodes 9, 10, and 11. ### Key Observations * Node 0 has connections to only 3 other nodes. * Nodes 1, 2, and 3 act as central hubs, connecting node 0 to the rest of the network. * Nodes 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, and 12 have multiple connections, while nodes 13-24 have fewer connections. * The connections between nodes 4-12 and 13-24 are primarily one-way, with nodes 13-24 mostly connecting back to nodes 4-12. * Nodes 6 and 7 have a high degree of connectivity to the bottom row nodes (17-22). ### Interpretation The graph represents a network with a hierarchical structure. Node 0 is the root, and the network fans out to the bottom layer (nodes 13-24). The connections suggest a flow or dependency from the top nodes to the bottom nodes. The higher degree of connectivity for nodes 1, 2, and 3 indicates their importance in relaying information or resources from node 0 to the rest of the network. The bottom nodes (13-24) appear to be terminal nodes, with fewer outgoing connections. The graph could represent a variety of systems, such as a social network, a supply chain, or a computer network, where the nodes represent entities and the edges represent relationships or connections between them. </details> (b) Relationships graph. Figure 3: A synthetic organization with 25 employees, 4 hierarchical steps and 2.5 $×$ informal connections. Red shades indicate the amount of knowledge (e.g.: number of columns known) that an employee holds. Decay 0.8, alpha 0.1. For each repetition, a table subject was randomly selected from a predefined list, which was then used to generate the simulated complete knowledge $s^{*}$ through an LLM. This includes the overall table description, each column (with the respective type, meaning, and examples), as well as its possible variable relations. The possible table subjects for the generated knowledge include: aerospace, agriculture, automotive, business, construction, finances, food service, healthcare, machinery, mining, oil, packaging, energy, retail, sports, transportation, tourism, and tech. These subjects were selected following common industry sectors, as defined both in literature [25, 26, 27] and by the International Labour Organization https://www.ilo.org/industries-and-sectors. Given a parameter configuration, we then build the respective company structure, including the Hierarchy Network and the Relationship Network. In sequence, the individual facts from the original knowledge $k^{*}$ are disseminated in the company network using the SI epidemic model throughout the constructed company. These steps follow the methodology described in Section III-B. We also generate a textual background description for each employee, according to a job role as defined by the hierarchy and a randomized personality archetype, as well as a description of the information relevant to the problem they have access to: what partial knowledge of the table the individual has, their connections in the company’s network, and what partial knowledge they are aware their connections have (determined probabilistically). These backgrounds are intended to make responses more human-like and also serve as context to answer the main agent’s questions during the simulation. Finally, using the built structures and generated descriptions, we begin the conversation as described in Section III-A by assigning the questioning agent a random starting employee from the bottom of the hierarchy. All experiments were conducted on OpenAI’s GPT-4o mini model. V Discussion After conducting repeated conversational experiments with varying fictional company structures, we summarized the conversational statistics by averaging the results for each company structural parameter. We collected the self-critical score values generated during the conversations and other aspects that describe how the agent navigated the companies’ internal structures. In Table I, Len(path) represents the average number of non-unique fictitious employees contacted by the agent during inquiries about the data. In some cases, the agent contacted certain employees multiple times, effectively making these simulated individuals act as hubs within the conversational graph. We measured the average number of hubs (# Hubs) observed during the simulations. Additionally, we calculated the minimum distance between the first person contacted by the agent and “patient zero”, who possesses all the knowledge about the table’s columns. In the table, this metric is labeled Min. dist. and was obtained using the Dijkstra algorithm [28] applied to the relationship graph of each simulated company. We measured the percentage of times the conversational agent reached “patient zero” (% p0) and reported the average and mean self-critical score produced. TABLE I: Overview of statistics grouped and averaged for different simulated companies’ parameters. NIC: number of informal connections; MHD: maximum hierarchical depth. | Parameter | Value | Len(path) | # Hubs | Min. dist. | % p0 | Avg. score | Final score | | --- | --- | --- | --- | --- | --- | --- | --- | | Alpha | 0.1 | 31.6 | 1.26 | 3.94 | 0.38 | 5.49 | 6.79 | | 0.5 | 16.0 | 0.59 | 3.95 | 0.22 | 6.34 | 8.07 | | | NIC | 0.0 | 36.34 | 1.32 | 5.89 | 0.41 | 5.35 | 6.79 | | 2.5 | 19.69 | 0.82 | 3.06 | 0.26 | 6.1 | 7.66 | | | 5.0 | 15.38 | 0.65 | 2.89 | 0.23 | 6.3 | 7.84 | | | Decay | 0.5 | 27.81 | 1.06 | 3.91 | 0.34 | 5.67 | 7.2 | | 0.8 | 19.8 | 0.79 | 3.99 | 0.25 | 6.16 | 7.66 | | | MHD | 2 | 13.81 | 0.65 | 2.72 | 0.24 | 6.37 | 7.97 | | 5 | 25.0 | 1.01 | 4.01 | 0.32 | 5.8 | 7.36 | | | 10 | 27.85 | 1.06 | 4.39 | 0.29 | 5.74 | 7.19 | | | 20 | 28.56 | 0.98 | 4.66 | 0.33 | 5.75 | 7.21 | | | # Employees | 20 | 8.66 | 0.6 | 2.77 | 0.48 | 6.06 | 7.57 | | 75 | 26.67 | 1.08 | 4.05 | 0.28 | 5.94 | 7.59 | | | 200 | 36.09 | 1.1 | 5.02 | 0.14 | 5.74 | 7.13 | | | # Features | 5 | 25.13 | 0.91 | 4.01 | 0.28 | 6.2 | 7.6 | | 20 | 22.48 | 0.95 | 3.88 | 0.31 | 5.64 | 7.26 | | Regarding the parametization of the SI model, we observe that the average conversational path length tends to decrease as the alpha value increases, while the description scores tend to improve. Analogously, the number of non-unique employees contacted by the conversational agent decreases as the decay of knowledge transmission increases. The same decrease is observed for the number of hubs within the organization. These observations suggests that knowledge is more evenly distributed within the companies’ structure, which aligns with our expectations. There does not appear to be a clear relationship between changes in the alpha value and the minimum distance to patient zero. However, patient zero becomes less likely to be reached as the alpha value increases and the agent has more options to explore before reaching the employee holding all the information about a table. Additionally, the number of hubs decreases with an increase in alpha. As expected, the average path length and the distance to “patient zero” increase as the maximum hierarchy depth increases. This indicates that the agent needs to contact more employees to retrieve the desired knowledge. Additionally, the overall description score decreases slightly with an increase in maximum hierarchy depth, hinting at knowledge fragmentation within the company structure. On the other hand, the score values remain relatively stable despite changes in the number of employees, highlighting the robustness of the description extraction procedure regardless of company size. We likewise observe that a higher Number of Informal Connections (NIC) corresponds to a higher self-critical score obtained at the end of the simulations. Additionally, as the NIC increases in the simulated company structure, the number of conversational hubs and the overall conversation path length decrease. Similarly, the minimum distance between the starting employee and patient zero decreases as the number of connections increases. These results are expected since a higher NIC increases the likelihood of quickly reaching employees who hold specific information about the data at hand. The number of times the employee with all the information is contacted decreases with a higher NIC, suggesting that information is more likely to be distributed among employees when they are more interconnected. However, this observation, constrained by our simulation setup, only accounts for the target knowledge sought by the conversational agent. In real-world scenarios, informal connections are more likely to imply shared informal knowledge rather than business knowledge. Apart from a slight decrease in score quality as the number of features increases, varying the number of features does not strongly correlate with other experimental parameters. This is expected, as these parameters primarily influence the companies’ structure rather than the knowledge retrieval task. After analyzing the simulated conversations through which the agents reconstructed the original knowledge, we also evaluated the quality of the reproduced table descriptions that were generated at the end of each experiment run. This helps us to consider whether the proposed methodology can effectively achieve its desired goal of recovering the disseminated knowledge. For that purpose, we compare the final description $k_{t}$ generated by the agent with the original knowledge description $k^{*}$ for that table according to the following metrics: Full-knowledge recall We first checked if the agent was able to generate a table description that included a mention of every column originally disseminated in the company’s network. An agent is considered to have achieved full-knowledge recall in an experiment run if it succeeded in at least reproducing all table columns in its final report. METEOR We also measure the METEOR score [29] of the descriptions for each column in the table. The score creates word alignments between the candidate and reference texts, using not only exact matches, but also stems and WordNet synonyms and calculates the harmonic combination of precision and recall for those alignments. The METEOR scores for all columns are averaged to arrive at an overall score cMETEOR for the generated description $k_{t}$ . G-Eval We then used an LLM-as-judge approach to better capture the semantic equivalency between the original and generated descriptions. For that, we apply the G-Eval LLM evaluation framework [30] to score each column description according to prompted definitions of textual quality. The G-Eval framework uses LLMs with Chain-of-Thought to fill out a form and generate a final score between 1 and 5 according to the given definition. The G-Eval scores for all columns are averaged to arrive at an overall score for the generated description $k_{t}$ , which we refer to as cGEvalCoh, for a definition of “coherence”, and cGEvalFaith, for a definition of “faithfulness”. Self-critical with context Finally, to directly compare how having access to the official table $k^{*}$ can change the agent’s own evaluation, we altered the self-critic LLM prompts to give the evaluation score for the same final description $k_{t}$ , but now having access to the original description as context. From the 864 simulations executed, the LLM agent achieved full-knowledge recall in 820 of them (94.9%). As such, in the vast majority of cases, the agent at a minimum retained some mention of every column present in the original table $k^{*}$ . In the remaining 44 cases, the final description $k_{t}$ retained $\sim 77\%$ of the expected columns on average. The agent-generated descriptions measured on average $0.17$ on the column-based METEOR score, $2.65$ for column-based G-Eval Coherence and $4.37$ for G-Eval Faithfulness. In addition, adding the original description to the self-critical evaluation reduced the average score from $7.43$ to $6.75$ . While looking directly at the value of those metrics is insufficient to evaluate the quality of the created texts definitively, we can analyze their values in relation to each other and to additional attributes of the simulation runs. Table II shows the rank correlation between the final score given by the self-critic agent during the simulation and the evaluation metrics based on the original knowledge. We identify a strong correlation between the self-critical score with and without context, indicating that the agent’s own evaluation during the conversation partly maintains its relative ranking once it gains access to the original knowledge, even if the absolute score does get reduced. We also observe that the cMETEOR and cGEvalCoh metrics are strongly correlated, indicating that both the word alignment-based metric and the LLM-as-judge method similarly measure the correspondence between the target and reference texts in this context. cGEvalFaith also showed a moderate correlation with the other evaluation metrics, such as $0.66$ to the score with context. Lastly, we assess the effectiveness of our proposed autonomous agent-based knowledge retrieval process by evaluating how the quality of the obtained descriptions is affected by interacting with patient zero or not. In our simulated setup, once the agent reaches patient zero, the task becomes trivial. Ideally, the agent should be able to retrieve high-quality descriptions from employees who do not hold full knowledge. We use the company simulation parameters discussed in Table I as inputs for dimensionality reduction with the UMAP algorithm [31] and aggregated points over a grid with colors representing either cGEvalFaith or % p0 (averaged over the three repetitions performed for each configuration). The resulting projections are shown side by side in Figure 4. We identify areas with high-quality descriptions where patient zero was never contacted (e.g., the center region of the projected points). Conversely, we also observe areas where patient zero was contacted and the obtained cGEvalFaith was high. These findings, along with the low correlation ( $-0.06$ ) between the compared metrics, reinforce that the effectiveness of the proposed agent does not depend on reaching the employee $p_{0}$ who possesses complete knowledge. TABLE II: Spearman correlation between metric pairs and metrics averaged over the simulations (bottom row). SCS: self-critical score; SCS+C: self-critical score with context. | | cMETEOR | cGEvalCoh | cGEvalFaith | SCS | SCS+C | | --- | --- | --- | --- | --- | --- | | cMETEOR | – | 0.772820 | 0.436230 | 0.263699 | 0.279494 | | cGEvalCoh | 0.772820 | – | 0.565856 | 0.347426 | 0.447342 | | cGEvalFaith | 0.436230 | 0.565856 | – | 0.497946 | 0.665396 | | SCS | 0.263699 | 0.347426 | 0.497946 | – | 0.729141 | | SCS+C | 0.279494 | 0.447342 | 0.665396 | 0.729141 | – | | Mean Overall Score | 0.1741 | 2.6531 | 4.3675 | 7.4317 | 6.7465 | <details> <summary>extracted/6597023/Figures/faithfulness_nolabel.png Details</summary>  ### Visual Description ## Heatmap: Pixelated Radial Pattern ### Overview The image is a heatmap displaying a pixelated radial pattern. The pattern is composed of square pixels of varying shades of red, indicating different values. The heatmap is contained within a square frame. A colorbar on the right side of the image provides a scale for interpreting the color intensities. ### Components/Axes * **Colorbar:** Located on the right side of the image. It ranges from approximately 2.5 (lightest shade of red) to 4.5 (darkest shade of red). The colorbar has tick marks at 2.5, 3.0, 3.5, 4.0, and 4.5. * **Pixel Grid:** The main area of the image contains a grid of square pixels. The pixels are arranged in a roughly circular or radial pattern, with varying densities and intensities of red. ### Detailed Analysis * **Pixel Values:** The pixel values are represented by the shades of red. Darker red pixels correspond to higher values (closer to 4.5), while lighter red pixels correspond to lower values (closer to 2.5). * **Pattern Distribution:** The pattern appears to be a somewhat fragmented radial distribution. There are clusters of higher-value (darker red) pixels in certain areas, while other areas have lower-value (lighter red) pixels or are empty (white). * **Specific Pixel Values (Approximate):** * Darkest Red Pixels: ~4.5 * Medium Red Pixels: ~3.5 * Lightest Red Pixels: ~2.5 ### Key Observations * The heatmap displays a non-continuous, pixelated radial pattern. * The values range from approximately 2.5 to 4.5, as indicated by the colorbar. * The distribution of values is uneven, with clusters of high and low values. ### Interpretation The heatmap likely represents some form of spatial data where the intensity of red corresponds to a specific value at that location. The radial pattern suggests that the data might be related to a circular or spherical phenomenon. The fragmented nature of the pattern could indicate discontinuities or variations in the underlying data. Without additional context, it's difficult to determine the exact meaning of the data, but the heatmap provides a visual representation of the spatial distribution of values within the specified range. </details> (a) <details> <summary>extracted/6597023/Figures/p0_reached_nolabel.png Details</summary>  ### Visual Description ## Heatmap: Radial Distribution ### Overview The image is a heatmap displaying a radial distribution pattern. The data is represented by colored squares, with the intensity of the red color indicating the magnitude of the value. The distribution appears to be concentrated in certain radial directions, forming a somewhat flower-like shape. ### Components/Axes * **X and Y Axes:** The X and Y axes are not explicitly labeled, but they represent a 2D spatial grid. * **Color Scale (Legend):** Located on the right side of the image. * The color scale ranges from white to dark red. * White corresponds to a value of 0.0. * Dark red corresponds to a value of 1.0. * Intermediate values are represented by shades of red, with 0.2, 0.4, 0.6, and 0.8 marked on the scale. ### Detailed Analysis The heatmap consists of a grid of squares, each colored according to its value. The values are distributed in a radial pattern, with higher values (darker red) concentrated in certain directions. * **Radial Distribution:** The distribution is not uniform. There are areas of high concentration (dark red) and areas of low concentration (white or light red). * **Concentration Areas:** There are approximately four main areas of concentration, forming a somewhat flower-like shape. These areas are located roughly at the "corners" of the image. * **Value Range:** The values range from approximately 0.0 (white) to 1.0 (dark red). * **Specific Data Points:** * Several squares have values close to 1.0 (dark red). These are scattered throughout the concentration areas. * Many squares have values close to 0.0 (white), particularly in the areas between the concentration areas. * The remaining squares have intermediate values, ranging from light red to medium red. ### Key Observations * The data is not uniformly distributed. * There are distinct areas of high concentration. * The distribution has a radial pattern. ### Interpretation The heatmap visualizes a radial distribution of data, where the intensity of the red color represents the magnitude of the value. The pattern suggests that the underlying phenomenon being represented is concentrated in certain radial directions, forming a somewhat flower-like shape. The areas of high concentration (dark red) indicate regions where the phenomenon is most prominent, while the areas of low concentration (white) indicate regions where it is less prominent. The specific meaning of this distribution would depend on the context of the data. </details> (b) Figure 4: UMAP comparing the quality of the obtained table descriptions against the dependency on contacting patient zero. VI Conclusion In this paper, we propose a novel approach for tacit knowledge retrieval using LLM-based agents within organizational environments. We map the problem as a task to reconstruct database table descriptions that have been disseminated through a company’s employee network. Our approach allows the agent to navigate through the company graph, engaging in natural language conversations with employees to gradually accumulate partial information of the desired table and gain context to help direct its next course of action. To validate our proposed method, we explore a broad simulated setup that encompasses a diverse range of companies structures with different generated table descriptions. Our empirical findings show that the proposed approach is robust and effective in retrieving tacit knowledge spread within the hierarchy of the simulated companies. By evaluating the reference-free self-critical scores used by our agent during its exploration process, we observe that these scores exhibit similarities to the reference-based evaluation metrics considered in our setup. We also identify that the agent is often able to retrieve the full table description without ever directly contacting the employee that was the source of the disseminated knowledge, achieving a recall rate of $94.9\%$ . These results showcase the robustness of our approach and its ability to reconstruct tacit knowledge through automated conversational interactions. For future work, we are implementing our approach at Kunumi, letting the agent interact with real employees in the company network. 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