CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation

CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation Yaoning Yu

Kai-Min Chang

Ye Yu

University of Illinois Urbana-Champaign United States

U.S. Bank United States

University of Illinois Urbana-Champaign United States

Yi-Chia Wang

Haojing Luo

Haohan Wang

Stanford University United States

Starc.Institute United States

University of Illinois Urbana-Champaign United States

arXiv:2608.09790v1 [cs.AI] 10 Aug 2026

Abstract

can examine how changes to product design, reward structures, fee policies, or information presentation influence the perspectives expressed during consumer discussions without requiring large-scale real-world interventions. Existing financial simulators mainly model borrowing behavior, trading decisions, and market dynamics [6, 7, 9, 18]. They do not generate the consumer discussion threads through which financial products are explained, questioned, and compared. Moreover, existing social simulators generate general online interactions or profile-conditioned discussion threads [21, 28, 30]. They do not model how consumers communicate about credit card products by exchanging recommendations, sharing personal experience, and discussing practical trade-offs [29]. We present CARD, a framework for simulating realistic credit card discussions. Rather than prescribing the specific content of each comment, CARD models how consumers communicate about credit card products. The planner organizes discussion structure and communication roles, the writer generates discussion content conditioned on the seed post and local discussion context, and the metricguided revision loop calibrates the generated discussion collection toward the communication patterns observed in real consumer communities. Contributions. This paper makes the following contributions: • We formulate consumer credit card discussion simulation and define realism through thread-level communication patterns observed in real consumer communities. • We propose CARD, a framework that models financial discussion structure, communication roles, reasoning patterns, and productspecific issue coverage to generate realistic consumer credit card discussion threads. • We evaluate CARD on real Reddit credit card discussion threads and show that it produces discussion thread distributions that are consistently closer to real consumer discussion threads than existing social simulation baselines.

Online credit card discussions provide a natural setting for studying how consumers communicate about financial products. Simulating these discussions requires more than just generating individual comments, the generated threads should also match how real users express themselves and interact with others. We introduce CARD, a framework for generating realistic credit card discussion threads. Given a credit card post and its matched real thread, CARD uses non-verbatim guidance on reply structure, comment function, stance, tone, and conversational variation. A planner organizes these controls, a writer generates the discussion, and a calibration loop updates comments’ populations that contribute to differences between the generated and real thread distributions. We evaluate CARD on real Reddit credit card discussions using lexical, semantic, behavioral, and structural metrics. CARD matches the distributions of real credit card discussions better than simulation baselines across multiple LLMs and also demonstrates smaller effect sizes and distribution distances across metrics. These results show that structured planning and targeted revision can generate the realism of simulated credit card discussions.

CCS Concepts • Computing methodologies → Multi-agent systems; Natural language generation.

1

Introduction

Credit card decisions depend on more than product terms and fees because consumers also rely on information shared by other users when they compare products, evaluate rewards, and interpret credit card benefits [2, 15–17, 24]. Online communities have become an important source of this information where consumers ask questions, share personal experiences, compare card features, explain product rules, and correct financial claims [27]. These discussions reveal how consumers interpret, compare, and reason about credit card products in practice. Unlike product disclosures, they expose practical experiences, alternative recommendations, and consumerspecific trade-offs that help consumers understand the same credit card product from different perspectives. Realistic simulation of these discussions provides a controlled environment for studying how consumers interpret and communicate about credit card products from different perspectives. Researchers

2 Related Work 2.1 Financial Simulation and Consumer Finance Agent-based simulation has long been used in finance to study how heterogeneous individual decisions aggregate into marketlevel outcomes. ABIDES provides a high-fidelity discrete-event environment for financial-market simulation, while ABIDES-Gym extends this environment to reinforcement-learning research [3, 6].

Preprint. Under review. 1

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Trovato et al.

ABIDES-Economist broadens this line of work beyond market microstructure by modeling interactions among households, firms, a government, and a central bank [7]. More recent studies incorporate large language model (LLM) agents to simulate trading decisions, communication, market responses, and behavioral consistency with established financial theories [4, 9, 18, 19]. Within consumer credit, agent-based models have also been used to study credit card promotion strategies and heterogeneous customer responses [12]. A broad consumer-finance literature shows that credit card behavior is shaped by regulation, contract design, rewards, information presentation, and behavioral biases. The Credit Card Accountability Responsibility and Disclosure (CARD) Act examines how restrictions on fees and repricing, together with changes to statement disclosures, affected consumer borrowing costs, repayment information, and access to credit [2]. Studies of minimum-payment disclosures show that salient payment anchors can influence repayment amounts and slow debt reduction, while alternative information designs and repayment cues can change these choices [11, 13, 14, 17]. Other work finds that rewards and introductory product features can affect spending, borrowing, and product selection, and that issuers may design or target contracts around differences in consumer sophistication and behavioral bias [1, 5, 24]. Consumers also frequently depart from cost-minimizing repayment and borrowing strategies across multiple cards, including borrowing on highercost cards and allocating repayments according to balances rather than interest rates [10, 23, 26]. Together, these studies show that credit card behavior is heterogeneous, context-dependent, and shaped by social and informational cues rather than product terms alone. Existing simulators, however, focus on decisions and market outcomes rather than the discussions through which consumers form these decisions. CARD targets this discussion layer directly: it simulates the online conversations in which credit card advice, experience, and disagreement are exchanged.

2.2

[29]. Our work builds on this distributional view and develops a generation framework specifically for credit card discussions, where realism requires communication patterns that reflect credit card discussions.

3

Methodology

CARD models how consumers communicate about credit card products, rather than prescribing the specific content of individual comments, through three stages. The discussion planner specifies how the discussion should develop and assigns a financial role to each comment. The writer then generates comments that follow these roles and discuss the planned credit card issues, claims, rules, and trade-offs. The self-looped calibrator then revises all the generated discussion thread collection toward the linguistic, behavioral, and structural patterns observed in real credit card communities.

3.1

Task Formulation

CARD represents an online credit card discussion as a discussion thread rooted at a credit card seed post. We use discussion to refer to the complete thread, including the seed post, comments, and reply relations. A post 𝑝 is the root item that starts a discussion: 𝑝 = (𝑝 title, 𝑝 body, 𝑝 prod, 𝑝 comm ). A comment 𝑐𝑖 is a response either to the root post or to another comment. A discussion thread contains the root post and all associated comments and reply relations:   𝑛𝑝 𝑇𝑝 = 𝑝, {(𝑐𝑖 , 𝑟𝑖 )}𝑖=1 , where 𝑟𝑖 identifies the parent of comment 𝑐𝑖 , either the root post or an earlier comment. For a collection of seed posts P, CARD generates a collection of threads T̂ = {𝑇ˆ𝑝 }𝑝 ∈ P . The seed post defines the credit card product, consumer question, and financial context. CARD generates the remaining discussion, including the reply structure, number of comments, and comment text. At the discussion level, CARD controls the number of comments, reply depth, and branch structure. At the comment level, it controls each comment’s financial discussion function, credit card issue type, financial reasoning move, claim stance, tone, length, and opening style. Across the collection of discussions, CARD aims to reproduce the population-level communication patterns of real credit card communities, including lexical and semantic variation, disagreement, tone, personal stories, emotion, and reply structure.

LLM-Based Social Simulation and Realism Evaluation

LLM agents have been used to simulate individual behavior, interpersonal interaction, and large-scale online communities. Generative Agents studies believable behavior in a small simulated community [21], S3 simulates emotion, attitude, and interaction dynamics over social networks [8], SOTOPIA evaluates agents in goal-oriented social interactions [31], OASIS models social-media activity involving user networks, content, actions, and recommendation mechanisms [28], and AgentSociety simulates societal dynamics with over ten thousand LLM-driven agents [22]. Closer to our setting, SynthPAI generates Reddit-style comment threads from profile-conditioned LLM agents [30]. Surveys of this area organize existing systems across individual-, scenario-, and society-level simulation [20]. A central challenge is whether these simulations reproduce the aggregate properties of real human communities rather than merely generating plausible individual responses. MiroBench evaluates this form of realism by comparing generated and real Reddit discussion threads across linguistic, behavioral, and structural distributions

3.2

Distributional Matching Objective

Let M = {𝜙 1, 𝜙 2, . . . , 𝜙 𝐾 } denote the set of 𝐾 evaluation metrics. For a discussion thread 𝑇𝑝 , let 𝜙𝑘 (𝑇𝑝 ) denote the value of the 𝑘-th metric, where 𝑘 ∈ {1, . . . , 𝐾 }. The metrics cover linguistic, behavioral, and structural aspects, including lexical repetition, semantic variation, comment length, reply depth, disagreement, tone, story content, and emotion. Their full definitions are given in Section 4.2. For a discussion collection T , define the empirical metric distribution Φ𝑘 (T ) = {𝜙𝑘 (𝑇 ) : 𝑇 ∈ T }. Given a generated discussion thread collection T̂ and its matched real thread collection T real , CARD generates discussions that reduce the discrepancy between Φ𝑘 ( T̂ ) and Φ𝑘 (T real ) for each metric 𝜙𝑘 ∈ M. 2

CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation

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Figure 1: Overview of CARD. Given a credit card seed post and its matched real discussion. The planner controls reply structure, financial communication functions, credit card issue types, financial claim stances, and reasoning about benefits, costs, rules, and trade-offs. The writer follows this plan and a partial view of previous comments to generate a new comment. The self-looped population revisor then improves the generated discussion collection through four steps: (a) the metric evaluator compares the generated and real collections across distributional metrics; (b) the controller selects comments that contribute to the current mismatch; (c) the reviser generates best-of-𝑁 candidate rewrites; and (d) the metric gate accepts candidates that improve the target metric while preserving the planned financial role of each comment. The revision loop stops when the target metric is no longer significantly different from the real distribution or when the maximum number of iterations is reached. Table 1: Credit card discussion controls used by the CARD planner.

Statistical diagnostics. We evaluate distributional alignment between generated and real discussions using the Mann–Whitney U and Kolmogorov–Smirnov tests, together with Cliff’s 𝛿 and Wasserstein distance. We use these tests as mismatch diagnostics.

3.3

Symbol Control

Framework Overview

For each credit card seed post, CARD retrieves one matched real with the same about the same financial scenario. The matched discussion provides non-verbatim guidance for how consumers organize financial advice, product explanations, claim corrections, and trade-off reasoning. It does not provide exact text to copy. The overall process is   𝐴𝑝 = 𝑃 (𝑝,𝑇𝑝real ), 𝑇˜𝑝 = 𝑊 (𝑝, 𝐴𝑝 ), 𝑇ˆ𝑝 = 𝑅 𝑇˜𝑝 , 𝐸 (𝑇˜𝑝 ,𝑇𝑝real ) . Here, 𝑃 builds an abstract discussion plan 𝐴𝑝 from the seed post 𝑝 and matched real discussion 𝑇𝑝real . The plan 𝐴𝑝 specifies discussion structure and the financial role of each comment. The writer 𝑊 generates an initial discussion 𝑇˜𝑝 that follows this plan based on the seed post 𝑝. The evaluator 𝐸 computes discussion-level metric values, and the reviser 𝑅 calibrates the generated discussion collection toward the communication patterns of real credit card communities. Figure 1 presents an overview of the framework.

Purpose

𝑟𝑖 𝑑𝑖 ℓ𝑖 𝑓𝑖 𝑠𝑖

Parent Depth Length Financial discussion function Financial claim stance

𝑚𝑖

Financial reasoning move

𝑞𝑖

Financial issue type

𝜏𝑖 𝑜𝑖

Tone Opening style

Places the comment in the reply tree Controls its structural position Controls the approximate comment length Sets the financial role of the comment Controls support, qualification, or disagreement with a claim Specifies how the comment develops a financial issue or claim Identifies the financial aspect of the ongoing discussion to which the comment responds Controls how the comment is expressed Reduces repeated comment openings

number of comments, reply depth, branch structure, and the balance between top-level and nested replies. These quantities are derived from the matched real discussion and adjusted by global generation parameters, such as comment count and discussion size. Comment level. For each planned comment, CARD specifies its communication role before text generation. The abstract plan is ˆ 𝐴𝑝 = {𝑎𝑖 }𝑛𝑖=1 ,

where 𝑛ˆ is the target number of generated comments. Each comment plan is represented as

3.3.1 Discussion planner. The planner models recurring communication behaviors in credit card discussions before any comment is generated. These behaviors include requesting product advice, recommending or rejecting a card, explaining reward and eligibility rules, qualifying financial claims, comparing fees and benefits, and introducing practical trade-offs. The planner represents these behaviors through discussion-level and comment-level controls. Discussion level. At the discussion level, CARD plans how a credit card conversation develops through questions, recommendations, explanations, corrections, and follow-up replies. It controls the

𝑎𝑖 = (𝑟𝑖 , 𝑑𝑖 , ℓ𝑖 , 𝑓𝑖 , 𝑠𝑖 , 𝑚𝑖 , 𝑞𝑖 , 𝜏𝑖 , 𝑜𝑖 ) . Together, these controls define the role of each comment in the credit card discussion. A comment may request advice, recommend a product, explain a reward or eligibility rule, qualify a product claim, correct financial advice, share product experience, or introduce a cost–benefit trade-off. The financial issue type 𝑞𝑖 identifies the credit card issue addressed by the comment, such as rewards, fees, eligibility, approval, product value, or card usage. The financial 3

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Running Example: Writer Prompt

reasoning move 𝑚𝑖 specifies how the comment develops that issue, for example by comparing alternatives, explaining a rule, adding a practical constraint, or challenging an earlier claim. These controls specify how each comment participates in the discussion rather than what it should say. The specific content is generated by the writer conditioned on the seed post and the local discussion context. Table 1 summarizes this control set.

Write a comment based on the planned instruction and previous comments. Planned instruction: skeptical correction; casual tone; qualify the parent claim; discuss the pickup-markup trade-off... ... Visible context: [a partial view of earlier comments discussing pickup prices] Writing instructions: write a natural Reddit-style reply, do not copy previous comments, and reply directly to the parent.

Running Example: Planner Output Thread structure: 37 comments; 12 top-level replies; maximum depth 4. Planned comment: Function: Skeptical correction with a practical caveat. Issue: Practical value of the DoorDash credit. Reasoning move: Introduce fees and markup as a trade-off. Stance: Qualify the parent claim. Tone: Casual and skeptical. Opening: “Sure, if you ignore the delivery fees . . . ”

3.3.3 Self-Looped population calibrator. Individual comments may follow their planned financial roles while the overall discussion collection still differs from real credit card communities. CARD therefore applies a self-looped calibration stage to calibrate collection-level communication patterns after all the discussion threads are generated. It improves one target metric at a time while treating all other metrics as protected. It also preserves the credit card discussion plan, such as the seed post, reply tree, financial discussion function, financial claim stance, and financial issue type. This prevents a metric-oriented rewrite from changing a recommendation into an explanation, reversing a financial claim, or dropping a cost–benefit trade-off. The revision loop continues until neither statistical test detects a significant difference for the target metric or the maximum number of revision iterations is reached. Round diagnosis and protection policy. Each revision round starts by comparing the current generated thread collection with the matched real thread collection using the metric families in Section 4.2. If statistical tests detect a significant difference between the generated and real distributions for a metric, the controller selects that metric as the target metric 𝜙𝑡 . All other metrics form the protected set P𝑡 = M \ {𝜙𝑡 }.

3.3.2 Writer. The writer generates each comment that executes the planned communication role while remaining grounded in the ongoing credit card discussion initiated by the seed post. For the 𝑖-th comment, the writer produces
𝑐𝑖 ∼ 𝑃𝜃 𝑐𝑖 | 𝑝, 𝑎𝑖 , 𝑐𝑟𝑖 , 𝐻𝑖 , where 𝑎𝑖 is the comment plan, 𝑐𝑟𝑖 is the generated parent comment when applicable, and 𝐻𝑖 is the visible portion of the previously generated comments in the same discussion. The writer receives the seed post, its credit card product context, the comment plan, the generated parent comment, and selected previous comments. It then generates a new comment that performs the planned financial role. For example, the comment may recommend a card, explain a reward rule, compare fees and benefits, qualify a claim, or introduce a spending constraint. Since comments are generated in reply order, later comments can extend, qualify, correct, question, support, or challenge earlier financial claims. Prompt controls. Each field in 𝑎𝑖 is prompted into an explicit writer instruction. The instruction specifies different aspects such as the target length, financial claim stance, tone, financial issue type, and financial reasoning move. These controls determine both what financial issue the comment addresses and how it develops the discussion. The model then generates new comments conditioned on the seed post and local discussion context. Context dropout and variation. Consumers rarely read an entire discussion before replying. CARD therefore varies the visible discussion history during generation. Context dropout removes part of the previously generated discussion from the current writer’s view, while context variation changes which earlier comments are emphasized in the prompt. The seed post and the direct parent comment remain available so that the generated comment continues to address the current credit card discussion. This design is inspired by dropout in neural network training, where randomly omitting part of the available representation reduces co-adaptation and encourages more robust behavior [25]. In CARD, exposing each generated comment to a partial and slightly different view of the discussion encourages varied responses and reduces repeated semantic patterns. It also reflects online discussion behavior, where participants do not attend to every earlier comment equally.

The round is allowed to reduce the mismatch in 𝜙𝑡 , while the metrics in P𝑡 must remain within tolerance. The controller also identifies where the generated–real gap occurs, such as the upper tail or middle mass, and selects the revision operations for the round. Running Example: Round Diagnosis Metric Self-BLEU Semantic cosine similarity Disagreement rate Politeness rate Comment length Comment depth Emotion entropy Target metric: Self-BLEU. Protected metrics: All remaining metrics.

Result ✗ ✓ ✓ ✓ ✓ ✓ ✓

Thread and comment selection. Given a target metric 𝜙𝑡 , CARD first computes a target-metric gap for each generated thread. For a generated thread 𝑇𝑝 and its matched real thread 𝑇𝑝real , the gap is 𝑔𝑡 (𝑇𝑝 ) = 𝜙𝑡 (𝑇𝑝 ) − 𝜙𝑡 (𝑇𝑝real ) . Threads with larger 𝑔𝑡 (𝑇𝑝 ) are selected first because they contribute more to the current mismatch. Within each selected thread, CARD 4

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where 𝑇 (𝑚) is the current thread before the edit. A positive 𝑅𝑡 (𝑐𝑖′ ) indicates that the candidate reduces the mismatch on the target metric. A candidate is locally accepted when

ranks comments by how strongly they contribute to the target metric. For example, in Self-BLEU, comment contribution is estimated from within-thread lexical overlap. CARD compares the target comment with other comments in the same thread, counts shared repeated 𝑛-grams, and detects repeated opener and connector patterns. Comments with larger contribution to the gap are revised first.

𝑅𝑡 (𝑐𝑖′ ) > 𝜖𝑡 ,

Preserve(𝑎𝑖 , 𝑐𝑖′ ) = 1,

where 𝜖𝑡 is the minimum required reduction in the target metric gap. The preservation check requires the rewrite to retain the planned comment attributes in 𝑎𝑖 , including the financial discussion function, claim stance, issue type, reasoning move, approximate length, and reply target. This check preserves the planned financial role and credit card content of the comment. CARD may also apply metric-specific local gates. For example, the Self-BLEU reviser checks whether the rewritten comment reduces its lexical overlap with other comments in the same discussion. Among locally accepted candidates, CARD selects the rewrite with the largest target-gap reduction. If no candidate satisfies the acceptance conditions, the original comment is left unchanged.

Running Example: Thread and Comment Selection ✓ Is the DoorDash credit basically just $5 off after fees and markup? ✓ Existing DoorDash users only. ✗ Thanks, yeah, pickup usually stretches it further. I’d stick to low-markup spots. ✓ Just a clearing delay, not a card glitch. The third comment is selected because it contributes most to the SelfBLEU mismatch.

Candidate generation. For each selected comment, CARD first samples candidate rewrites using a predefined set of metric-specific revision operations. The controller selects these operations based on the current target metric and the detected failure pattern. If the predefined operations do not produce an effective revision, the controller asks an LLM to generate additional revision instructions. These instructions are conditioned on the current failure pattern and the revision history, including previously used instructions and their outcomes. This allows the controller to avoid repeating ineffective edits and to propose a more targeted revision strategy for the current comment. If the controller selects 𝑀 operations and samples N candidates for each operation, CARD evaluates 𝑀 × 𝑁 candidate rewrites for that comment: n o 𝑀,𝑁 C𝑖 = 𝑐𝑖′(𝑚,𝑛) .

Running Example: Candidate Acceptance Candidate 1 Yeah, pickup is still expensive. Use cheaper places. ✗ Self-BLEU ✗ Semantic cosine ✓ Disagreement ✓ Politeness ✓ Length ✗ Emotion Rejected

Candidate 2 Since pickup often raises the price, I’d stick to restaurants with low markups. ✓ Self-BLEU ✓ Semantic cosine ✓ Disagreement ✓ Politeness ✓ Length ✓ Emotion Accepted

Applying candidate edits. Each accepted rewrite replaces the original comment at the same position in its thread. The root post, parent relation, and all unrevised comments remain unchanged. After all selected comments have been processed, the updated threads (𝑟 +1) . form a proposed thread collection for the current round: T̂prop CARD then recomputes every evaluation metric over the complete proposed collection. Local candidate scores therefore determine which edits are proposed, while collection-level statistics determine whether the entire revision round is retained. Round-level acceptance and rollback. CARD maintains the most recent accepted thread collection T̂ (𝑟 ) . In round 𝑟 + 1, the reviser (𝑟 +1) produces a proposed collection T̂prop . CARD then rescores the complete proposed collection and compares its metric quality with that of the most recent accepted collection. The proposed round is accepted only if it improves the target metric and does not cause any protected metric to degrade beyond tolerance. If accepted, the proposed collection becomes the input to the next round; otherwise, CARD rolls back to the most recent accepted collection: ( (𝑟 +1) T̂prop , if the round is accepted, T̂ (𝑟 +1) = T̂ (𝑟 ) , otherwise.

𝑚=1,𝑛=1

The candidate prompt includes the target comment, its parent comment, selected earlier comments, and the planned financial discussion function, claim stance, issue type, and reasoning move. The rewrite must preserve the credit card content needed by that role, such as a product recommendation, reward rule, eligibility condition, fee comparison, or spending trade-off. Running Example: Candidate Generation Target comment: Thanks, yeah, pickup usually stretches it further. I’d stick to low-markup spots. 1. Revise opener (1) Yeah, pickup usually costs more. I’d stick to low-markup spots. ... 2. Change wording (1) Pickup usually adds even more. I’d go with low-markup places. ... 3. Merge sentences (1) Since pickup usually costs more, I’d stick to low-markup spots. ...

For each target metric, the revision loop continues until either no significant difference is detected or the maximum of seven revision iterations is reached. The controller then selects the next unfinished mismatch metric and start from round diagnosis and protection policy through the self-revision loop. The overall revision process

Candidate scoring and acceptance. For each candidate rewrite 𝑐𝑖′ ∈ C𝑖 , CARD forms a candidate thread 𝑇𝑖′ by replacing only 𝑐𝑖 with 𝑐𝑖′ . The candidate receives a target-gap reduction score: 𝑅𝑡 (𝑐𝑖′ ) = 𝑔𝑡 (𝑇 (𝑚) ) − 𝑔𝑡 (𝑇𝑖′ ), 5

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terminates only after every mismatch metric has either passed the statistical test or exhausted its revision budget. CARD finally returns the most recently accepted collection.

4 Experiment 4.1 Baselines We compare CARD with OASIS and SynthPAI, two LLM-based frameworks that support social simulation, across four LLMs. OASIS [28] is a general and scalable social-media simulator that models user profiles, social networks, user actions, and recommendation mechanisms. It is designed to study large-scale social phenomena, including information diffusion, group polarization, and herd behavior. SynthPAI [30] uses profile-conditioned LLM agents to generate Reddit-style comments for personal attribute inference.

4.2

Figure 2: Distributional agreement with real credit-card discussion threads.Lower values indicate closer agreement. Real denotes the real-vs.-real reference averaged over 10 repeated 150-vs.-150 samplings; each method row reports the average distance across four backbone models.

Evaluation Metrics

We follow the MiroBench [29] credit-card thread-level evaluation protocol. All methods are evaluated on the same set of 150 seed posts, with each method generating one discussion thread for each seed post. Each generated thread is paired with its matched real Reddit discussion, and we compute the same set of metrics for all generated-real pairs.We then compare each metric distribution across the generated and real thread collections. Table 5 summarizes what each metric measures. Higher Self-BLEU, Self-BERTScore, and mean semantic cosine values generally indicate greater lexical or semantic similarity among comments. For the remaining metrics, there is no fixed direction; the goal is to match the corresponding real distribution.

means that the test does not detect a statistically significant difference at 𝛼 = 0.05; it does not establish that the two distributions are equivalent. We also report the absolute Cliff’s 𝛿 and Wasserstein distance as measures of the magnitude of the distributional difference, where smaller values indicate better matching.

4.3

Statistical results. Since CARD uses statistical tests to guide selfloop revision, we first evaluate whether the generated distributions become statistically indistinguishable from the real distributions. Table 2 reports the Mann–Whitney U and Kolmogorov–Smirnov test results. CARD with gpt-5.4-mini and deepseek-v4-flash achieves the highest non-rejection rate, with 91.7% of the metric– test pairs having 𝑝 > 0.05. In comparison, the strongest SynthPAI configuration reaches 37.5%, while OASIS remains at or below 4.2%. CARD with gpt-5.4-mini passes both statistical tests on most lexical, semantic, behavioral, and structural metrics, leaving politeness as Passing a statistical test, however, does not indicate how close two distributions actually are. We therefore also report effect sizes and distribution distances to measure the magnitude of the remaining difference. Distributional distance. Figure 2 complements the statistical tests by showing the remaining distributional differences measured by absolute Cliff’s 𝛿 and Wasserstein distance. Across almost all metrics, CARD consistently produces smaller effect sizes and shorter distributional distances than OASIS and SynthPAI. The improvements are particularly clear for lexical diversity, semantic variation, discussion structure, disagreement, story content, and emotion. These results show that CARD not only satisfies the statistical criteria more frequently, but also generates discussion distributions that are quantitatively closer to real credit card discussions. Human evaluation. We further evaluated discussion realism using a blind turing test. For each comparison, annotators were shown one real Reddit discussion and one generated discussion in random order and asked to identify which discussion was written by humans, with a third option indicating that the two discussions

Table 5: Thread-level metrics used to compare generated and real credit card discussion threads. Metric

What it measures

Lexical and semantic diversity Self-BLEU Lexical repetition among comments in a thread. Self-BERTScore Contextual semantic similarity among comments. Mean semantic cosine Mean embedding similarity among comments. Tone and disagreement Hard disagreement rate Politeness rate Impoliteness rate Neutral rate

Frequency of direct disagreement between comments. Proportion of comments classified as polite. Proportion of comments classified as impolite. Proportion of comments classified as neutral.

Structure and length Length CV Average reply depth Structural virality

Variation in comment length within a thread. Average depth of comments in the reply tree. How widely interactions spread across the reply tree.

Story and emotion Mean story probability Emotion entropy

Presence of personal experience or narrative detail. Variation in emotional tone within a thread.

Results Analysis

Statistical comparison. For each metric, we compare the generated and real thread-level distributions using the Mann–Whitney U test and the Kolmogorov–Smirnov test. The Mann–Whitney U test measures whether one distribution tends to have larger values than the other, while the Kolmogorov–Smirnov test measures differences in the overall distribution shape. A result with 𝑝 − 𝑣𝑎𝑙𝑢𝑒 > 0.05 6

CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation

Method

Self-BLEU

Model

MWU

Self-BERT

KS

MWU

KS

Sem. Cos. MWU

KS

Hard Dis. MWU

KS

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Polite MWU

Impolite KS

MWU

KS

Neutral MWU

KS

Len. CV MWU

KS

Depth MWU

KS

Virality MWU

KS

Story MWU

Emotion KS

MWU

KS

Non-rej. (%)

OASIS

gemini-2.5-flash < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 deepseek-v4-flash < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.037 0.073 gpt-4o-mini < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.048 0.053 gpt-5.4-mini < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.039 0.020 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.0 4.2 4.2 0.0

SynthPAI

gemini-2.5-flash < 0.001 < 0.001 < 0.001 < 0.001 0.186 0.073 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.054 0.098 < 0.001 < 0.001 0.102 0.004 0.197 0.013 < 0.001 < 0.001 < 0.001 < 0.001 deepseek-v4-flash < 0.001 < 0.001 < 0.001 < 0.001 0.034 0.169 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.495 0.129 0.868 0.073 0.581 0.073 0.260 0.073 < 0.001 < 0.001 gpt-4o-mini < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.006 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.896 0.073 0.881 0.039 < 0.001 < 0.001 0.731 0.129 gpt-5.4-mini < 0.001 < 0.001 < 0.001 < 0.001 0.858 0.276 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.002 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

25.0 37.5 20.8 8.3

CARD

gemini-2.5-flash deepseek-v4-flash gpt-4o-mini gpt-5.4-mini

0.159 0.381 0.020 0.307

0.059 < 0.001 < 0.001 0.106 0.973 0.725 0.011 < 0.001 < 0.001 0.139 0.323 0.362

0.917 0.266 0.014 0.373

0.443 0.443 0.015 0.532

0.233 0.458 0.024 0.848

0.058 0.725 0.001 0.997

0.508 0.245 0.344 0.006

0.725 < 0.001 < 0.001 0.817 0.489 0.817 0.231 0.081 0.231 0.031 0.770 0.997

0.361 0.805 0.003 0.185

0.532 0.951 0.022 0.725

0.392 0.537 0.028 0.393

0.817 0.628 0.005 0.291

0.094 0.580 0.079 0.258

0.291 0.951 0.180 0.816

0.053 0.452 0.057 0.134

0.291 0.894 0.139 0.532

0.979 0.019 0.149 0.260

0.984 0.063 0.180 0.011 0.792 0.725 0.291 < 0.001 < 0.001 0.291 0.892 0.816

83.3 91.7 41.7 91.7

Table 2: Metric-level statistical comparison across methods and models. Each metric reports Mann–Whitney U and Kolmogorov– Smirnov 𝑝-values. Values with 𝑝 > 0.05 are bolded, indicating that the corresponding test does not detect a statistically significant difference between the generated and real distributions (higher is better). Non-rej. (%) reports the percentage of metric–test pairs for which the null hypothesis is not rejected at 𝛼 = 0.05 across all 12 metrics.

Method

Model

OASIS

gemini-2.5-flash deepseek-v4-flash gpt-4o-mini gpt-5.4-mini

Comments 2,438 935 1,700 1,176

SynthPAI

gemini-2.5-flash deepseek-v4-flash gpt-4o-mini gpt-5.4-mini

2,175 1,508 1,694 5,060

CARD

gemini-2.5-flash deepseek-v4-flash gpt-4o-mini gpt-5.4-mini

2,351 2,466 2,574 2,674

Figure 3: Results of the blind Turing test over 20 discussion pairs for each method. The blue portion is the percentage of judgments in which the generated discussion was not identified as human-written or indistinguishable from the real discussion (higher is better). The gray portion shows the percentage of judgments in which the generated discussion was correctly identified as LLM-generated.

Table 3: Number of generated comments across methods and models.

could not be distinguished. We compared CARD against OASIS and SynthPAI using 20 matched discussion pairs for each method. Five annotators participated in the study. Each annotator evaluated 36 discussion pairs, for a total of 72 unique discussion pairs across all methods. Figure 3 reports the percentage of judgments in which the generated discussion was not identified as LLM-generated (i.e., judged as real or marked as indistinguishable) and the percentage of correct identifications. CARD achieves the highest proportion of generated discussions that were not identified, substantially outperforming both OASIS and SynthPAI. Effectiveness of self-loop calibration. The statistical improvements above are primarily achieved through CARD’s self-loop revision process. Table 4 shows that self-loop revision substantially improves distributional alignment across revision models. Three of the four LLMs increase the number of metrics that pass the statistical tests, although the degree of improvement varies considerably across models. DeepSeek-V4-Flash provides the most efficient revision behavior. It retains all attempted revision rounds and improves the overall pass count from 6 to 11 metrics while generating only 26,770 candidate rewrites. Compared with gptbased revisers, it requires fewer candidate generations and achieves a higher acceptance rate of selected comments (26.2%), suggesting that it more often produces rewrites that satisfy both target improvement and protection constraints. GPT-5.4-mini also improves the overall pass count from 7 to 11 metrics and achieves the highest edited-thread rate (66.4%) and

accepted-comment rate (35.5%). However, it requires a larger candidate search, generating 89,639 rewrites with 20.7 rewrites per selected comment. This indicates that GPT-5.4-mini often needs more candidate exploration before satisfying the acceptance criteria. GPT-4o-mini generates the largest number of candidate rewrites (121,299), but improves none of the overall passing metrics. Its accepted-comment rate is only 13.5%, and all tone and structure revision rounds fail to be retained.

5

Conclusion

We introduce CARD, a framework for simulating credit card discussion threads that match how consumers communicate and interact in real online communities. CARD combines discussion thread planning, sequential comment generation, and metric-guided revision to control thread structure, discussion functions, tone, disagreement, and other communication patterns without copying the content of matched real threads. Experiments on real Reddit credit card discussion threads show that CARD reduces linguistic, behavioral, and structural distributional gaps compared with general socialsimulation baselines. These results suggest that explicit planning and collection-level revision provide a useful basis for realistic consumer-finance discussion thread simulation. 7

Conference’17, July 2017, Washington, DC, USA

Revision Outcome

Revision Process

Model

gpt-4o-mini gpt-5.4-mini DeepSeek V4 Flash Gemini 2.5 Flash

Trovato et al.

Diversity

7 / 21 (33.3%) 10 / 13 (76.92%) 11 / 11 (100%) 7 / 14 (50.0%)

7 / 7 (100%) 0 / 7 (0.0%) (No revision) 0 / 7 (0.0%) 663 / 1267 (52.3%) 1022 / 7545 (13.5%) 5 / 12 → 5 / 12 (41.7% → 41.7%) 6 / 6 (100%) 4 / 7 (57.1%) (No revision) (No revision) 645 / 971 (66.4%) 1536 / 4331 (35.5%) 7 / 12 → 11 / 12 (58.3% → 91.7%) 1 / 1 (100%) 2 / 2 (100%) 7 / 7 (100%) 1 / 1 (100%) 300 / 580 (51.7%) 456 / 1742 (26.2%) 6 / 12 → 11 / 12 (50.0% → 91.7%) 6 / 7 (85.7%) 1 / 7 (14.3%) (No revision) (No revision) 402 / 737 (54.5%) 579 / 3697 (15.7%) 6 / 12 → 10 / 12 (50.0% → 83.3%)

Tone

Story

Edited Threads

Structure

Accepted Comments

Candidate Rewrites

Total Accepted Rounds

Overall Passes Before → After

Generated Avg. Rewrites Rewrites / Comment 121,299 89,639 26,770 47,046

16.1 20.7 15.4 12.7

Table 4: Summary of CARD’s self-loop revision process. Revision Process reports accepted over attempted revision rounds for each revision stage. Overall Passes compares the number of target metrics satisfying the statistical criteria before and after self-loop revision.

6

Future Work

[15] Harrison Hong, Jeffrey D Kubik, and Jeremy C Stein. 2004. Social interaction and stock-market participation. The journal of finance 59, 1 (2004), 137–163. [16] Harrison Hong, Jeffrey D Kubik, and Jeremy C Stein. 2005. Thy neighbor’s portfolio: Word-of-mouth effects in the holdings and trades of money managers. The Journal of Finance 60, 6 (2005), 2801–2824. [17] Benjamin J Keys and Jialan Wang. 2019. Minimum payments and debt paydown in consumer credit cards. Journal of Financial Economics 131, 3 (2019), 528–548. [18] Nian Li, Chen Gao, Mingyu Li, Yong Li, and Qingmin Liao. 2024. EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 15523–15536. [19] Zeping Li, Guancheng Wan, Keyang Chen, Yu Chen, Yiwen Zhao, Philip Torr, Guangnan Ye, Zhenfei Yin, and Hongfeng Chai. 2026. Behavioral Consistency Validation for LLM Agents: An Analysis of Trading-Style Switching through StockMarket Simulation. In Findings of the Association for Computational Linguistics: ACL 2026. 40356–40370. [20] Xinyi Mou, Xuanwen Ding, Qi He, Liang Wang, Jingcong Liang, Xinnong Zhang, Libo Sun, Jiayu Lin, Jie Zhou, Huang Xuanjing, et al. 2026. From individual to society: A survey on social simulation driven by large language model-based agents. Comput. Surveys 58, 11 (2026), 1–41. [21] Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. 2023. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology. 1–22. [22] Jinghua Piao, Yuwei Yan, Jun Zhang, Nian Li, Junbo Yan, Xiaochong Lan, Zhihong Lu, Zhiheng Zheng, Jing Yi Wang, Di Zhou, et al. 2025. AgentSociety: LargeScale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society. arXiv preprint arXiv:2502.08691 (2025). [23] Alejandro Ponce, Enrique Seira, and Guillermo Zamarripa. 2017. Borrowing on the wrong credit card? Evidence from Mexico. American Economic Review 107, 4 (2017), 1335–1361. [24] Hong Ru and Antoinette Schoar. 2016. Do credit card companies screen for behavioral biases? Technical Report. National Bureau of Economic Research. [25] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929–1958. [26] Victor Stango and Jonathan Zinman. 2016. Borrowing high versus borrowing higher: price dispersion and shopping behavior in the US credit card market. The Review of Financial Studies 29, 4 (2016), 979–1006. [27] Sachin Thukral, Suyash Sangwan, Arnab Chatterjee, Lipika Dey, Aaditya Agrawal, Pramit Kumar Chandra, and Animesh Mukherjee. 2022. Understanding how social discussion platforms like Reddit are influencing financial behavior. In 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 612–619. [28] Ziyi Yang, Zaibin Zhang, Zirui Zheng, Yuxian Jiang, Ziyue Gan, Zhiyu Wang, Zijian Ling, Jinsong Chen, Martz Ma, Bowen Dong, Prateek Gupta, Shuyue Hu, Zhenfei Yin, Guohao Li, Xu Jia, Lijun Wang, Bernard Ghanem, Huchuan Lu, Chaochao Lu, Wanli Ouyang, Yu Qiao, Philip Torr, and Jing Shao. 2024. OASIS: Open Agent Social Interaction Simulations with One Million Agents. arXiv:2411.11581 [cs.CL] https://arxiv.org/abs/2411.11581 [29] Yaoning Yu, Ye Yu, Haojing Luo, and Haohan Wang. 2026. MiroBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions. arXiv preprint arXiv:2606.14715 (2026). [30] Hanna Yukhymenko, Robin Staab, Mark Vero, and Martin Vechev. 2024. A synthetic dataset for personal attribute inference. Advances in Neural Information Processing Systems 37 (2024), 120735–120779. [31] Xuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang, Haofei Yu, Zhengyang Qi, Louis-Philippe Morency, Yonatan Bisk, Daniel Fried, Graham Neubig, and Maarten Sap. 2023. SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents. arXiv:2310.11667 [cs.CL] https://arxiv.org/abs/2310.11667

Future work can extend CARD in two main directions. First, the framework can be adapted to other financial and non-financial discussion domains by defining domain-specific discussion functions and evaluation metrics. Second, the current framework focuses on how people communicate rather than whether generated threads match the specific content of real discussions. Future versions could add content-level objectives, including topic coverage, claim alignment, and factual consistency, while preserving the linguistic, behavioral, and structural realism studied in this work.

References [1] Sumit Agarwal, Sujit Chakravorti, and Anna Lunn. 2010. Why do banks reward their customers to use their credit cards? Technical Report. Working paper. [2] Sumit Agarwal, Souphala Chomsisengphet, Neale Mahoney, and Johannes Stroebel. 2015. Regulating consumer financial products: Evidence from credit cards. The Quarterly Journal of Economics 130, 1 (2015), 111–164. [3] Selim Amrouni, Aymeric Moulin, Jared Vann, Svitlana Vyetrenko, Tucker Balch, and Manuela Veloso. 2021. ABIDES-gym: gym environments for multi-agent discrete event simulation and application to financial markets. In Proceedings of the Second ACM International Conference on AI in Finance. 1–9. [4] Yu Bi, Lingxiao Zhao, Jinyun Tong, Zhe Feng, and Carmine Ventre. 2025. Agentbased Modeling and Simulation of Ambiguity in Catastrophe Insurance Markets. In Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems. 316–324. [5] Leonardo Bursztyn, Bruno Ferman, Stefano Fiorin, Martin Kanz, and Gautam Rao. 2018. Status goods: experimental evidence from platinum credit cards. The Quarterly Journal of Economics 133, 3 (2018), 1561–1595. [6] David Byrd, Maria Hybinette, and Tucker Hybinette Balch. 2019. Abides: Towards high-fidelity market simulation for ai research. arXiv preprint arXiv:1904.12066 (2019). [7] Kshama Dwarakanath, Tucker Balch, and Svitlana Vyetrenko. 2024. ABIDESEconomist: Agent-Based Simulator of Economic Systems with Learning Agents. arXiv preprint arXiv:2402.09563 (2024). [8] Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, and Yong Li. 2023. S3: Social-network Simulation System with Large Language Model-Empowered Agents. arXiv preprint arXiv:2307.14984 (2023). [9] Shen Gao, Yuntao Wen, Minghang Zhu, Jianing Wei, Yuhan Cheng, Qunzi Zhang, and Shuo Shang. 2024. Simulating financial market via large language model based agents. arXiv preprint arXiv:2406.19966 (2024). [10] John Gathergood, Neale Mahoney, Neil Stewart, and Jörg Weber. 2019. How do individuals repay their debt? The balance-matching heuristic. American Economic Review 109, 3 (2019), 844–875. [11] Benedict Guttman-Kenney, Jesse Leary, and Neil Stewart. 2023. Weighing anchor on credit card debt. arXiv preprint arXiv:2305.11375 (2023). [12] Conor Brian Hamill, Raad Khraishi, Simona Gherghel, Jerrard Lawrence, Salvatore Mercuri, Ramin Okhrati, and Greig Alan Cowan. 2025. Agent-based modelling of credit card promotions. International Journal of Bank Marketing 43, 4 (2025), 849–870. [13] Patrick Hendy, Robert Slonim, and Kadir Atalay. 2021. Unsticking credit card repayments from the minimum: Advice, anchors and financial incentives. Journal of Behavioral and Experimental Finance 30 (2021), 100505. [14] Hal E Hershfield and Neal J Roese. 2015. Dual payoff scenario warnings on credit card statements elicit suboptimal payoff decisions. Journal of Consumer Psychology 25, 1 (2015), 15–27. 8

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