Write a comment

SUMMARY

This paper examines bot-driven manipulation of copy trading in Solana meme coin markets, particularly pump.fun and GMGN. The paper makes four main contributions. First, it develops a taxonomy of bundle, sniper, bump, and comment bots and proposes detection algorithms for these categories. Second, it analyzes approximately 6,000 meme coins and finds that position-manipulation bots tend to redistribute project returns rather than reduce them overall, while attention-manipulation bots can inflate apparent performance. Third, it develops a wallet-profitability prediction task using structured features. Fourth, it evaluates a multi-agent system consisting of wallet, coin, and timing agents against LASSO, XGBoost, and neural-network baselines under a bonding-curve execution model.

Theorem 2 is particularly important. Under the pump.fun convex bonding curve, a copier who buys immediately after the wallet being copied pays a higher price for the replicated purchase. This creates an imitation penalty that exists independently of the particular trading strategy. The reported results also show that the multi-agent system is the only evaluated model producing a positive average copier return of approximately 3% after the modeled trading frictions.

STRENGTHS

S1. The imitation penalty analysis in Lemma 1, Theorem 2, and Section A.6 is the strongest part of the paper. The authors take the common observation that copy traders suffer from adverse execution and establish it formally using the pump.fun bonding-curve mechanism. This changes the practical question from simply identifying profitable wallets to identifying wallets whose expected performance is sufficient to overcome the additional execution cost faced by a copier.

S2. The empirical result concerning position-manipulation bots is useful because the authors do not assume that bot activity necessarily reduces overall project performance. Figure 4 suggests that these bots may redistribute gains among market participants rather than simply destroy value. This distinction deserves attention.

S3. The taxonomy in Section 4.2, together with the detection heuristics in Figures 2 and 3, provides a clear description of bundle, sniper, bump, and comment strategies.

S4. The chronological sample construction and the use of thresholds fitted on the training data reduce obvious sources of information leakage. The relationship between the model knowledge cutoff and the evaluation period is also disclosed.

S5. The SHAP-informed condition design provides a useful connection between feature attribution from the statistical models and the conditions used by the multi-agent system.

MAJOR COMMENTS

M1. The detection algorithms require independent validation.

The downstream analysis depends heavily on the accuracy of the bot labels, but the paper does not report validation statistics for the proposed detectors.

Algorithm 1 appears to classify any non-creator purchase in the launch block as bundle-bot activity. On a chain with approximately 400 ms block times and no public mempool, independent automated traders competing for the same launch could also appear in that block. Such transactions could therefore be classified as bundle activity even when there was no coordination with the creator.

A related issue exists in Algorithm 2. The use of K=5 treats activity within approximately two seconds as inconsistent with normal human reaction time. However, automated alerts, pre-signed transactions, and professional trading infrastructure could complicate this assumption.

The paper does not report precision, recall, false-positive rates, or a manually labeled validation sample. This matters because the bot indicators are subsequently used in Table 1, supplied to the coin agent, and incorporated into the broader empirical conclusions.

I recommend manually reviewing a stratified sample of flagged coins and wallets and reporting agreement between independent annotators. Sensitivity analysis for K and the threshold xi would also be useful.

M2. The evaluation does not address adaptation by strategic adversaries.

Section 4.1 describes a strategic adversary, and Section 4.3.5 acknowledges that gradual bundles are more difficult to identify. However, the empirical evaluation uses observed strategies and fixed detection rules.

An adversary who understands the detection system could modify behavior. For example, accumulation could be spread beyond the K-block sniper window. A wallet could also build a favorable historical performance record before using that reputation to attract copy traders. Funding relationships could be obscured through additional intermediary wallets.

For this reason, the reported 3% copier return should be interpreted as an estimate under the evaluated market conditions and adversarial behavior rather than as evidence of performance against an adaptive adversary.

A useful robustness test would introduce controlled errors into the bot labels, such as 10% and 20% label perturbation, and measure the resulting change in multi-agent system performance.

M3. The economic results need more information about the return distribution.

The paper reports an average copier return of approximately 3% per meme coin, but the mean alone is difficult to interpret in a market where a small number of extreme returns can have a large effect on the average.

The authors should also report the median copier return, the percentage of copy events producing positive returns, relevant return quantiles, and measures of drawdown or loss severity.

Figure 8 currently emphasizes averages. Reporting the return distribution would make it possible to determine whether the positive mean represents broad performance or is primarily driven by a relatively small number of highly profitable trades.

The execution model also assumes immediate copying without additional execution delay or priority-fee competition. A sensitivity analysis using realistic execution delays would improve the economic interpretation of the results.

M4. Statistical uncertainty should be reported.

The reported AUC values are 0.7271 for XGBoost, 0.7164 for the multi-agent system, and 0.6795 for the zero-shot version. It is not clear whether the differences between these models are statistically meaningful.

Confidence intervals should be reported for the principal performance measures. Bootstrap comparisons or an appropriate test such as DeLong's test could be used for the AUC comparisons. At minimum, the paper should provide class frequencies and confusion matrices at the selected operating threshold.

The paper should also distinguish clearly between predictive and economic performance. XGBoost has the highest reported AUC, while the multi-agent system may offer advantages under other measures or in the economic simulation. These are different claims and should be presented separately.

M5. Reproducibility, inference cost, and latency need additional discussion.

The multi-agent system depends on GPT-4o, but the paper does not report token usage, approximate inference cost, or decision latency. These factors are important in a copy-trading application because the value of a trading signal may decline rapidly after the original transaction.

The authors should report approximate cost and wall-clock inference time per decision. Testing the approach with another model family would also help determine whether the results depend strongly on the particular model used in the reported experiments.

MINOR COMMENTS

m1. Section 4.1 describes the setting as "near zero-sum." The discussion should account explicitly for the 1% pump.fun transaction fee and relevant bonding-curve migration mechanics or use more qualified wording.

m2. Table 1 combines objective thresholds, such as t-stat > 1.645, with subjective categories such as "organically positive" comments and "no mechanicality" in candlestick behavior. The authors should explain how these subjective assessments were made and, where possible, report agreement or reliability measures.

m3. Comment-bot detection in Section 4.3.4 uses four few-shot examples and then aggregates the classifications at the coin level. The accuracy of the per-comment classifier should be evaluated on a separately labeled sample.

m4. The conditions are derived from Ding et al. [6] and trader annotations, and those conditions are subsequently used to select or structure the reasoning examples in Section 5.1.3. This creates a potential dependence between the authors' prior assumptions and the reasoning process. The paper should acknowledge this design choice explicitly.

m5. The coin agent receives the bot indicators as Boolean inputs in Prompt 3. Errors in the underlying detectors can therefore propagate directly into the agent. An error-injection experiment would help quantify this dependence.

m6. Several presentation issues should be corrected. The term "statistic-driven" is awkward and could be replaced with "feature-based" or "statistical." Figure 7 and Table 3 appear to contain LaTeX conversion artifacts. The phrase "a LLM-powered" should be corrected to "an LLM-powered."

QUESTIONS TO THE AUTHORS

Q1. What proportion of coins identified by Algorithm 1 were manually verified as involving creator-coordinated activity? Can the authors estimate the false-positive rate caused by independent automated traders entering the launch block?

Q2. How does the multi-agent system perform when 10% or 20% of the bot indicators are deliberately perturbed? This would help determine how sensitive the system is to detector error.

Q3. What is the median copier return, and what percentage of copy events produce positive returns at the selected operating threshold?

Q4. What are the approximate inference cost and wall-clock latency for one multi-agent system decision? How does this compare with the time window during which a copy trade remains economically useful?

Q5. The agent weights are optimized using a 15% validation set. What are the fitted weights? Is the final solution reasonably distributed across the three agents, or does one agent account for most of the ensemble decision?

OVERALL ASSESSMENT

The paper makes a useful contribution to the study of copy trading and manipulation in meme coin markets. The theoretical result concerning the imitation penalty is particularly valuable because it identifies an execution disadvantage that follows directly from the bonding-curve mechanism.

The main weaknesses concern validation of the bot detectors, limited reporting of the copier-return distribution, and the absence of statistical uncertainty around the model comparisons.

NOTE

My work includes blockchain-assisted data integrity and reconstruction-based anomaly detection. I therefore focused particularly on the detection methodology, validation of the bot classifications, and the effect of classification errors on the downstream analysis.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.