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REVIEW 3 major objections 4 minor 92 references

Demystifying Solana Bots: From GitHub Blueprints to On-Chain Fingerprints

T0 review · 3 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read Solana bots, built off-chain, leave on-chain execution fingerprints that fall into four distinct regimes, and their codebases share a five-stage operational pipeline.

desk verdict A careful, well-validated empirical mapping of Solana bots from code and on-chain data, with one headline claim — the shared five-stage pipeline — that outruns the evidence. read the letter →

arxiv 2607.28424 v2 pith:YRPVSWNV submitted 2026-07-30 cs.SE

Xiaoye Zheng , Yujing Chen , Minghao Wu , David Lo , Difan Xie , Daoyuan Wu , Xiaohu Yang , Zhiyuan Wan This is my paper
classification cs.SE
keywords SolanablockchainbotsMEVbottaxonomyoperationalpipelineon-chainfingerprintsempiricalsoftwareengineeringtransactionclustering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that Solana bots — off-chain programs that submit transactions on a high-throughput, low-fee blockchain — can be systematically understood from two complementary evidence sources: what their source code implements and what their transactions leave on-chain. Analyzing 586 open-source bot codebases and 200 bot-controlled addresses with over 44 million transactions, it derives a 15-category taxonomy of bot functions, identifies a five-stage pipeline shared across categories, and finds that on-chain execution traces cluster into four regimes separated by submission intensity and execution effectiveness. The four regimes align with two intents: three clusters behave like maximal-extractable-value (MEV) arbitrage bots pivoting on wrapped SOL, and the fourth behaves like an automated trading bot concentrated on a meme-coin launch platform. The study matters because bot activity on Solana's decentralized exchanges exceeds $250 million in daily volume, so knowing what bots implement and how they execute helps auditors, regulators, and bot builders reason about a significant slice of on-chain activity.

What carries the argument

Two abstractions carry the argument. A five-stage pipeline — setup, observation and acquisition, analytics, planning and decision, execution and reporting — abstracts recurring code-level building blocks into a shared backbone. A four-cluster execution landscape, from density-based clustering of five per-address features (interval, volume, success rate, fees, asset diversity), is how on-chain fingerprints are read. Interpretation rests on two motifs: a round-trip (swapping out of and back into the same base asset across two or more venues) marks arbitrage; a directional flow marks trading operations. Discriminating statistics: wrapped SOL appears in 70.9–99.9% of arbitrage-cluster transactio

What would settle it

Take bot addresses from a sniping service (bots that race to buy newly listed tokens) for one month, compute the same five features, and project them onto the four baseline clusters. If they form a new isolated cluster or mostly map to noise, the four-regime fingerprint landscape and its arbitrage/trading alignment fail for the broader population.

Watch

Extended reading notes

Core claim

Open-source Solana bots share a five-stage pipeline — setup, observation, analytics, planning, execution — despite heterogeneous building blocks. Clustering 200 bot addresses on transaction frequency, volume, success rate, fees, and asset diversity yields four clusters: low-rate/low-success multi-venue; high-rate/low-success specialized-venue; steady aggregator-centric with high success; and low-rate near-perfect-success trading. The first three show a round-trip arbitrage motif and pivot on wrapped SOL; the fourth trades directionally on a single launch platform. Cluster-to-service correspondence and persistence into a later validation window mark the fingerprints as systematic.

Load-bearing premise

The on-chain half of the study rests on addresses drawn from a few leaderboard bot services that focus on trading and arbitrage; if sniping, wash-trading, NFT, and analytics bots behave differently, the four-cluster fingerprint landscape and its arbitrage-versus-trading interpretation may not generalize to the wider Solana bot population.

Editorial extensions

If this is right

  • Unknown addresses can be classified as arbitrage-oriented or trading-oriented from five basic transaction statistics, without access to code or intent labels.
  • The five-stage pipeline offers an architectural reference model for auditing bot codebases and for building modular frameworks that reuse shared components.
  • Because over 30% of dependencies lag more than a year behind the latest release, with core SDKs lagging 125–572 days, protocol upgrades can silently break a large share of deployed bots.
  • Venue composition is the temporally stable fingerprint (up to 8 of 10 top venues overlapped across time windows), while traded tokens are not (only 3 of 10 overlapped); detection should rely on venue and infrastructure signals rather than the assets traded.
  • Transactions that reach a proprietary automated-market-maker venue were profitable 62.3% of the time versus 21.0% otherwise, so execution-edge research should examine venue access as much as strategy logic.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The four clusters are learned from leaderboard services that are trading- and arbitrage-oriented; sniping, wash-trading, NFT, and analytics bots may occupy separate regimes, and a study sampling those services could test whether the 2x2 landscape extends.
  • Open-source bots are only the visible slice of the population; the five-stage pipeline likely underrepresents proprietary layers such as custom low-latency submission loops, so it should be read as a common floor rather than the full architecture.
  • The stability of venue fingerprints and instability of traded tokens suggests a practical monitoring rule: track where a bot routes and whom it pays for inclusion, not which token it happens to be buying this week.
  • A testable extension connects the two datasets: check whether addresses belonging to codebases with the most outdated core-SDK dependencies show lower on-chain success rates or higher failed-transaction shares; the paper's dependency-lag and execution-effectiveness measurements make the link plausible but do not establish it.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper presents a large-scale empirical study of Solana bots from two complementary data sources: 586 GitHub repositories and 200 bot-associated on-chain addresses with over 44 million transactions. RQ1 derives a 15-category functionality taxonomy via LLM-assisted open card sorting with human validation (91% accuracy on a stratified sample, Cohen's kappa 0.81). RQ2 analyzes code-level building blocks obtained by clustering 78,300 functions with a UMAP-HDBSCAN pipeline, derives a five-stage operational pipeline, and characterizes third-party dependency usage and technical lag. RQ3 clusters the 200 addresses by transaction-level execution features into four clusters, interprets three as MEV-oriented and one as trading-operations-oriented, and validates temporal stability using a later validation window. The paper claims to be the first implementation-grounded characterization of Solana bots and draws implications for bot design, dependency maintenance, and execution infrastructure.

Significance. If the central claims hold, this is a substantial contribution to the empirical software engineering and blockchain-bot literature. The study is descriptive rather than theory-driven, but it is unusually careful in several respects: inter-rater kappas (0.71–0.81), a stratified human validation of the taxonomy with per-category accuracy reported, a function-clustering pipeline with a reported silhouette score, manual validation of cluster-to-intent mapping with 96.23% agreement, and a separate time-window validation using 5-NN projection onto the baseline clusters. The replication package is referenced with a DOI. These strengths make the paper's descriptive findings about taxonomy and on-chain fingerprints credible. However, the headline 'shared five-stage pipeline' claim is not backed by a quantitative conformance analysis, and the profitability measure in RQ3 needs clarification; both affect the strength of the central conclusions.

major comments (3)
  1. [§5.1.2, Finding 3, Abstract] The claim that Solana bots follow a 'largely shared' five-stage pipeline is not quantitatively validated. Section 5.1.2 states only that 'multiple representative repositories' were selected per category and that building blocks were grouped into stages based on architectural responsibilities and co-occurrence. It does not report the number of repositories inspected, the selection criteria, inter-rater reliability for stage assignment, or any conformance measure across the 586 repositories. The paper itself notes (Fig. 2 and §5.2.2) that the Planning and Decision stage is simplified or omitted in categories such as Sniping, which is the largest category (134 repos). Without a conformance statistic, the 'shared' qualifier is an unsupported generalization; the pipeline may be a plausible reference model that few repositories instantiate. I request an operational definition of pipeline confo
  2. [§6.1.2, Findings 5–6, Fig. 4] The 'realized per-address profit' is computed from per-transaction balance deltas between pre_token_balances and post_token_balances. The text does not state whether transaction fees, priority fees, Jito tips, or transfers to service/protocol accounts are subtracted from these deltas. This matters because the paper later contrasts profitable HumidiFi-invoking transactions (62.30%) with non-HumidiFi transactions (21.01%) and draws conclusions about execution outcomes. If the balance-delta measure excludes fees and tips, the reported profitability may be materially biased, especially for clusters with high fee or tip outflows (e.g., Cluster 3 in Table 10). Please specify the exact profit formula, including what is debited and credited, and provide a sensitivity check using a net-of-fee and net-of-tip measure.
  3. [§3 and §8] The on-chain address sample is drawn from two baseline services (Trojan, SolanaMevBot) and one validation service (Axiom), all trading/arbitrage-oriented with public leaderboards. The paper acknowledges in §8 that this limits generalization to trading- and MEV-oriented bots and that three taxonomy domains are absent from the on-chain clusters. This is an acknowledged and appropriately scoped limitation, so I do not treat it as a blocking error. However, the claim of 'four distinct execution clusters' among only 158 non-noise addresses from these services should be phrased as a characterization of these services, not of Solana bots generally, in the abstract and conclusion.
minor comments (4)
  1. [References] Several references are duplicated or near-duplicated: [22] and [24] appear to be the same paper, and [61] and [66] as well as [19] and [67] are listed twice with different numbers. Please unify these entries to avoid citation inconsistencies.
  2. [§5.2.2] The pipeline stage is called 'Planning and Decision' in Figure 2 but 'Planning and Execution' in the paragraph discussing category variations. Please make the terminology consistent.
  3. [§9] Minor language issue: 'We addresses this gap' should be 'We address this gap.'
  4. [Throughout] Several figure/table cross-references in the main text (e.g., 'Table 8 in the Appendix', 'Table 11 in the Appendix') are fine, but the footnote marker in Table 9 for the token '1 coin can change your life' has a formatting issue (the superscript is lost). Please check the final rendering.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; a descriptive empirical study whose minor self-referential validations are limitations, not derivation-level circularity.

full rationale

This is a descriptive empirical study, not a derivation with fitted parameters or predictions. The central claims—15-category taxonomy (RQ1), five-stage pipeline (RQ2), and on-chain execution clusters (RQ3)—are inductive summaries of the collected GitHub repositories and on-chain addresses. No equation-level circularity is present: no parameter is fitted to a subset and then reported as a prediction; no uniqueness theorem from the authors is invoked to force a choice; no ansatz is smuggled in via citation. The taxonomy is validated by human annotation with Cohen's kappa (0.78/0.81) and a 100-repo sanity check (91% accuracy), providing independent content. The cluster interpretation in Section 6.2.2 is primarily based on manual round-trip motif annotation (96.23% agreement), not solely on service labels. Two self-referential aspects are worth noting as limitations rather than circularity. First, the 'MEV vs. Trading Operations' cluster labels are cross-validated using the same bot-service labels (SolanaMevBot vs. Trojan) that defined the on-chain sample; Section 8 acknowledges this scope, so the validation is not an external test. Second, the five-stage pipeline (Section 5.1.2) is abstracted from 'multiple representative repositories' within the same 586-repo dataset, and no conformance statistic is reported for how many repositories actually instantiate all five stages; this is a generalizability threat to the 'shared' qualifier, not a circular derivation. These issues lower confidence in generalization but do not make any result equivalent to its inputs by construction. Hence score 2.

Assumptions & free parameters 4 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new physical or conceptual entities. Its central claims rest on data-source fidelity, LLM/clustering reliability, and the representativeness of two bot services. The free parameters are analytic modeling choices, not fitted physical constants.

free parameters (4)
  • UMAP hyperparameters (n_neighbors, n_components) = 10, 10
    Tuned via grid search to maximize Silhouette in function clustering (Section 5.1.1); clustering outcomes depend on these choices.
  • HDBSCAN hyperparameters for function clustering (min_cluster_size, min_samples) = 5, 5
    Tuned via grid search in Section 5.1.1; controls the granularity of building-block extraction.
  • Address-clustering HDBSCAN hyperparameters = not reported
    Section 6.1.1 applies HDBSCAN without stating hyperparameters; the four-cluster solution (33/11/12/102 plus 42 noise) is not exactly reproducible.
  • k for k-NN validation classifier = 5
    Validation assigns addresses by 5-nearest-neighbor majority vote (Section 6.1.3), an arbitrary but reasonable choice.
assumptions (6)
  • domain assumption Dune SQL records accurately represent Solana transaction outcomes, fees, and token balance changes for the sampled addresses.
    All on-chain features (success rate, fees, profits) are computed from Dune SQL data (Section 3); errors in Dune's indexing would propagate through RQ3.
  • domain assumption Solscan program-ID annotations correctly map invoked program IDs to named venues.
    Venue characterization in Section 6.1.2 relies on Solscan annotations; mislabeled programs would misattribute cluster venue profiles.
  • domain assumption The GitHub search and filtering procedure yields a representative set of public Solana bot repositories.
    The taxonomy (RQ1) and pipeline (RQ2) are grounded entirely in the 586-repo dataset collected via keyword queries (Section 3); unknown repos not surfaced by search are excluded.
  • domain assumption LLM-generated function summaries and repository tags (DeepSeek v3.2) are semantically faithful enough for card sorting.
    Taxonomy construction (Section 4.1) and building-block labeling (Section 5.1.1) depend on LLM outputs; human validation covers only small samples.
  • domain assumption UMAP/HDBSCAN on Qwen3 embeddings identifies meaningful code clusters.
    Building blocks are derived by embedding and clustering functions (Section 5.1.1); if the embeddings do not capture functional similarity, the building-block and pipeline findings weaken.
  • domain assumption The round-trip motif and venue-specific directional flow definitions accurately separate MEV from trading operations.
    Cluster-to-taxonomy mapping (Section 6.2.2) uses these operational definitions; manual annotation of 53 addresses (96.23% agreement) supports but does not prove the mapping.

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Cite this review

Pith. "Pith review of Demystifying Solana Bots: From GitHub Blueprints to On-Chain Fingerprints." pith.science (2026). https://pith.science/paper/YRPVSWNV

@misc{pith2026260728424,
  author       = {Pith},
  title        = {Pith review of: Demystifying Solana Bots: From GitHub Blueprints to On-Chain Fingerprints},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YRPVSWNV}},
  note         = {Machine review of arXiv:2607.28424}
}
read the original abstract

Solana is an emerging blockchain platform designed for high throughput and low transaction fees, making it inexpensive to submit transactions at scale and, consequently, increasing exposure to bot spamming and related financial exploitation. Solana bots are typically off-chain software systems that operate in a competitive on-chain execution environment by constructing and submitting transactions, and the bot-related transactions on the decentralized exchanges exceed 250 million dollars in daily trading volume in January 2026. Prior studies on Solana have examined system performance, smart-contract security, and specific on-chain phenomena. However, we still lack a systematic understanding of what Solana bots implement in practice and how these implementations manifest as observable on-chain execution fingerprints. To address this gap, we performed a large-scale empirical study of Solana bots from two complementary views: (i) 586 bot repositories collected from GitHub, and (ii) 200 bot addresses on Solana, with over 44 million on-chain transactions. Our study derives an implementation-grounded taxonomy of Solana bots comprising 15 categories grouped into five domains (e.g., Trading Operations, MEV, and On-chain Analytics), identifies a largely shared five-stage operational pipeline manifested in bot implementations, and uncovers systematic variation in on-chain trading behaviors of Solana bots across diverse trading platforms and assets. Based on our findings, we highlight future research directions, and provide recommendations for building and operating bots on the Solana blockchain.

Figures

Figures reproduced from arXiv: 2607.28424 by the authors.

Figure 1
Figure 1. Repository coverage distribution per building block. [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Bot pipeline across categories, organized into five stages. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Transaction success rate vs. submission rate across [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Total profits Distribution across clusters. Bands [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

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Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.