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REVIEW 5 major objections 6 minor 88 references

Why Does My Transaction Fail? A First Look at Failed Transactions on the Solana Blockchain

T0 review · 5 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Bots fail 58.43% of Solana transactions; humans 6.22%

desk verdict Worth taking seriously, but the bot-vs-human headline is built on a classifier that labels only a quarter of the data; the error taxonomy alone justifies a revised publication. read the letter →

arxiv 2504.18055 v1 pith:2PUTGM6W submitted 2025-04-25 cs.SE

Xiaoye Zheng (1) , Zhiyuan Wan (1 , 2) , David Lo (3) , Difan Xie (2) , Xiaohu Yang (1) ((1) Zhejiang University , (2) Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain , Data Security
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(3) Singapore Management University)
This is my paper · ORCID
classification cs.SE
keywords SolanafailedtransactionsblockchainDeFibotaccountserrorclassificationempiricalstudyautomatedmarketmaker
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 establishes that the flood of failed transactions on Solana is overwhelmingly a bot problem, not a user problem: across 53 sampled days spanning a year, bot-initiated transactions failed 58.43% of the time while human-initiated transactions failed only 6.22%. It further shows the failures concentrate in a small set of programs: the top ten failure-triggering programs account for 77.95% of all failed transactions, led by the Raydium AMM and Jupiter DEX aggregators. By classifying the error messages attached to more than a billion failed transactions, the paper finds that three error types—price or profit not met, invalid status, and validity expiration—explain 84.90% of failures. If these numbers hold, they reframe Solana's congestion and reliability debate: the problem is high-frequency automated trading racing slim margins, not ordinary users making mistakes.

What carries the argument

The load-bearing machinery is a large curated dataset plus two classification layers. The dataset joins on-chain block and transaction records retrieved through an RPC provider with off-chain program names and source code, covering 2,898,175,006 non-vote transactions. On top of it, the paper builds a random-forest account classifier that labels 12,712,516 accounts as bot or human from transaction frequency and volume features, validated at 96.91% accuracy on a manually labeled sample, and an error taxonomy constructed by extracting 2,311 unique error messages and grouping them through thematic analysis into ten error types. The account classifier makes the bot-versus-human failure-rate contrast possible; the error taxonomy carries the finding that most failures reduce to price, state, and validity checks.

What would settle it

Recompute the six headline statistics (bot failure rate, human failure rate, top-ten program share, and the three largest error-type shares) from all 365 days of August 2023 through July 2024 rather than the 53 sampled days; if any figure moves outside the paper's stated ±0.06% margin, or materially away from the reported values, the characterization fails.

Watch

Extended reading notes

Core claim

The paper's central discovery is a quantitative map of why Solana transactions fail at the ecosystem scale. Mining 1,510,834,168 failed non-vote transactions from 72,123,900 blocks, it reports that bots experience a transaction failure rate of 58.43% versus 6.22% for humans, that the top ten programs responsible for failures account for 77.95% of all failed transactions, and that just three error categories—price or profit not met (47.99%), invalid status (19.19%), and validity expiration (17.72%)—account for 84.90% of all failures. The paper also reports that failed transactions sit deeper in blocks (median position 592 versus 529) while paying higher fees per compute unit, that DEX aggregators fail mainly on price or profit conditions, and that AMMs fail mainly on invalid status checks, often because sniper bots trade before a pool is initialized. The intended upshot is that Solana's failure problem is a bot-driven, DeFi-concentrated phenomenon rather than a general user-experience defect.

Load-bearing premise

Every headline percentage is computed from 53 sampled days—one randomly chosen day per week—and the paper asserts, without derivation, that the sample represents the whole year at a 99.999% confidence level; if those days miss memecoin surges or protocol-upgrade periods, all of the aggregate figures shift.

Editorial extensions

If this is right

  • If bots are the main source of failed transactions, then reducing bot spam—for example through dynamic fees on rapid repeated submissions—should lower both failure counts and network congestion.
  • Failure diagnosis can be prioritized: a transaction that fails on a DEX aggregator should first be checked for unmet price or profit conditions, and a transaction that fails on an AMM should first be checked for pool state readiness.
  • Human-focused tooling should target out-of-funds checks and input validation, since humans fail less often and on a narrower set of errors.
  • The 24-hour periodicity in failure rates means failure load is partly predictable, opening the door to time-aware fee or retry strategies.
  • Architecture-level comparison: Solana's low fees and parallel execution appear to enable the bot behavior; ecosystems with gas auctions show lower bot-driven failure rates.

Reading between the lines

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

  • If the 47.99% price-or-profit-not-met share reflects rational bot strategy rather than user error, then a large share of 'failed' transactions are actually expected costs of a search process: bots intentionally fire unprofitable transactions and let rejection filter them, so a testable extension is measuring how failure rates respond when the fee for repeated submissions from one account rises.
  • The drop in failure rates after the June 2024 validator update, noted in the paper, suggests a quasi-natural experiment; comparing failure rates around that update with and without bot-account filtering could isolate how much of the reduction comes from protocol changes versus reduced spam.
  • The paper's taxonomy could be applied prospectively: if invalid-status errors spike around new pool creations, real-time detection of sniper-bot waves could be built from the error logs alone.
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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

5 major / 6 minor

Summary. The paper presents a large-scale empirical study of failed non-vote transactions on the Solana blockchain. The authors sample 53 days (one per week) between August 1, 2023 and July 31, 2024, collect 2,898,175,006 non-vote transactions, classify initiator accounts as bot or human using a random forest with a 0.9 confidence threshold, characterize failures by program, time, block position, and cost, extract error messages, and derive a ten-category error taxonomy via thematic analysis. The main findings are that bot accounts have a 58.43% failure rate versus 6.22% for human accounts; the top ten programs account for 77.95% of failures; price/profit-not-met, invalid-status, and validity-expiration errors account for 84.90% of failures; and DEX aggregators and AMMs exhibit distinct error profiles. The paper concludes with ecosystem-level recommendations and a public replication package.

Significance. If the results are robust, this is a valuable first systematic account of failed transactions on Solana, with direct relevance to protocol design, DeFi program development, and user tooling. The dataset is unusually large, the data-processing pipeline is described in detail, the error taxonomy is built through manual coding with consensus procedures, and a replication package is provided. The headline conclusion that Solana's transaction-failure problem is largely bot-driven is plausible and practically important, but it currently rests on the classified subset of accounts, and several statistical claims are under-supported. I also note that the random forest is a labeling tool rather than a model of the outcome being estimated, so I do not see a circularity problem here; the issue is coverage and validation of the labels, not circularity.

major comments (5)
  1. [§3.2, Table 1] Finding 1 is computed only from accounts classified with random-forest probability above 0.9. These accounts produce 779.3M transactions, i.e., 26.9% of the 2,898.2M sampled non-vote transactions. The remaining 2,118.8M transactions—including 1,057.1M failed transactions, or 70.0% of all failures in the sample—come from 'unknown' accounts and are excluded from the bot-versus-human comparison. The unknown-account failure rate is 49.9%, close to the overall sample failure rate of 52.1%, so the claim that Solana's failures are largely bot-driven depends on an unverified assumption about the initiator type of unknown-account failures. Since §7.3 does not discuss this coverage threat, please report a sensitivity analysis (e.g., lower the threshold, or manually label a sample of unknown-account failures) and bound Finding 1 under worst-case assumptions about the unknown set.
  2. [§3.1] The claim that stratified weekly sampling achieves a '99.999% confidence level with a margin of error of ±0.06%' is asserted without derivation. The failure rate is highly non-stationary—the paper itself notes memecoin-mania episodes and the June 10, 2024 validator update—so a textbook simple-random-sampling formula cannot be applied to one randomly selected day per week. Please provide the exact estimator, its variance, and the assumptions; otherwise, the aggregate percentages in Findings 2 and 5 are not shown to represent the full year.
  3. [§3.2] The classifier is trained on 200 manually labeled accounts and validated on 194 sampled accounts, yielding 96.91% accuracy. This measures agreement on a hand-picked validation set, not whether the high-confidence subsets are representative of bots and humans at large. The imbalance is striking: classified human accounts average about 2.3 transactions per account-year, while classified bot accounts average about 966, so the random forest may be separating high-activity from low-activity accounts rather than automated from human initiators. Please report per-class precision and recall, class-balance information, and validation results for accounts near the 0.9 threshold.
  4. [§4.2.2] The Wilcoxon rank-sum tests are applied to billions of transactions, so p<0.001 is essentially guaranteed and does not convey the magnitude of the observed differences. Please report standardized effect sizes (e.g., rank-biserial correlation) and confidence intervals for the block-position, fee, compute-unit, and cost-efficiency comparisons, so readers can gauge whether the reported differences are practically meaningful.
  5. [§4.2.1, Finding 3] Finding 3 states that failure rates exhibit a strong positive correlation with the volume of failed transactions, but the surrounding text describes correlation with hourly transaction volume, and no correlation coefficient is reported. Please state the exact statistic (e.g., Spearman's r), its lag, and its confidence interval; otherwise the 'strong positive correlation' claim is not quantitatively supported.
minor comments (6)
  1. [§7.1] The reference '[73? ]' is a broken citation placeholder and should be corrected.
  2. [§4.2.1 and §4.2.2] The sentence 'A Wilcoxon rank-sum test confirms the statistical significance of the difference in block positions...' appears twice; the duplicate in the Temporal Trends paragraph should be removed.
  3. [§5.2, Finding 5] Finding 5 lists the top three error types as 'price or profit not met, validity expiration, and invalid status', whereas the preceding text and Figure 6 present the second and third types in the order invalid status then validity expiration. Please make the order consistent.
  4. [§4.2.2] The phrase 'the failed transactions have be outcompeted' is a grammatical error; it should read 'have been outcompeted'.
  5. [Abstract and §3.1] The abstract says the dataset spans 'more than 72 million blocks', but the sampling procedure selected 10,458,452 blocks from the 72,123,900-block range; please clarify that 72 million is the full-year block range, not the sampled block count.
  6. [Table 1] Consider adding a row for unknown accounts in Table 1, since their exclusion is material to the interpretation of the bot/human comparison.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the study's findings are direct measurements and manual taxonomies, not predictions fitted to their own outcomes.

full rationale

The paper is an observational empirical study; its headline numbers are computed directly from blockchain data rather than produced by a model fitted to those same numbers. Account typing in Section 3.2 uses a random forest trained on 200 manually labeled accounts with features (transaction frequency and volume) drawn from prior work [62, 71]; the failure/success outcome is not an input feature, and the reported 58.43% vs 6.22% failure rates are subsequently computed over transactions of the classified accounts, so the rates are not forced by the classifier. The error taxonomy (RQ2) comes from manual coding of error log messages (Section 5.1), and the percentages in Finding 5 are direct frequency counts over the coded messages, not outputs of a fitted model. No load-bearing step cites an unverified self-result: the bot-detection features cite external prior work [62, 71], and no uniqueness theorem or author-supplied ansatz is invoked to exclude alternatives. Points such as the unsubstantiated 99.999% confidence claim for weekly sampling and the exclusion of 73.1% of transactions below the 0.9 classification threshold are validity and representativeness concerns, not circularity: they do not make any measured quantity equal to an input by construction. Accordingly, the appropriate score is 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The paper's central claims rest on observational data and human-defined categories, not on a formal derivation. The main assumptions concern data representativeness, error attribution, and the validity of the bot/human classifier.

free parameters (2)
  • account classification confidence threshold = 0.9
    Chosen by hand in Section 3.2; accounts with classification probability below 0.9 are labeled 'unknown' and excluded from bot/human analysis.
  • random forest hyperparameters = scikit-learn defaults
    Section 3.2 uses scikit-learn's random forest but does not report hyperparameters; these affect the classifier decisions and thus the bot/human labels.
assumptions (5)
  • domain assumption A transaction is classified as failed iff its error metadata field is non-empty (Section 3.1).
    Standard Solana semantics, but it defines the scope of the study and excludes dropped or unprocessed transactions that have no metadata.
  • domain assumption The sampled 53 days (one per week) are representative of the full year, with 99.999% confidence and ±0.06% margin of error asserted in Section 3.1.
    This is load-bearing for all aggregate percentages; the calculation is not shown.
  • domain assumption The outermost program in an error log's call stack is responsible for the failure (Section 2.2).
    In nested cross-program invocations, the outermost program may not be the true root cause, but the paper uses it for program attribution.
  • domain assumption Failed transactions with incomplete or missing error messages can be excluded without biasing the error-type distribution (Section 5.1).
    365 million of 1.51 billion failed transactions lack explicit messages; the remaining 1.145 billion are treated as representative.
  • domain assumption Thematic analysis codes derived from 173 error messages generalize to the full set of 2,311 unique messages (Section 5.1).
    The authors state the codes account for 99.71% of messages, but unexamined messages could contain new error types.

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Pith. "Pith review of Why Does My Transaction Fail? A First Look at Failed Transactions on the Solana Blockchain." pith.science (2026). https://pith.science/paper/2PUTGM6W

@misc{pith2026250418055,
  author       = {Pith},
  title        = {Pith review of: Why Does My Transaction Fail? A First Look at Failed Transactions on the Solana Blockchain},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2PUTGM6W}},
  note         = {Machine review of arXiv:2504.18055}
}
read the original abstract

Solana is an emerging blockchain platform, recognized for its high throughput and low transaction costs, positioning it as a preferred infrastructure for Decentralized Finance (DeFi), Non-Fungible Tokens (NFTs), and other Web 3.0 applications. In the Solana ecosystem, transaction initiators submit various instructions to interact with a diverse range of Solana smart contracts, among which are decentralized exchanges (DEXs) that utilize automated market makers (AMMs), allowing users to trade cryptocurrencies directly on the blockchain without the need for intermediaries. Despite the high throughput and low transaction costs of Solana, the advantages have exposed Solana to bot spamming for financial exploitation, resulting in the prevalence of failed transactions and network congestion. Prior work on Solana has mainly focused on the evaluation of the performance of the Solana blockchain, particularly scalability and transaction throughput, as well as on the improvement of smart contract security, leaving a gap in understanding the characteristics and implications of failed transactions on Solana. To address this gap, we conducted a large-scale empirical study of failed transactions on Solana, using a curated dataset of over 1.5 billion failed transactions across more than 72 million blocks. Specifically, we first characterized the failed transactions in terms of their initiators, failure-triggering programs, and temporal patterns, and compared their block positions and transaction costs with those of successful transactions. We then categorized the failed transactions by the error messages in their error logs, and investigated how specific programs and transaction initiators are associated with these errors...

Figures

Figures reproduced from arXiv: 2504.18055 by the authors.

Figure 2
Figure 2. Hourly Trends of Successful and Failed Non [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Cumulative Distribu￾tion of Programs Responsi￾ble for Failed Transactions on Solana [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 5
Figure 5. Fees, Compute Units, and Cost Efficiency of Successful vs. Failed Transactions on Solana. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Distribution of Error Types in Failed Transactions. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Mappings of Error Types Across Top 10 Programs, Bots, and Human Accounts with the Highest [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.