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REVIEW 4 major objections 5 minor 33 references

Unraveling Ethereum's Mempool: The Impact of Fee Fairness, Transaction Prioritization, and Consensus Efficiency

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Ethereum's transaction inclusion remains fee-driven after EIP-1559, a live-node study finds.

desk verdict Real live-node dataset and a plausible congestion-finality link, but the flagship inclusion-probability figure is circular and the supporting regressions are underreported. read the letter →

arxiv 2506.07988 v1 pith:6KOTJTW5 submitted 2025-06-09 cs.CR

S M Mostaq Hossain , Amani Altarawneh This is my paper
classification cs.CR
keywords EthereummempoolEIP-1559transactioninclusionfeefairnessgaspriceproof-of-stakeconsensuslatencyvalidatorefficiency
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

The paper argues that Ethereum's post-EIP-1559 fee market still systematically favors high-fee transactions. Using live data from a Geth execution node and a Prysm consensus node, it finds that transactions above roughly 75 Gwei are nearly always included while those below 20 Gwei face delay or exclusion. It also reports that mempool congestion correlates with longer block finalization times, and that extremely high fees do not reliably speed up confirmation. If correct, this means EIP-1559 improved fee predictability but did not remove fee-based inclusion bias, and that mempool load is a measurable drag on consensus efficiency.

What carries the argument

Three quantitative tools carry the argument: the inclusion-probability proxy $P(Tx_i \in B_t) = G(Tx_i)/\sum_j G(Tx_j)$, which approximates a transaction's likelihood of entering the next block by its share of total mempool gas price; the linear regression $T_{final} = \alpha M_{pend} + \beta G_{avg} + \gamma$ linking finalization time to pending transaction count and average gas price; and the fairness deviation metric $\Delta F_{fair} = (I^{obs}_{LP} - I^{exp}_{LP})/I^{exp}_{LP}$ comparing observed with expected inclusion of low-fee transactions. The live Geth and Prysm nodes supply the transaction-pool and beacon-chain data these tools are fit to.

What would settle it

A falsifying result would be a dataset of actual inclusion outcomes over many blocks in which, after controlling for block space and priority fees, inclusion probability does not rise with gas price, or in which low-fee transactions are included at rates far above their gas-price share. Concretely, take historical blocks, record each transaction's gas price and whether it entered the next block it could enter, bin by gas price, and compare observed inclusion frequencies with the predictions of Equation 1.

Watch

Extended reading notes

Core claim

The central claim is that Ethereum transaction inclusion remains heavily fee-driven under proof of stake. Empirically, inclusion probability rises with gas price toward near-certainty above 75 Gwei, low-fee transactions below 20 Gwei experience delay or exclusion, and mempool clearance rates fluctuate around 40–60% with congestion delaying finalization. The paper further finds that fees above roughly 50 Gwei offer diminishing returns, so very high fees do not guarantee faster confirmation. The authors conclude that fee-based prioritization bias persists despite EIP-1559 and propose congestion-aware fee adjustments, reserved block slots for low-fee transactions, and handling of out-of-gas vulnerabilities.

Load-bearing premise

The analysis rests on the assumption that a transaction's share of total mempool gas price represents its chance of being included in the next block, a proxy the authors themselves call informal; because that share is proportional to gas price by construction, the positive gas-price/inclusion correlation the paper reports is guaranteed by the metric rather than independently discovered.

Editorial extensions

If this is right

  • If inclusion is as fee-driven as reported, EIP-1559's base-fee mechanism does not by itself restore fee fairness, and protocol changes such as reserved low-fee block slots would be needed to change inclusion outcomes.
  • Mempool congestion is not just a fee-market symptom but a measurable driver of block finalization latency, implying that validator efficiency depends on mempool state.
  • The finding that fees above roughly 50–75 Gwei hit diminishing returns suggests users can often set moderate priority fees without sacrificing inclusion speed, and that excess fees are largely wasted.
  • The fairness deviation metric and congestion-aware adjustment proposals, if adopted, could be evaluated on other proof-of-stake chains that use mempool-based transaction selection.

Reading between the lines

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

  • The paper's proxy in Equation 1 is proportional to gas price by construction, so the headline correlation between gas price and the proxy is in part baked into the metric; an independent test would compare actual inclusion frequencies against the proxy's predictions rather than regressing the proxy on itself.
  • The proposed reserved-slot mechanism is a testable policy: allocating a small number of block slots by arrival time or lottery would let an experimenter measure how much inclusion fairness improves without sacrificing throughput.
  • The diminishing returns above roughly 50 Gwei could be turned into a practical fee oracle, recommending that users set priority fees near the plateau point rather than matching the mempool's maximum.
  • Because the congestion-finalization correlation comes from a single node pair over one observation window, it should be checked against historical beacon-chain data to see whether it holds across network stress events.
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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

4 major / 5 minor

Summary. This paper reports an empirical study of Ethereum's mempool after EIP-1559, using a Geth execution node and a Prysm consensus node to collect real-time transaction, fee, and block finalization data. It proposes a normalized gas-price share as a proxy for inclusion probability (Eq. 1), a linear regression model for finalization time (Eq. 2), and a fairness deviation metric (Eq. 3). Based on these tools and several plots, the paper concludes that transaction inclusion remains heavily fee-driven, that low-fee transactions face delays or exclusion, that mempool congestion affects validator efficiency and finalization latency, and that excessively high fees do not guarantee faster confirmation. It then suggests protocol-level improvements such as congestion-aware fee adjustments, reserved block slots for low-fee transactions, and better handling of out-of-gas vulnerabilities.

Significance. If the empirical claims were adequately supported, the paper would be a useful contribution to the ongoing evaluation of EIP-1559 and to the less-studied coupling between mempool congestion and consensus-layer performance. The authors' setup, combining execution-layer and consensus-layer monitoring, is a reasonable and potentially valuable approach, and the proposed fairness metric and policy recommendations are relevant to current Ethereum design discussions. The paper also explicitly acknowledges that Eq. 1 is only a proxy, which is honest. However, as it stands, the central quantitative evidence does not support the headline conclusions: the main inclusion metric is defined in terms of gas price and therefore produces a gas-price trend by construction, while the latency and fairness regressions are reported without coefficients, errors, sample sizes, or study periods. No dataset or code is released, so the empirical results cannot currently be independently verified.

major comments (4)
  1. [§5.2, Eq. (1); §7.1, Fig. 2] The 'inclusion probability' P(Tx_i ∈ B_t) is defined as G(Tx_i) / Σ_j G(Tx_j), i.e., as the transaction's share of total gas price in a mempool snapshot. For a fixed snapshot the denominator is common to all transactions, so P is monotonically increasing in G(Tx_i). Consequently, plotting P against gas price must show a positive trend, and the near-100% inclusion for high fees reported in Section 7.1 is an artifact of this normalization rather than a measured property of actual block inclusion. The paper's own statement that 'the observed correlation aligns with the inclusion proxy in Eq. 1' confirms this circularity. The authors should replace this proxy with direct observed inclusion rates (e.g., the fraction of broadcast or mempool transactions at each fee level that actually appear in subsequent blocks) and report counts, time windows, and confidence intervals.
  2. [§5.3, Eq. (2); §7.4, Fig. 5] The regression model T_final = α · M_pend + β · G_avg + γ is presented as the basis for the claim that higher gas prices reduce latency and that mempool congestion affects finalization. However, the paper never reports the estimated coefficients, standard errors, p-values, R², sample size, or study period for this model. Figure 5 shows a fitted line and confidence band, but without the underlying regression output the reader cannot assess whether the alleged negative relationship is statistically significant, how large the effect is, or whether alternative specifications would change the conclusion. The same issue affects the related discussion in Section 7.2, which invokes Eq. 2 to claim that clearance rates confirm congestion effects. Full regression diagnostics are required before these claims can be evaluated.
  3. [§5.4, Eq. (3); §7.3, Fig. 4] The fairness deviation metric ΔF_fair depends on I_obs^LP and I_exp^LP, but the paper never defines how the expected fraction I_exp^LP is computed from observable data, nor does it report the observed and expected values, sample sizes, or confidence intervals behind Figure 4. The claim that fee market efficiency 'plateaus around 80%' is therefore not supported by any quantitative evidence. The authors should specify the exact formula for I_exp^LP, describe how low-fee transactions are identified, and report the underlying counts and uncertainties.
  4. [§6, Experimental Setup; §7, Results] The paper does not provide essential provenance for its empirical claims. Section 6.1 does not state the data collection dates, the duration of the observation window, the number of transactions observed, the number of blocks analyzed, or the sampling methodology. Section 6.2 says transactions were broadcast with varying gas fees, but gives no details on fee levels, transaction counts, or timing. Because no dataset or code is released, and Figures 2–5 are presented without underlying statistics, the empirical evidence summarized in Section 9 cannot be independently checked. The authors should state these details explicitly and, ideally, make the anonymized data and analysis scripts available.
minor comments (5)
  1. [References] Reference [19] (Cambus et al., a paper on the 2-ruling set problem) appears unrelated to Ethereum fee markets or transaction processing and should be replaced with a relevant citation or removed.
  2. [§5.2, Eq. (1)] The displayed equation is corrupted in the manuscript: 'NP j=1' appears where a summation symbol and limits should be, and the notation Txi is not consistently introduced. Please fix the typesetting.
  3. [§7.3] The sentence beginning 'The third analysis, illustrated in Figure 4, explores the relationship Figure 4 shows how...' contains a duplicated phrase and should be rewritten.
  4. [§8, Take-Away sections] The emoji headers '🔑 Take-Away RQ-i/ii/iii' are informal for an archival publication; please replace them with standard subsection headings.
  5. [§6.2] The description of the automated transaction submission is too vague to be useful; please specify how many transactions were broadcast, at what fee levels, over what time period, and how inclusion latency was measured.

Circularity Check

1 steps flagged · score 6.0 of 10

Inclusion-probability evidence is circular: Eq. 1 defines P as a normalized gas-price share, so Fig. 2's positive correlation is forced by the metric's construction; the headline fee-bias claim therefore rests in part on a tautology, though latency and clearance analyses are independent.

  1. self definitional [Section 5.2, Eq. (1), and Section 7.1, Fig. 2]
    "We define a proxy metric for the likelihood of a transaction Txi being included in the next block Bt, based on its relative gas price compared to competing transactions. This is not a formal probability definition ... P (Txi ∈ Bt) = G(Txi) / sum_j G(Txj) (1) ... Figure 2 shows a strong positive correlation between gas price and transaction inclusion probability."

    Within any mempool snapshot the denominator of Eq. 1 is the same for all transactions, so P(Tx_i in B_t) = G(Tx_i)/S is a deterministic, monotonically increasing function of G(Tx_i). Plotting this quantity against gas price (Fig. 2) therefore displays an algebraic identity, not an empirical association. The paper explicitly labels Eq. 1 a proxy rather than a probability, but Section 7.1 then uses the forced correlation as evidence that 'EIP-1559 does not eliminate prioritization bias' and that low-fee transactions 'remain disadvantaged.' A metric constructed to be proportional to gas price cannot provide independent evidence that high gas prices increase inclusion likelihood.

full rationale

The paper's central fee-prioritization claim rests partly on Eq. 1's self-referential inclusion proxy: the 'strong positive correlation' in Fig. 2 is guaranteed by the normalization and is therefore not an empirical finding. However, the paper also offers independent, non-circular evidence: broadcast-transaction latency vs. gas price (Fig. 5), mempool clearance rates (Fig. 3), and a heatmap linking mempool size to finalization time (Fig. 6). These direct observations would, if fully reported, support the fee-driven-inclusion and congestion-latency conclusions. The fairness-deviation metric (Eq. 3) compares observed vs. proportional inclusion and is not circular by construction. No load-bearing self-citation chain was found; the references are external prior work and client documentation. Because one of the paper's named quantitative analyses (inclusion probability vs. gas price) reduces to its own definition while other analyses are independent but underreported, partial circularity is present but the derivation as a whole is not wholly forced.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new entities are introduced. The proposed congestion-aware fee adjustments and reserved block slots are policy suggestions, not postulated scientific entities. The main free parameters are the regression coefficients in Eq. 2 and hand-chosen rolling window sizes. The proxy in Eq. 1 is the most consequential ad hoc assumption because it forces the central inclusion-probability result.

free parameters (2)
  • Regression coefficients alpha, beta, gamma = Not reported
    Equation 2 fits finalization time as a linear function of pending mempool count and average gas price; coefficients are learned from the authors' dataset but values, standard errors, and goodness-of-fit are never reported.
  • Rolling average window sizes = 50 transactions and 30 blocks
    Chosen by hand for smoothing; affects visual interpretation of Figures 3 but not the central argument.
assumptions (4)
  • ad hoc to paper The normalized gas-price share in Eq. 1 approximates the probability of inclusion in the next block
    Section 5.2 explicitly states this is 'not a formal probability definition', but a normalized approximation. Because it is proportional to gas price by definition, any plot of it against gas price trivially shows a positive trend. The paper nevertheless uses it as the basis for Figure 2 and the inclusion-probability finding in Section 7.1.
  • ad hoc to paper Linear relationship between finalization time and pending count and average gas price
    Equation 2 is a linear regression model asserted without justification or diagnostic checks; used to claim mempool congestion affects finalization time in Sections 7.2 and 7.5.
  • domain assumption A single Geth and Prysm node pair is representative of the global Ethereum mempool
    Section 6.1 describes a single node deployment; mempool content is local and varies by peer connections and client type, so the proxy and clearance rates are specific to that node's view.
  • domain assumption Proportional inclusion is the fairness baseline
    Equation 3 defines fairness as observed low-fee inclusion matching the mempool share of low-fee transactions. This assumes proportional inclusion is the correct normative baseline, which is not argued.

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Pith. "Pith review of Unraveling Ethereum's Mempool: The Impact of Fee Fairness, Transaction Prioritization, and Consensus Efficiency." pith.science (2026). https://pith.science/paper/6KOTJTW5

@misc{pith2026250607988,
  author       = {Pith},
  title        = {Pith review of: Unraveling Ethereum's Mempool: The Impact of Fee Fairness, Transaction Prioritization, and Consensus Efficiency},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6KOTJTW5}},
  note         = {Machine review of arXiv:2506.07988}
}
read the original abstract

Ethereum's transaction pool (mempool) dynamics and fee market efficiency critically affect transaction inclusion, validator workload, and overall network performance. This research empirically analyzes gas price variations, mempool clearance rates, and block finalization times in Ethereum's proof-of-stake ecosystem using real-time data from Geth and Prysm nodes. We observe that high-fee transactions are consistently prioritized, while low-fee transactions face delays or exclusion despite EIP-1559's intended improvements. Mempool congestion remains a key factor in validator efficiency and proposal latency. We provide empirical evidence of persistent fee-based disparities and show that extremely high fees do not always guarantee faster confirmation, revealing inefficiencies in the current fee market. To address these issues, we propose congestion-aware fee adjustments, reserved block slots for low-fee transactions, and improved handling of out-of-gas vulnerabilities. By mitigating prioritization bias and execution inefficiencies, our findings support more equitable transaction inclusion, enhance validator performance, and promote scalability. This work contributes to Ethereum's long-term decentralization by reducing dependence on high transaction fees for network participation.

Figures

Figures reproduced from arXiv: 2506.07988 by the authors.

Figure 1
Figure 1. The framework analyzes mempool behavior, execution-consensus correlation, and fee market efficiency using real-time monitoring, statistical [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The plot shows a positive correlation between gas price and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Mempool clearance rate over time, with raw fluctuations and [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: The plot illustrates the relationship between max fee per gas [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: The plot shows that higher gas prices reduce transaction [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.