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REVIEW 4 major objections 9 minor 40 references

There Will Be Spam: Characterizing State-Invariant Transactions and Speculative MEV

T0 review · 4 major / 9 minor · reviewed 2026-07-31 · grok-4.5

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Pith's one-line read Nearly 1.4 billion blockchain transactions leave the ledger state unchanged beyond fees, and on Layer-2s they are a large share of all activity.

desk verdict Useful general spam metric and a real multi-chain census, undercut by a clear abstract/table mismatch on the headline MEV shares. read the letter →

arxiv 2607.24172 v1 pith:DH3GEW2M submitted 2026-07-27 cs.CR

Vabuk Pahari , Johnnatan Messias , Christof Ferreira Torres This is my paper
classification cs.CR
keywords state-invarianttransactionsspeculativeMEVblockchainspamLayer-2rollupsaddresspoisoningEthereumOptimismBase
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

Blockchains keep every transaction forever, so storage and verification costs grow without bound. This paper defines state-invariant transactions as those whose inclusion or removal does not change the resulting ledger state except for fees paid, and treats them as on-chain spam because they still burn execution, bandwidth, storage, and blockspace. Measuring Ethereum, Optimism, and Base from September 2023 through July 2025, the authors find nearly 1.4 billion such transactions: only 2.6% of Ethereum traffic, but 24% on Optimism and 37% on Base. Speculative MEV (searchers probing for arbitrage or liquidations on-chain because fees are low and mempools are private) drives most of that Layer-2 spam, yet is not the sole source, and once the fees of failed probes are counted many speculative bots are unprofitable. On Ethereum, address-poisoning phishing accounts for a large share of non-reverted state-invariant traffic. The claim is that filtering or pricing these transactions would cut resource waste, lower costs, and curb some abuse without breaking the chains’ transparency model.

What carries the argument

State-invariant transaction: a transaction whose removal from a block leaves the resulting blockchain state identical once fee transfers to the proposer or fee vaults are ignored. Detected at scale via per-block stateDiff traces (only sender and fee recipients change, and the deltas match the fees), then classified with calldata entropy matching for speculative vs non-speculative MEV, plus external labels for address poisoning and inscription prefixes.

What would settle it

Re-run the same block range with full ABI-aware or de-obfuscated calldata parsing (or ground-truth bot source) and check whether the speculative/non-speculative split and the 57%/68% MEV attribution on Optimism and Base hold; if most “speculative” bots actually encode amounts off-chain, the dominance claim fails.

Watch

Extended reading notes

Core claim

State-invariant transactions—transactions that execute code but leave account balances and storage unchanged beyond fee transfers—are a measurable, large class of spam. Across the study window they total nearly 1.4 billion; they are a minor fraction of Ethereum volume but roughly a quarter to more than a third of all transactions on Optimism and Base. Speculative MEV is the dominant but not exclusive source on those rollups (about 57% and 68%), and after including the cost of state-invariant attempts speculative strategies are often unprofitable; on Ethereum, address poisoning is a major non-MEV source among successful state-invariant transactions.

Load-bearing premise

A successful arbitrage or liquidation is treated as non-speculative only when an entropy-filtered token amount from the trade appears in the calldata, so bot labels and the MEV share of spam rest on searchers not systematically hiding those amounts.

Editorial extensions

If this is right

  • Removing or not retaining state-invariant history would free a large fraction of archive-node storage on Optimism and Base.
  • Fee or sequencing rules that discourage pure probes would shrink Layer-2 spam and priority-gas auctions without needing a public mempool.
  • Protocol filters on zero-effect executions could simultaneously reduce address-poisoning volume on Ethereum.
  • Profitability rankings of MEV strategies reverse once state-invariant fees are counted, so searchers and designers should price failed probes explicitly.
  • Inscriptions and pure message-passing are real but small contributors compared with speculative MEV and phishing in this window.

Reading between the lines

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

  • Rollup designs that made speculation cheap may need complementary “state-effect” or simulation gates before inclusion, or history-pruning rules keyed to state-invariance.
  • Wallet and explorer UX that hide zero-value and look-alike transfers would blunt address poisoning even without consensus changes.
  • If calldata obfuscation becomes common, the paper’s scalable bot classifier will need a trace- or simulation-based backup, reopening the cost tradeoff the authors avoided.
  • Archive-node centralization pressure on high-throughput L2s is partly an artifact of retaining economically null transactions, not only of useful throughput.
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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 / 9 minor

Summary. The paper introduces "state-invariant transactions" — transactions whose removal from a block leaves the ledger state unchanged except for fee transfers — as a formal, origin-agnostic metric for on-chain spam. Using per-block stateDiff traces from Reth archive nodes (with fee-vault accounting for L2s), the authors measure such transactions on Ethereum, Optimism, and Base from Sep 2023 to Jul 2025, finding ~1.4B state-invariant transactions: 2.6% of Ethereum's volume, but 24% on Optimism and 37% on Base. A trace-independent calldata-matching heuristic (with an entropy filter at false-positive rate f=10^-6) classifies MEV bots as speculative/non-speculative/hybrid, building on Torres et al.'s arbitrage/liquidation detection. The paper reports that speculative MEV dominates state-invariant volume on L2s, that most speculative bots are unprofitable once failed-attempt fees are counted, and that 53% of non-reverted state-invariant transactions on Ethereum stem from address poisoning. The core detection methodology is well-specified and matches the formal definition; limitations (obfuscation, dataset gaps) are disclosed.

Significance. If the numbers are reconciled, this is a significant measurement contribution. It provides the first formal, origin-agnostic definition of on-chain spam (state-invariance), the first large-scale quantification across an L1 and two major L2s over 23 months, and evidence that (i) prior spam measurements systematically underestimated non-arbitrage spam, (ii) speculative MEV on L2s is largely unprofitable once failed-attempt fees are counted, and (iii) a large share of Ethereum's successful spam is address poisoning — a security-relevant finding, not just an efficiency one. The classification heuristic is cheap, stated with an explicit false-positive bound, and its failure modes (calldata obfuscation, hybrid strategies, dataset coverage gaps) are disclosed in §3.3, which makes the results appropriately falsifiable. The cross-chain contrast (2.6% vs. 24% vs. 37%) and the mempool-supply-chain explanation for Ethereum's lower spam rate are both useful inputs to fee-mechanism and sequencing-policy design. The dataset and methodology, if released, would likely become a standard reference.

major comments (4)
  1. [Abstract / §1 vs. Tables 3–4] The headline attribution figures — speculative MEV accounting for 57% (Optimism) and 68% (Base) of state-invariant transactions — cannot be reproduced from the paper's own tables. Table 4 reports 83,760,338 speculative state-invariant txs on Optimism and 681,985,254 on Base; dividing by the Table 3 totals (122,454,486 and 1,228,926,146) yields 68.4% for Optimism and 55.5% for Base — roughly the reverse of the stated ordering, and an exact match for neither. Meanwhile §4.3.1's separate statement that 73%/60% of state-invariant txs correspond to identified bots matches Table 4's totals exactly (89.05M/122.45M = 72.7%; 716.24M/1228.9M = 58.3%), so the unexplained 57%/68% pair is the outlier. These percentages are the quantitative spine of the 'speculative MEV is dominant on L2s' claim and are repeated in the abstract, §1, the contributions list, and §5. Please reconcile: either correct the
  2. [Table 4 (Hybrid rows)] Table 4 has internal inconsistencies in the Hybrid rows that need explanation. For Base, the Hybrid state-invariant total is 32,930,392, but the Arbitrage and Liquidation sub-rows sum to 32,406,533 + 5,652,728 = 38,059,261, which exceeds the total. The same pattern holds for Optimism Hybrid (4,792,528 + 1,315,749 = 6,108,277 ≠ 5,071,709) and Ethereum Hybrid (223,701 + 29,795 = 253,496 ≠ 227,892). If this is because bots appear in both the arbitrage and liquidation sub-rows and transactions are double-counted across sub-rows, the table needs a footnote saying so and clarifying that the total row is deduplicated; otherwise the numbers are simply wrong. Relatedly, the 'Suc. Txs' entry for Base Hybrid reads '555,6975' — clearly 5,556,975 (which makes the Base total of 14,469,009 check out). Also, the bot counts do not add up (Ethereum: 582 + 836 + 468 = 1,886 vs. stated total 1,845), presuma
  3. [§4.5 and Abstract/§1 (53% address-poisoning share)] The 53% headline figure does not survive an internal consistency check either. §1 and the abstract state that address poisoning accounts for 53% of *non-reverted* state-invariant transactions on Ethereum. Table 3 gives 7,046,314 successful (non-reverted) state-invariant transactions, and 3,757,045/7,046,314 = 53.3% — but §4.5 states that of the 3,757,045 poisoning-related transactions, only 2,777,055 (72%) execute successfully. Using the successful subset gives 2,777,055/7,046,314 = 39.4%, not 53%. Either the 53% claim uses the wrong numerator (mixing failed transactions into a 'non-reverted' share) or §4.5's 72%-successful statement is wrong. Additionally, §4.5 first says the 3,402,101 matched transactions are 'nearly twice the number reported in the original dataset' and immediately after says the methodology 'identifies approximately 50% more' — 3.40M vs. 1.76M is ~93% more; pick one
  4. [§4.3.1 and §4.3.5 (denominator ambiguity)] The text is careless about which denominator the MEV-attribution percentages use, which materially changes the message. §4.3.1 states 'the overwhelming majority of state-invariant transactions (91%) are generated by speculative arbitrage bots' on Optimism — but 81.56M/89.05M = 91.6% is the share of *MEV-bot-attributed* state-invariant transactions; as a share of all state-invariant transactions it is 81.56M/122.45M = 66.6%. The Base sentence in the same paragraph is correctly qualified ('of all state-invariant transactions attributed to MEV bots'), and §4.3.5's '93%/95%' figures are also shares of the MEV-attributed subset. Given that the paper's own thesis is that speculative MEV is 'not the only source' of spam, mixing attributed-share and total-share denominators in adjacent sentences invites misreading; please audit all such percentages and state denominators uniformly.
minor comments (9)
  1. [§4.5] Typo '278,25' for the Base address-poisoning transaction count (presumably 278,250 or similar).
  2. [§3.3 (Limitations)] Cites 'Tsuchiya et al. [32]' for the address-poisoning dataset, but [32] is 'Blockchain Amplification Attack' (Tsuchiya et al., PoMACS 2025); the correct reference is [31] (USENIX Security 2025). §3.2.2 uses [31] correctly.
  3. [§3.1 (formal definition vs. detection)] The formal definition says S_Bi = S_Bi' after removal 'excluding the transfer of transaction fees', but removing a transaction also reverts the sender's nonce increment, which can affect subsequent same-sender transactions in the block. The stateDiff-based detection (which checks only balances/storage against expected fees) would not catch this. Please clarify whether nonce changes are ignored by the operational check and argue (or bound) why this does not matter in practice — e.g., bots rarely send multiple dependent transactions per block.
  4. [Table 3 header] The column is headed 'Earnings [ETH]' but §4.2.1–4.2.2 interpret the percentages as shares of *priority* fees (excluding base fee). On L2s, fees are split across three vaults (§3.1); please state precisely which fee components are summed in this column, and reconcile with Figure 2(c)/(d), which distinguishes 'Total Fee' from 'Priority Fee'.
  5. [§4.3.5 (profitability claims)] 'Most speculative bots are unprofitable' / 'speculative MEV is not the most profitable strategy' would be more precisely stated as *aggregate* profits being negative for speculative arbitrage on L2s (Table 6: −31.26 ETH Optimism, −28.07 ETH Base), while speculative liquidation remains strongly profitable in aggregate and ~21–26% of speculative bots remain individually profitable after costs. Please also state the revenue-estimation assumptions inherited from [10] (e.g., whether token amounts are valued at block-time prices, and whether L1 data-availability fees are included in costs).
  6. [§4.2.2 and passim] Grammar: 'successful state-invariant transactions are, on average, pay less' → 'pay less on average'. Typos: 'asscociated' (§1), 'medatada' (§2.4), 'large-sale' (§7), 'is label as' (§4.4).
  7. [Table 2 vs. Table 4] Arbitrage + liquidation bot counts (10,087 + 688 = 10,775) do not match Table 4's total of 10,769 bots for Base; a one-line note on how cross-strategy bots are counted would help.
  8. [Figures 2–3] Axis labels and legends are small and the four-panel fee figures would benefit from a log-scale or normalized variant given the Ethereum/L2 magnitude gap. Figure 1's 'Entropy Filter' step would be clearer if the filtered (too-short) amount were visually distinguished from the matched one.
  9. [General] Reproducibility: the paper would be substantially strengthened by releasing the state-invariant transaction dataset, the bot classifications, and the classification code. The methodology is described at a level that permits re-implementation, but the headline discrepancies above illustrate the value of artifacts that let readers verify the numbers directly.

Circularity Check

0 steps flagged · score 0.0 of 10

Empirical measurement paper with an operational definition and independent on-chain counts; no derivation reduces to its inputs by construction.

full rationale

The paper’s central quantities (counts of state-invariant transactions, shares attributed to speculative/non-speculative/hybrid MEV, profitability after fees, address-poisoning volume) are obtained by applying an operational definition to archive-node stateDiff traces and by labeling bots via a calldata-matching heuristic, then tallying observed transactions and fees. State-invariance is defined as ‘removal leaves ledger state unchanged beyond fees’ and checked against trace_replay_block_transactions stateDiffs; it is not derived from the spam percentages later reported. Speculative vs non-speculative labeling is a stated heuristic (token amounts in calldata under an entropy filter), not a fitted parameter renamed as a prediction, and profitability is revenue minus observed fees rather than a residual forced by construction. Self-citations (Torres et al. MEV detectors; Messias et al. inscription methodology; Tsuchiya et al. poisoning dataset) supply prior detectors and labels used as measurement instruments; they do not import a uniqueness theorem or ansatz that forces the 1.4B count or the L2 dominance claims. Numerical inconsistencies between abstract percentages and Tables 3–4 are a correctness/reproducibility issue, not circularity. No self-definitional loop, fitted-input-as-prediction, or load-bearing self-citation chain is present.

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

The central claims rest on an operational definition of spam, standard EVM state semantics, fee accounting differences between L1 and OP-stack L2s, and two classification heuristics (calldata amount matching; external poisoning/inscription labels). One explicit free parameter sets the false-positive floor for amount matching. No new physical entities; the invented construct is the measurement category itself.

free parameters (2)
  • calldata amount-match false-positive rate f = 10^{-6}
    Minimum hex length m ≥ ceil(log16(L/f)) gates which token amounts may match calldata; authors set f=10^{-6}. Changes which successful MEV txs are labeled non-speculative and thus bot category mixes and spam attribution shares.
  • non-MEV bot activity thresholds = 10000 txs; >99% state-invariant
    Contracts with ≥10,000 state-invariant txs and (for message-passing) >99% successful txs state-invariant, plus unverified-source filter, define which destinations are treated as bots vs infrastructure.
assumptions (5)
  • ad hoc to paper A transaction is state-invariant iff removing it leaves S_B identical ignoring proposer/fee-vault balance changes exactly equal to fees; all reverted contract txs qualify.
    Core definition in §3.1; equates spam with fee-only stateDiff. Standard EVM semantics underneath, but the spam identification choice is paper-specific.
  • domain assumption Presence of at least one eligible dynamic token amount in calldata indicates off-chain precomputation (non-speculative); absence indicates speculative on-chain search.
    §3.2.1 labeling rule. Motivated but not proved; fails under obfuscation/encryption as authors note.
  • domain assumption OP-stack fee splits (base, sequencer/priority, L1 data) land only in known fee vaults; matching sender+vaults with exact fee deltas is necessary and sufficient for fee-only state change.
    §3.1 L2 methodology; depends on correct vault addresses and fee accounting for Optimism/Base.
  • domain assumption Torres et al. arbitrage/liquidation detectors (extended) adequately enumerate successful MEV for bot labeling and revenue.
    §3.2.1 and Table 2; missed MEV strategies would under-attribute state-invariant volume to MEV.
  • domain assumption Tsuchiya et al. address-poisoning labels (plus from/to expansion) are a valid ground set for poisoning attribution on Ethereum.
    §3.2.2–3.3; public set has a block gap; L2 poisoning inferred only via ETH attacker reuse.
invented entities (2)
  • state-invariant transaction (as spam metric) independent evidence
    purpose: Provide a strategy-agnostic, comparable measure of computational/on-chain spam across EVM chains.
    New named category defined by fee-only stateDiff; independent_evidence is operational (replay/stateDiff checks), not a new on-chain object.
  • hybrid MEV bot category independent evidence
    purpose: Capture bots that mix off-chain amount hints with on-chain abort/adjust logic, missed by binary speculative labels.
    Classification bucket from the calldata heuristic applied per successful MEV tx; useful taxonomy, not a new mechanism.

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Cite this review

Pith. "Pith review of There Will Be Spam: Characterizing State-Invariant Transactions and Speculative MEV." pith.science (2026). https://pith.science/paper/DH3GEW2M

@misc{pith2026260724172,
  author       = {Pith},
  title        = {Pith review of: There Will Be Spam: Characterizing State-Invariant Transactions and Speculative MEV},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DH3GEW2M}},
  note         = {Machine review of arXiv:2607.24172}
}
read the original abstract

Blockchains rely on transparency and immutability to ensure trust, but these guarantees come at the cost of an ever-growing ledger that increasingly threatens decentralization by making it more expensive to store and maintain the full transaction history. In this work, we introduce state-invariant transactions, defined as transactions whose inclusion or removal does not affect the resulting blockchain state beyond transaction fees. We argue that these transactions constitute a form of on-chain spam because they consume execution, bandwidth, storage, and blockspace without contributing to the final ledger state. We present the first large-scale measurement of state-invariant transactions across Ethereum, Optimism, and Base, identifying nearly 1.4 billion such transactions. While only 2.6% of Ethereum transactions are state-invariant, they account for 24% of transactions on Optimism and 37% on Base, representing a significant source of unnecessary resource consumption on Layer-2 blockchains. We show that speculative Maximal Extractable Value (MEV) is the dominant source of state-invariant transactions on Optimism and Base, accounting for 57% and 68%, respectively, but is not the only source as previously assumed. Moreover, despite its popularity, speculative MEV is not the most profitable strategy once the costs of state-invariant transactions are considered. Beyond MEV, we identify substantial malicious activity, with address poisoning campaigns accounting for 53% of non-reverted state-invariant transactions on Ethereum. Our findings suggest that mitigating state-invariant transactions could substantially reduce blockchain resource consumption and transaction costs while limiting phishing campaigns and other forms of blockchain abuse.

Figures

Figures reproduced from arXiv: 2607.24172 by the authors.

Figure 1
Figure 1. Our methodology for distinguishing between speculative and non-speculative MEV is based on the [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Overview of state-invariant transactions over time across Ethereum, Optimism, and Base in terms of [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Overview of daily proportion of state-invariant transactions and gas consumption relative to the total [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Percentage of successful MEV transactions per MEV bot strategy across the three blockchains. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

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Reviewed July 31, 2026 · model on record in the stance chip above.