Incentives and Market Structure in Intent-Based Exchanges: Evidence from a Solver-Reward Reform

Ruiyang Zhang

REVIEW 3 major objections 5 minor 9 references

Incentives and Market Structure in Intent-Based Exchanges: Evidence from a Solver-Reward Reform

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A solver-reward reform reallocated CoW Protocol trading value by order size, monotone from small to large orders.

desk verdict An honest, carefully-identified empirical paper on how solver reward rules reshape value capture in intent-based DEXes; the central monotone order-size gradient is real and robust, but the cross-venue placebo doesn't cover the outcome dimension that carries the claim. read the letter →

arxiv 2607.21955 v1 pith:Z7X7T3YU submitted 2026-07-24 cs.CR

Ruiyang Zhang This is my paper · ORCID

classification cs.CR

keywords intent-basedexchangessolverrewardsmarketconcentrationnaturalexperimentCoWProtocoldecentralizedfinanceexecutionqualityorderflowauctions

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 a governance change in how solvers are paid can causally reallocate who captures trading value on an intent-based exchange. The specific claim is that CoW Protocol's CIP-74, which tied solver rewards to protocol revenue and added an ad-valorem volume fee, made concentration fall among small orders and rise among large orders, monotonically across four order-value buckets. The monotone gradient (Spearman rho = 1.00, exact permutation p = 0.042) survives dropping the largest solver, while trade-count concentration fell and average execution quality did not move by more than about 7 basis points. If this is right, the design of solver rewards is a first-order determinant of market structure in intent-based trading, even when users' average prices are unchanged.

What carries the argument

The central object is the payment rule introduced by CIP-74: the solver reward cap becomes proportional to the protocol revenue of the winning solution, plus an unconditional volume fee of about 2 basis points. The model's Proposition 1 states that the ad-valorem fee enters every solver's payoff identically and therefore cannot change who wins; Proposition 2 states that the revenue-linked cap makes the marginal return to winning increase with order value, so solvers whose costs grow slowly with size are most advantaged on large orders. The empirical machinery that carries the argument is a within-bucket interrupted-time-series regression estimating a level break in volume-HHI at the reform date, with the monotonicity of the four bucket breaks tested by exact permutation and re-checked after deleting the largest solver.

What would settle it

The central claim would be falsified if re-estimating the four bucket-level HHI breaks while excluding the single largest solver and the largest 1% of trades no longer yields a monotone gradient with permutation p < 0.05, or if more than 5% of placebo dates chosen in the pre-reform window produce a similarly perfect monotone gradient.

Watch

Extended reading notes

Core claim

The paper reports that CoW Protocol's CIP-74, effective 8 December 2025, caused a reallocation of trading value by order size: volume-HHI level breaks were -0.086 in the 0-1k bucket, -0.052 in 1k-10k, +0.025 in 10k-100k, and +0.085 in 100k+, a perfect rank correlation between order-size bucket and break (Spearman rho = 1.00; exact permutation p = 0.042) that persists when the single largest solver is excluded. Aggregate volume-weighted HHI rose from 0.176 to 0.241, but the paper shows that aggregate movement is substantially carried by the incumbent top solver and treats the order-size gradient as the load-bearing result. By trade count the market de-concentrated (count-HHI -0.060), and average execution-quality changes are bounded below roughly 7 basis points and statistically indistinguishable from zero. The authors explain the pattern with a model in which the revenue-linked reward cap raises the marginal payoff to winning large orders for inventory-rich solvers, while the ad-valorem fee is competitively neutral.

Load-bearing premise

The load-bearing premise is that, absent CIP-74, CoW Protocol's order-size concentration would have followed its pre-reform flat-to-declining trend and that no other CoW-specific shock, such as anticipatory solver behavior, token-price moves, or order-flow mix changes, coincided with the 8 December 2025 effective date.

Editorial extensions

If this is right

Solver-reward parameters that look like accounting details are first-order determinants of which agents capture intent-market value.
A revenue-linked reward cap can entrench inventory-rich incumbents in the large-order segment without reducing the number of active solvers or raising trade-count concentration.
The cross-venue placebo places the concentration break solely at CoW's reform date, implying the effect is not a synchronized market-wide event.
The bounded execution-quality null implies the reallocation happened without a detectable change in the average price users received, so the immediate user-side cost is small even as market structure shifted.
The directionally consistent but underpowered triple-difference on the February 2026 fee cut leaves open whether a partial rollback can reverse the concentration.

Reading between the lines

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

If the revenue-linked cap mechanism generalizes, other intent-based venues that index solver rewards to revenue should show a similar monotone order-size gradient when their reward rules change; this is a testable prediction for future governance events on UniswapX or 1inch Fusion.
The count-volume divergence suggests that concentration metrics based on trade counts alone can miss value-level reallocation, so dashboards and oversight for intent markets may need to track both dimensions.
The bounded mean-level execution-quality null leaves open distributional effects: large-order users could have experienced worse execution even if the average did not move, and a size-bucketed execution-quality decomposition would test that.
The failure of a partial fee rollback to undo concentration hints at path dependence in solver-market structure, which could make later corrective governance, such as consistency rewards, harder to assess without the same event-study machinery.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Ruiyang Zhang's paper is one of the first genuinely causal looks at how a reward-rule change in an intent-based DEX reshapes who captures value. The natural experiment is well chosen — a governance-dated change (CIP-74) with a clean effective date, public data, a control venue, and a real mechanism model. The headline monotone order-size gradient (small orders de-concentrate, large orders concentrate) survives dropping the largest solver, which is the right robustness test. The paper's design-based inference — permutation tests, placebo-in-time, changepoint location — is honest, and the authors explicitly downgrade the HAC p-values and disclose the one-solver caveat. That's real discipline.

The soft spots are the usual ones for observational work. The aggregate HHI break is carried by one solver; the authors know this and pivot to the gradient, which is what should be judged. The second-event triple-difference is a bounded null — directionally consistent, underpowered. Execution-quality null is bounded at ~7 bps. These are correctly framed.

The stress-test gap is the one thing I'd want addressed before publication: the cross-venue placebo on UniswapX validates only aggregate HHI. It does not show whether UniswapX exhibited the same four-bucket order-size gradient. That matters because the central claim is precisely the gradient. If a market-wide large-order shock coincided with the reform — a December price move, institutional flow, something affecting big orders broadly — both CoW and UniswapX would show the same within-bucket breaks even though only CoW changed its reward rule. The authors rule out a synchronized aggregate break, but not a synchronized order-size gradient. That's a proportionate concern: the timing, changepoint, and leave-one-solver-out evidence make the causal story plausible, but the one control venue available to test the load-bearing outcome dimension wasn't run on that dimension. I'd call this a fixable gap rather than a fatal flaw — run the bucket-level ITS on UniswapX and report it.

Citation pattern looks honest; no self-citation inflation, and the relevant empirical and theoretical work (Bachu, Yuminaga, Chitra et al.) is engaged rather than ignored. Replication package is committed with frozen data, which is more than most.

Who is this for? DeFi market design, protocol governance, and anyone working on auction-based execution venues. It's a solid empirical paper — the kind that deserves refereed journal/conference time rather than a desk reject. My recommendation: send it to peer review, with the UniswapX order-size gradient as a required addition.

Referee Report

3 major / 5 minor

Summary. The paper estimates the causal effect of CoW Protocol's CIP-74 solver-reward reform (effective 8 December 2025) on market concentration and execution quality, using 395 days of public on-chain data. It reports a sharp aggregate rise in volume-weighted HHI, a monotone order-size gradient in within-bucket HHI breaks (small orders de-concentrate, large orders concentrate), no increase in trade-count HHI, and no detectable change in average execution quality. The identification strategy combines interrupted time series, changepoint detection, placebo-in-time, a cross-venue placebo (UniswapX), leave-one-solver-out, an exact permutation test on monotonicity, and a triple-difference around a February 2026 fee cut. The paper's stated central claim is that CIP-74 reallocated trading value by order size, with the order-size gradient as the result that survives dropping the largest solver.

Significance. If the causal interpretation holds, this is the first causal evidence on how solver-reward rules shape competitive structure in intent-based exchanges, providing an empirical counterpart to restricted-entry theory. The paper is unusually transparent: it ships a replication package, uses public data, relies on exact permutation inference for the headline monotonicity result, explicitly downgrades anti-conservative HAC p-values, and appends a candid Limitations section. The main weakness is that the causal identification for the central result is incomplete: the cross-venue placebo tests only aggregate concentration, not the order-size gradient that the paper itself designates as load-bearing, and the fixed USD bucket definitions introduce a potential price-composition confound. These gaps are fixable with additional analysis of the existing control-venue data, so the paper merits revision rather than rejection.

major comments (3)

[§6.3, §6.4] The cross-venue placebo is run only on aggregate HHI, whereas the paper's central claim, stated in §1 and §6.4, is the monotone order-size gradient. UniswapX is the only firm-level control venue, yet the paper never computes the same four-bucket within-segment HHI breaks, or a permutation test for a monotone gradient, on UniswapX. A market-wide large-order shock (e.g., a broad price move, a shift in institutional order flow, or a change in large-order composition) could produce the identical within-bucket pattern on a venue that had no reward reform. Because the mechanism test in §6.4 is run only on CoW, the causal claim that the gradient is specific to CIP-74 is not directly placebo-tested. Please either present the UniswapX order-size gradient and show it is flat or non-monotone, or explicitly restrict the causal claim to 'conditional on no concurrent large-order market shock' and assess the resulting threat. Limitation 3 acknowledges only the scarcity of control venues, not this specific missing check.
[§4, §6.4] The four order-value buckets are fixed USD intervals, and no adjustment is made for token-price movements or for the resulting reclassification of trades across bucket boundaries over time. The paper reports that the incumbent's mean large-trade size rises from $316k to $435k over the sample, an increase of roughly 37%; if this reflects broad price appreciation rather than a change in order composition, the composition of the 100k+ bucket is not stable. A price-driven reclassification could mechanically change within-bucket HHI even absent any solver response, and the current design does not control for this or placebo-test it on the control venue. Please report bucket shares over time, deflate USD values by the relevant token price, or otherwise demonstrate that the bucket-level HHI breaks are not an artifact of nominal bucket boundaries.
[§6.1, §6.2] The aggregate timing evidence is presented as 'time-locked' in the abstract, but the design-based evidence is borderline: placebo-in-time p-values are approximately 0.05 for HHI and 0.09 for top-solver share, and the dominant changepoint lands 2-4 days after the effective date. Since the paper itself argues that the HAC p-values are anti-conservative, the aggregate 'time-locked to CIP-74' claim rests on a single marginally significant permutation p-value. This is not fatal because the paper correctly leads with the order-size gradient, but the abstract and conclusion should qualify the aggregate claim as suggestive, and the discussion should address why a 2-4 day lag is consistent with the governance-effective date rather than with a delayed implementation or an unrelated event.

minor comments (5)

[§1 (Abstract)] The phrase 'reshapeswho captures value' in the abstract and introduction is missing a space; it should read 'reshapes who captures value.'
[Header] The manuscript retains the 'Conference’17, July 2017, Washington, DC, USA' template header, which should be removed for a journal submission.
[§2.1, Eq. (1)] Equation (1) uses the reward cap as if it were the exact reward, but the text describes it as a cap. Please clarify whether the cap is binding in all auctions or only for high-revenue solutions, and how non-binding cases are handled in the model and the empirical predictions.
[§6.6] The 10k+ triple-difference result (δ≈-0.103, clustered p≈2e-9) is dismissed because randomization-in-time p≥0.26, while the 100k+ result is reported at clustered p=0.099. The text would benefit from a more explicit statement of why randomization-in-time is the design-matched inference and why the clustered p-value is not used for the headline interpretation.
[§3] The phrase 'An adversarial novelty search found no prior causal/event-study analysis' is vague; if such a search was performed, describe its scope, or remove the claim to avoid an unfalsifiable assertion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the empirical design, model parameters, and inference are not derived from the outcome being explained.

full rationale

The paper's central claim is an empirical causal estimate: CIP-74 reallocated value concentration by order size, with a monotone gradient. The derivation chain is not circular. The solver-economics model in §2.1 takes beta and phi as protocol constants and assumes a cost-curve shape; none of these is fitted to the observed HHI breaks. The three testable predictions (value-not-count monotonicity, fee-cut reversal, causal counterpart to Chitra et al.) are stated before the tests and are falsifiable. The monotone gradient is an empirical outcome, not an input: the exact permutation test on Spearman's rho uses the pre-specified four buckets and the observed breaks. The triple-difference is reported as a bounded null (delta = -0.042, underpowered), not as confirmation, so it does not smuggle the conclusion into the design. There are no load-bearing self-citations: all cited theory and methods are external (Chitra et al. 2024; Newey-West 1987; Abadie et al. 2010; Card and Krueger 1994). The paper is candid about its weaknesses: Limitation 1 states the aggregate break is carried by one solver and the load-bearing result is the leave-one-solver-out gradient; Limitation 2 states the reward channel is bounded, not confirmed; Limitation 3 acknowledges a single firm-level control. The skeptic's concern that the cross-venue placebo validates only aggregate HHI, not the order-size gradient, is a validity/identification gap, not a circularity: it does not make the outcome equal to an input or reduce a prediction to a fit. The derivation is self-contained against its stated assumptions, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper has no fitted free parameters: beta and phi are protocol constants, and the bucket cutoffs are institutional choices. The load-bearing assumptions are economic shape assumptions and the natural-experiment identification premise. The model's predictions are falsifiable and are not tuned to the observed breaks.

assumptions (3)

domain assumption Bertrand competition in offered surplus: the lowest-cost solver wins, and an ad-valorem fee enters every solver's payoff identically.
Invoked in Section 2.1 Proposition 1 to establish fee neutrality and to identify the winner as the lowest-cost solver; this competitive model is not directly tested.
domain assumption Inventory-rich incumbents have cost curves c_i(v) that grow slowly in v, so their cost advantage is largest on large orders.
Invoked in Section 2.1 Proposition 2 and it carries the monotone-in-value prediction; this is an assumed shape, not estimated from data.
domain assumption No CoW-specific confounder at the CIP-74 effective date, and the pre-period trend would have continued absent treatment.
Identification premise for the interrupted-time-series design in Section 5, defended in Sections 6.2 and 6.3 through changepoint, placebo-in-time, and cross-venue placebo evidence.

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Cite this review

Pith. "Pith review of Incentives and Market Structure in Intent-Based Exchanges: Evidence from a Solver-Reward Reform." pith.science (2026). https://pith.science/paper/Z7X7T3YU

@misc{pith2026260721955,
  author       = {Pith},
  title        = {Pith review of: Incentives and Market Structure in Intent-Based Exchanges: Evidence from a Solver-Reward Reform},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Z7X7T3YU}},
  note         = {Machine review of arXiv:2607.21955}
}

read the original abstract

Intent-based decentralized exchanges delegate execution to a competitive class of agents -- solvers -- whose behavior is shaped by protocol-designed reward rules. We measure how a change to those rules reshapes who captures value, using a governance-dated natural experiment: CoW Protocol CIP-74 (effective 8 December 2025), which replaced a fixed solver-reward cap with one tied to protocol revenue and introduced an ad-valorem volume fee. Using daily solver shares over 395 days, we find the reform reallocated trading value by order size. The robust signature is a monotone size gradient: concentration fell in small orders and rose in large ones across four order-value buckets (Spearman rho=1.00, exact permutation p=0.042) -- a pattern that survives dropping the single largest solver. Aggregate concentration also rose (volume-weighted HHI 0.176->0.241), substantially carried by the incumbent top solver. By trade count the market de-concentrated (count-HHI -0.060). A simple solver-economics model rationalizes the pattern: an ad-valorem fee is competitively neutral, while a revenue-linked reward cap raises the marginal payoff to inventory-rich solvers on large orders -- consistent with restricted-entry predictions (Chitra et al. 2024). A control venue (UniswapX) shows no matching break. We detect no change in average execution quality (~7 bps bound). A triple-difference exploiting a February 2026 fee cut is directionally consistent but underpowered. Reward design measurably reallocates who captures value in intent markets, without moving the average price users receive.

Figures

Figures reproduced from arXiv: 2607.21955 by the authors.

**Figure 1.** CoW solver concentration (HHI and top-solver share), with CIP event lines and the detected 12 December changepoint. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 2.** ITS level break by order-size bucket: small orders de-concentrate; 100k+ concentrates. The gradient is monotone [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Cross-venue context: CoW vs. UniswapX vs. market [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Review history (2 revisions) →

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Reference graph

Works this paper leans on

9 extracted references · 5 canonical work pages

[1]

Quantifying Price Improvement in Order Flow Auctions

B. Bachu, X. Wan, C. C. Moallemi. “Quantifying Price Im- provement in Order Flow Auctions. ” 2024. arXiv:2405.00537

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An Analysis of Intent-Based Markets

T. Chitra, K. Kulkarni, M. Pai, T. Diamandis. “An Analysis of Intent-Based Markets. ” 2024. arXiv:2403.02525

arXiv 2024
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Y. Yuminaga, D. Chen, D. Sui. “Execution Welfare Across Solver-based DEXes. ” 2025. arXiv:2503.00738

arXiv 2025
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CoW DAO. CIP-67, CIP-72, CIP-74 (“Align solver rewards with protocol revenue and introduce a volume-based fee”), CIP-74 Retrospective, and CIP-85. CoW Protocol governance forum (forum.cow.fi) and docs.cow.fi, 2025–2026

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Quantifying Price Improvement in Order Flow Auctions

B. Bachu, X. Wan, C. C. Moallemi. “Quantifying Price Im- provement in Order Flow Auctions. ” 2024. arXiv:2405.00537

work page Pith review arXiv 2024

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An Analysis of Intent-Based Markets

T. Chitra, K. Kulkarni, M. Pai, T. Diamandis. “An Analysis of Intent-Based Markets. ” 2024. arXiv:2403.02525

arXiv 2024

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Execution Welfare Across Solver-based DEXes

Y. Yuminaga, D. Chen, D. Sui. “Execution Welfare Across Solver-based DEXes. ” 2025. arXiv:2503.00738

arXiv 2025

[4] [4]

Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance

B. Öz, C. Ferreira Torres, C. Schlegel, B. Mazorra, J. Gebele, F. Rezabek, F. Matthes. “Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance. ” 2025. arXiv:2501.17335

arXiv 2025

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CoW DAO. CIP-67, CIP-72, CIP-74 (“Align solver rewards with protocol revenue and introduce a volume-based fee”), CIP-74 Retrospective, and CIP-85. CoW Protocol governance forum (forum.cow.fi) and docs.cow.fi, 2025–2026

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A. Barzykin, P. Bergault, O. Guéant. “Algorithmic market making in dealer markets with hedging and market impact. ” Mathematical Finance 33(1):41–79, 2023. arXiv:2106.06974

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