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REVIEW 2 major objections 6 minor 63 references

Concentration in Governance Control Across Decentralised Finance Protocols

T0 review · 2 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper shows that across 35 validated links between DeFi governance tokens, a small set of link-defining addresses — mostly institutional investors — holds up to 34% of combined token supply, so a coordinated group could influence…

desk verdict A credible descriptive map of cross-protocol governance-token concentration, with an underspecified permutation control that should be fixed before the significance claims are trusted. read the letter →

arxiv 2501.13377 v2 pith:HS5NTDRV submitted 2025-01-23 cs.CE

Thomas Eisermann , Carlo Campajola , Claudio J. Tessone , Andreia Sofia Teixeira This is my paper · ORCID
classification cs.CE
keywords DecentralisedFinancegovernancetokenscross-protocolinfluencestatisticallyvalidatednetworkstokenconcentrationinstitutionalinvestorsEthereumDAOs
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 asks whether governance power in Decentralised Finance is concentrated not just inside single protocols but across protocols, through addresses that hold governance tokens in several communities at once. It builds a statistically validated network of shared token holdings among 15 DeFi governance tokens and looks at the addresses that define each link. The result is that these link-defining groups are small, hold a high fraction of combined token supply — up to 34% in places — and are mostly labelled as institutional investors. For most links, institutions hold the majority of internal influence, so a coordinated block of addresses could in principle weigh on decisions in more than one protocol at the same time. The authors also find that influence is diluted during speculative upswings, when new buyers enter and core holders' share shrinks.

What carries the argument

The central object is the Statistically Validated Network (SVN) projection of a bipartite graph whose two node sets are governance tokens and the Ethereum addresses holding them. Links between tokens are kept only when the number of addresses holding both tokens is larger than expected under a hypergeometric null model after Bonferroni correction; the addresses that actually hold both tokens of a link are called link-defining addresses $A_{i,j}$. The argument is carried by two measures computed on those addresses: internal influence, the average fraction of token supply held by $A_{i,j}$ across the two tokens, and directional influence, the share of one token's supply held by the link-defining set. These measures translate shared holdings into a concrete statement about how much voting weight a small group could coordinate.

What would settle it

Re-run the permutation test with control addresses sampled only from addresses that hold at least one of the two tokens in a link, while keeping the control group the same size as the link-defining set. If the significance markers in Figures 3 and 8 disappear or weaken substantially, then the reported concentration is an artifact of comparing token holders with random Ethereum addresses rather than a property of the holders themselves.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that cross-protocol governance influence is real and concentrated: statistically validated links between governance tokens are formed by small sets of link-defining addresses, and those addresses hold a disproportionate share of both tokens. Across the 35 validated links studied, internal influence reaches up to 34% of the combined token supply, and the Gini coefficient of influence within most links is above 0.7, meaning a few addresses dominate even inside the linking group. Label analysis shows institutional addresses hold the majority of internal influence on most links, while protocol contracts, vesting contracts, and liquidity pools dominate particular pairs such as CVX-CRV. Links persist over time, and directional influence is usually concentrated on one token in the pair, giving the linking addresses asymmetric control across the two communities. The paper reads these patterns as evidence that token-based governance contains an underexplored risk vector: the same holders can influence several protocols at once.

Load-bearing premise

The paper's significance tests assume that the randomly chosen comparison addresses are comparable to the link-defining addresses; because the paper never states that the comparison addresses hold at least one of the two tokens, the reported gaps could be nearly automatic if the comparison group came from ordinary Ethereum addresses.

Editorial extensions

If this is right

  • If the link-defining addresses acted together, they could simultaneously shape governance decisions in both token communities of a link, because their combined holdings pass substantial voting thresholds in several cases.
  • Single-protocol studies of governance concentration underestimate the problem: the same institutions appear on both sides of validated links, so cross-protocol concentration is a distinct layer of control.
  • The dominance of institutional labels implies that profit-oriented entities, not retail users, hold most of the cross-protocol governance weight, creating potential conflicts with community interests.
  • Because directional influence is usually concentrated on one token per link, control between paired protocols is asymmetric — the CVX-CRV case shows one protocol dependency driving another's governance weight.
  • During market upswings, rising TVL dilutes the internal influence of core holders, meaning speculative inflows temporarily shift governance power away from long-term community members.

Reading between the lines

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

  • The authors measure holdings, not votes; linking the holder network to on-chain proposal votes would show whether the potential coordinated control is ever exercised.
  • If the control group for the permutation test is not required to hold at least one of the tokens, re-running the comparison with token-holding controls would show how much of the reported significance is an artifact of sampling ordinary Ethereum addresses.
  • The same network construction could be run on other chains or on multi-chain governance tokens, where shared holders may be even harder to attribute to a single community.
  • Joining holdings data with vesting schedules and contract labels could separate structural concentration (tokens locked in contracts) from discretionary concentration (wallets that can actively vote), which have different implications for governance risk.
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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

2 major / 6 minor

Summary. The paper studies cross-protocol governance concentration in DeFi by constructing a Statistically Validated Network (SVN) of governance tokens based on shared holder addresses across 18 monthly snapshots from January 2021 to June 2022. For each validated token pair, it defines the set of 'link-defining' addresses that hold both tokens and measures their 'internal influence' and 'directional influence' as fractions of token supply, together with link size, Gini coefficients, median wealth, and label-based entity composition. The authors report persistent influential links, internal influence values up to 34% of combined supply, a majority of internal influence held by institutional addresses, and negative correlations between internal influence and TVL changes. The paper also includes a sensitivity analysis and makes code and data publicly available.

Significance. If the descriptive findings hold, this is a useful contribution to the understudied topic of cross-protocol governance control in DeFi: most prior work analyzes token distributions within individual protocols, whereas this paper explicitly examines shared holders across protocols and quantifies the concentration of governance-relevant holdings in small address sets. The paper is transparent about data sources, presents a sensitivity analysis, publishes its code and data, and acknowledges the gap between token holdings and actual voting power. The main weaknesses are in the statistical validation layer: the permutation control group in §3.7 is not specified to hold either token, which undermines the significance markers in Figures 3 and 8, and the TVL correlations in Figure 9 are reported without multiple-comparison correction. These issues affect the inferential claims but not the raw descriptive concentration values, so the paper can be revised rather than rejected.

major comments (2)
  1. [§3.7, Eqs. (4)–(5), Figs. 3 and 8] The control group Acontrol is defined in §3.7 only as 'a set of randomly selected addresses, matched in size' to Ai,j, with no statement that controls hold at least one of the two tokens ti or tj. Because Internal Influence (Eq. 4) and Directional Influence (Eq. 5) are exactly zero for an address holding neither token, a permutation test that shuffles the combined set Ai,j ∪ Acontrol will almost always reject the null for any Ai,j with positive token holdings. As written, the significance markers in Figures 3 and 8 do not provide evidence that link-defining addresses are unusually influential; they largely reflect the fact that random addresses hold none of the relevant tokens. Please specify the sampling universe of Acontrol and re-run the permutation tests with controls matched on token-holding status (e.g., addresses holding at least one of the two tokens), or remove the inferential language and present the values as descriptive quantities.
  2. [§4.2.1, Fig. 9, Table D.1] The correlation analysis between internal influence and TVL percentage change reports 18 link-level tests without any multiple-comparison correction. At α=0.05, two of the 18 correlations are significant (CVX-SUSHI and YFI-LDO), which is close to the number of false positives expected by chance, so the statement that 'few correlations achieve statistical significance' is not informative and the negative-correlation result is not robust as presented. Please apply a correction such as Benjamini-Hochberg or explicitly label Figure 9 as exploratory. This point is load-bearing for the Discussion's claim that internal influence shifts with speculative market cycles.
minor comments (6)
  1. [§3.6, Eq. (4)] The Internal Influence metric is defined as the arithmetic mean of the fractional holdings in the two tokens; because token supplies differ by orders of magnitude, this average can be dominated by the smaller-supply token. Please state the rationale for the arithmetic mean rather than, for example, a supply-weighted average.
  2. [§4.1, Fig. 1] The Jaccard similarity in Figure 1 is computed on token-pair links, so the sentence that 'subsets of addresses contributing to the links are likely the same over time' is not directly supported by this figure. Please add an address-level overlap analysis or soften this inference.
  3. [§4.2.1 and Appendix D] The text says the TVL correlations are 'adjusted for auto-correlation by differencing' but does not describe the adjustment procedure; the reference to 'Appendix Table D.1' is incomplete. Please spell out the differencing/detrending steps and how the reported Durbin-Watson and Ljung-Box statistics were used.
  4. [§3.3 and Fig. 7] The label-based institutional finding depends on third-party labels from partly closed sources. Please report the number of addresses labelled, the coverage relative to the link-defining sets, and the rate of conflicts during triangulation, so readers can assess label quality.
  5. [Throughout] There are several typos and grammatical errors, for example 'the respective addresses already influential within existing Defi Token may were aware' in §4.2, 'governance right a predominantly held by few institutional investor' in §5, and 'favourtism' in §5. A careful proofreading pass is needed.
  6. [Abstract and §3.5] The phrase 'identify influential addresses that shape these connections' is somewhat circular: the link-defining addresses Ai,j are defined as the addresses holding both tokens, so the Internal Influence metric is computed on the very set that defines the link. The paper should clarify that the concentration is a property of the overlap set rather than an independent causal finding.

Circularity Check

1 steps flagged · score 2.0 of 10

The only genuinely circular element is the definitional identification of link-defining addresses as the 'shapers' of SVN links; the empirical concentration, label, and dynamics results are self-contained and not forced by construction.

  1. self definitional [Abstract and Section 3.5 (Eq. 3)]
    "Using the links within the SVN, we identify influential addresses that shape these connections ... We define this set of relevant addresses as the link-defining addresses, denoted by Ai,j: Ai,j = {a ∈ Na | a holds both ti and tj} (3) ... These link-defining addresses represent the addresses that simultaneously hold both tokens ti and tj, thereby contributing to the formation of the statistically validated link between these tokens."

    The SVN links are defined by the set of addresses holding both tokens; Eq. 3 defines A_i,j as exactly that set. Therefore identifying A_i,j as the addresses that 'shape these connections' restates the definition of the link rather than deriving a new empirical fact. The circularity is limited to this framing: the numerical influence values, label composition, persistence, and TVL correlations are computed from independent data and do not follow from the definition.

full rationale

The paper's main contribution is descriptive: it builds an SVN from shared governance-token holdings, defines link-defining addresses as those holding both tokens of a validated link, and measures their combined share of supply (internal and directional influence), label composition, persistence, and correlation with market TVL. These measurements are self-contained against external token-holding and labelling data; they do not fit a parameter and then predict a closely related quantity. No load-bearing self-citation chain is present: the SVN method is imported from Tumminello et al. [7] and Gualdi et al. [8], which are external works, and the authors' own prior publications appear only in background discussion and are not used to justify the central inference. The one genuinely definitional element is the abstract's phrasing that the SVN links are used to 'identify influential addresses that shape these connections': by Eq. 3, A_i,j is defined as the set of addresses holding both tokens, so these addresses shape the connection by construction. This framing is circular but minor, because the paper does not treat the existence of A_i,j as a prediction; the reported concentration values, institutional-label majority, and time dynamics are empirical. The permutation control in Section 3.7 is underspecified: A_control is only 'matched in size', and if it is not drawn from addresses holding at least one of the two tokens, Eq. 4 gives a zero control metric and the significance markers in Figures 3 and 8 would be forced. Since the sampling frame is not stated, this is a validity or reproducibility risk rather than a demonstrated circular reduction, and it does not overturn the descriptive results. Overall, the central claim has independent content and the circularity burden is low.

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

The analysis rests on a standard network null model, on external address labels, and on the equivalence between token holdings and governance influence. The latter is acknowledged in the paper as approximate. No new physical or theoretical entities are introduced.

free parameters (4)
  • token selection thresholds = MCAP > 200M USD or TVL > 300M USD
    Used to shortlist 15 governance tokens (Section 3.1); changing these thresholds changes the sample and all downstream links.
  • supply threshold for link-defining wallets = 0.0005% (5e-06) of token supply
    Addresses holding less are excluded from metric calculations; sensitivity analysis in Appendix E shows stability, but the threshold is chosen by hand.
  • minimum link occurrences = 9 snapshots
    Filter used to collapse the time dimension in Figures 4, 5, and 6; this is an arbitrary persistence cut.
  • SVN significance level = alpha = 0.01 with Bonferroni correction
    Standard statistical threshold chosen by convention, not fitted to data.
assumptions (4)
  • domain assumption Random connectivity null model for token-address links (hypergeometric distribution, Eq. 1)
    Assumes shared holdings arise by chance given address and token degree; misspecification would produce spurious validated links.
  • domain assumption Governance token holdings approximate governance control
    Authors explicitly state holdings do not consistently reflect decision-making power (Section 5), yet the title and conclusions use 'governance control'.
  • domain assumption Address labels from EtherScan, Nansen, and Arkham are accurate after triangulation
    The institutional classification of link-defining addresses (Figure 7) relies on these labels; mislabeling would change the main finding.
  • domain assumption The 15 selected protocols are representative of DeFi governance
    Selection criteria from [3] plus manual refinement; no claim of completeness, but general conclusions about DeFi rely on this sample.

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Cite this review

Pith. "Pith review of Concentration in Governance Control Across Decentralised Finance Protocols." pith.science (2026). https://pith.science/paper/HS5NTDRV

@misc{pith2026250113377,
  author       = {Pith},
  title        = {Pith review of: Concentration in Governance Control Across Decentralised Finance Protocols},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HS5NTDRV}},
  note         = {Machine review of arXiv:2501.13377}
}
read the original abstract

Blockchain-based systems are frequently governed through tokens that grant their holders voting rights over core protocol functions and funds. The centralisation occurring in Decentralised Finance (DeFi) protocols' token-based voting systems is typically analysed by examining token holdings' distribution across addresses. In this paper, we expand this perspective by exploring shared token holdings of addresses across multiple DeFi protocols. We construct a Statistically Validated Network (SVN) based on shared governance token holdings among addresses. Using the links within the SVN, we identify influential addresses that shape these connections and we conduct a post-hoc analysis to examine their characteristics and behaviour. Our findings reveal persistent influential links over time, predominantly involving addresses associated with institutional investors who maintain significant token supplies across the sampled protocols. Finally, we observe that token holding patterns and concentrations tend to shift in response to speculative market cycles.

Figures

Figures reproduced from arXiv: 2501.13377 by the authors.

Figure 1
Figure 1. Jaccard similarity of the SVNs over time. [PITH_FULL_IMAGE:figures/full_fig_p011_1.png] view at source ↗
Figure 2
Figure 2. Distribution of the number of addresses across the sampling period [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Influence exerted by addresses within token links. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: A collapsed time view of internal influence metrics across token [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Gini coefficient showing the inequality of influence within token [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: Median wealth held by link-defining addresses across all tokens in [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Relative Internal Influence of link-defining addresses by entity [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: Directional influence within token pairs in validated links on the [PITH_FULL_IMAGE:figures/full_fig_p018_8.png]
Figure 9
Figure 9. Figure 9: Correlation between Internal Influence of Link Pairs and Percent [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.