Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals

Akshay SP; Ashutosh Sahoo; Dhanashekar Kandaswamy; Girish G N; Gurukiran S; Parag Paul

REVIEW 4 major objections 4 minor 1 cited by

Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that a wallet's Uniswap v3 behavior can be summarized by two blueprint-guided scores—liquidity provision and swap behavior—refined by a deep residual network, and that high-scoring wallets exhibit disciplined…

desk verdict A real engineering contribution with a self-referential evaluation: the dual-score system trains and tests on its own blueprint, so the behavioral-validity claims do not yet hold. read the letter →

arxiv 2507.20494 v1 pith:2ISLXPSK submitted 2025-07-28 q-fin.GN cs.LG

Dhanashekar Kandaswamy , Ashutosh Sahoo , Akshay SP , Gurukiran S , Parag Paul , Girish G N This is my paper · ORCID

classification q-fin.GNcs.LG

keywords DeFizScorewalletreputationUniswapv3liquidityprovisionbehavioralscoringdeepresidualnetworkon-chainbehavior

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 proposes a way to score DeFi wallets on Uniswap v3 without any identity data, using two complementary numbers: a Liquidity Provision Score and a Swap Behavior Score. The scores start from hand-built 'blueprint' rules that decompose behavior into volume, frequency, holding time, and withdrawal patterns, then a deep residual neural network is trained on noise-augmented blueprint scores to smooth out the rules and capture feature interactions. Pool context—TVL, fee tier, pool size—is folded in so the same behavior counts differently in a deep, low-fee pool versus a shallow, high-fee one. The authors report that on held-out wallets, 91.79% of LP scores and 90.83% of swap scores fall within ±50 points of the blueprint targets, and that higher-scoring wallets show stronger liquidity retention, longer holding, and broader token engagement. The larger claim is that these scores can support risk assessment, airdrop design, and governance weighting in DeFi.

What carries the argument

The load-bearing machinery is a blueprint-guided, noise-augmented supervised scoring pipeline: a hand-specified, weighted, per-category-capped scoring schema for LPs and swappers, plus Gaussian noise injection that creates soft training targets, plus a DeepMLP_ResNet (four residual blocks with projection shortcuts, layer normalization, dropout, and a regression head) that learns to reproduce those targets under wallet-level train/validation splits. The blueprint supplies interpretability and structure; the network supplies smooth, context-aware generalization; and pool-level features (TVL, fee tier, relative pool size) are appended so identical behavior is scored differently across pool environments.

What would settle it

Take a training window of Uniswap v3 activity, score wallets with zScore, then follow the same wallets in a later window on an outcome the blueprint does not use—say, whether they still hold liquidity after 90 days, whether they repay a DeFi loan, or whether their realized LP returns after gas and impermanent loss are positive. If top-zScore wallets do not outperform bottom-zScore wallets on those held-out outcomes, the claim that the score ranks strategic behavior is falsified.

Watch

Extended reading notes

Core claim

The central discovery is that a two-phase hybrid—interpretable rule-based blueprint followed by a noise-injected deep residual network with U-Net-style dense skip blocks—can produce a continuous 0–1000 behavioral score for Uniswap v3 wallets that differentiates roles and aligns with the authors' definition of strategic behavior. The LP score rewards long holding, low withdrawal, and high retention; the swap score rewards volume, frequency, token diversity, and routing complexity while penalizing micro-swaps and wash-like patterns. On their validation set, high-score bins concentrate exactly those behaviors: retention ratios rise from 0.00 in the lowest LP bin to 1.00 in the highest, and swap volumes climb from $4K to $82M across swap bins. The paper frames this as a bridge between anonymous on-chain interaction and trust-based protocol alignment.

Load-bearing premise

The hand-set blueprint weights and caps are assumed to be a valid measure of wallet reputation, and nothing outside the blueprint is used to check that assumption.

Editorial extensions

If this is right

Liquidity and swap scores give protocols a cheap, on-chain-only way to segment users by role—LP or trader—without any identity verification.
According to the bin tables, high-zScore wallets retain liquidity longer and trade more broadly, so the score could be used to gate airdrops, weight governance votes, or adjust lending terms toward users with stable behavior.
The noise-injected blueprint pipeline turns a fixed rule system into a differentiable scoring function, allowing pool context to be updated as markets shift without re-specifying all thresholds.
The architecture is reusable: swapping the blueprint to another role or protocol, such as lending or borrowing, yields a comparable behavioral score from the same network structure.

Reading between the lines

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

The reported 91% accuracies measure agreement with the authors' own rubric, not truth about reputation; a real test would compare zScore against external outcomes such as default rates, realized LP profitability, or continued participation after a market shock.
The deliberate exclusion of one-transaction wallets may inflate the observed score-behavior gradient, since the lowest-activity band is removed before bin analysis; re-including these wallets could change the shape of the tables.
Pool-level context suggests a natural stress test: check whether wallet rankings stay stable when a pool changes fee tier or experiences a sharp TVL change, to see whether the score reflects durable behavior or merely current pool conditions.
The dual-score output could be treated as a two-dimensional behavioral embedding, allowing clustering and anomaly detection to run directly on the score pair rather than on raw transaction features.

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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

If you need a practical wallet-ranking system for Uniswap v3, this paper gives you a clear, working recipe: two rule-based blueprint scores (LP and swap), noise-injected labels, and a residual MLP with pool context. The engineering is described well, the dataset is real on-chain data, and they point to an external validation report on GitHub. That is worth credit. The U-Net framing is cosmetic—it is a plain MLP with skip connections—but the architecture is sensible for the task.

The soft spot is the one the stress-test flags, and it is load-bearing. Every accuracy claim in Section 4 compares the network's output to the blueprint scores that were used to generate the training targets. Getting 91% agreement there shows the model fits its own labels, not that the scores capture anything externally meaningful. The bin-wise behavioral tables in Section 4.3 are also entailed by construction: they use features like holding duration, withdrawal volume, and token diversity that are literally components of the blueprint. So the monotonic trends are not discovered, they are baked in.

There are no baselines, no error bars, and the blueprint weights are hand-set without any external outcome (default, creditworthiness, future returns) to justify them. The paper is honest that the blueprint is the target, but the conclusion overreaches when it says the scores align with 'strategic behavior patterns.' What we actually know is that the scores align with the authors' rubric. That may be useful for internal segmentation, but it is not validation.

One more thing: the paper asserts independence from Udupi et al. [16], but the author lists overlap heavily (Sahoo, Paul, and others appear on both). That is not a scientific flaw, but it is misleading and a referee should ask for a clearer statement of what 'independently developed' means here.

So: the paper is a decent technical report for practitioners building DeFi risk or airdrop tools, not a demonstrated scientific result about wallet reputation. I would send it to peer review only if the venue is willing to demand external validation or reframing; as it stands, the central claim is unsupported. A serious referee could get something useful out of it, but the paper needs major revision before it should be published as anything more than a systems note.

Referee Report

4 major / 4 minor

Summary. The paper proposes a dual-role behavioral scoring framework for Uniswap v3 wallets, producing a Liquidity Provision Score and a Swap Behavior Score. Scores are first generated by a hand-designed rule-based blueprint (Section 3.1), then Gaussian noise is added to these scores to create training targets, and a deep residual network (DeepMLP_ResNet) is trained to regress toward those noisy targets (Section 3.2). The empirical evaluation in Section 4 compares model predictions to the blueprint scores and reports 91.79% / 90.83% agreement within a ±50 tolerance, and presents bin-wise behavioral summaries in Tables 1-2 as evidence that higher scores correspond to more strategic behavior. The conclusion claims that zScores align closely with strategic behavior patterns and that high-scoring users exhibit strong liquidity retention, disciplined trading, and broad engagement.

Significance. If the claimed behavioral validity were established, an interpretable, context-aware, dual-role scoring system for DeFi wallets could be a useful building block for risk models, incentive design, and reputation systems in decentralized finance. The paper has some strengths: the blueprint is explicit and interpretable, the architecture is described in detail, and the authors provide a link to a supplementary validation report and code repository. However, the central empirical claim is not supported by the evidence presented: the 'ground truth' used for evaluation is the same blueprint score that generated the training labels, so the reported accuracy is a measure of how well the model fits its own training targets. The bin-wise behavioral trends in Tables 1-2 are also largely entailed by the features and weights built into the blueprint rather than independently discovered. No external outcome, baseline model, or statistical uncertainty is provided. Therefore, the paper's main contribution, as stated in the conclusion, is an unvalidated assumption rather than an empirically confirmed result.

major comments (4)

[Section 4.1 and 4.2 (Figures 2 and 3)] The evaluation is circular. The 'ground truth' scores in Figures 2-3 are the blueprint scores from Section 3.1, which were then corrupted with Gaussian noise in Section 3.2 to form the network's training targets. The reported 91.79% and 90.83% agreement within ±50 therefore measures how well the network reproduces its own training signal, not how well the scores predict any externally meaningful behavior or outcome. This directly undermines the conclusion that 'empirical evaluations confirm that zScores align closely with strategic behavior patterns.'
[Tables 1-2 and Section 4.3] The bin-wise behavioral summaries do not provide independent validation. The metrics shown in Tables 1-2 (liquidity retention, holding duration, withdrawal volume, swap count, token diversity) are the same sub-category features used in the blueprint to construct the scores, with hand-set weights and caps. The increasing trends across score bins are therefore mechanical consequences of the blueprint's construction, not discoveries about wallet behavior. The paper would need to validate the scores against behaviors or outcomes that were not used to define the score.
[Sections 3.1-3.2 and Section 4] There is no external ground truth, no baseline, and no uncertainty quantification. The blueprint weights, caps, noise standard deviation, dusk thresholds, and tolerance window are all free parameters, and none is tested against an external outcome such as future returns, protocol revenue, loan repayment, or an independent reputation label. Without such validation, 'strategic behavior' is simply a restatement of the authors' rubric. Additionally, the paper does not compare the deep network against linear regression or against the raw blueprint scores, so the claimed benefit of the neural refinement phase is not established.
[Section 4.3 (dusk wallet exclusion)] The post-hoc exclusion of 'dusk' wallets, defined as wallets with one transaction and negligible volume, is applied without reporting results with those wallets included. Because this exclusion is made after examining the data and without a sensitivity analysis, it is unclear whether the reported accuracy and bin trends hold for the full population. The exclusion may be reasonable, but it should be presented as a robustness check with both full-sample and filtered results, and the threshold definitions should be precise.

minor comments (4)

[Section 4.3.2] The text 'often below 10f orLP sor50 for swaps' appears garbled and should be corrected to clearly state the dusk thresholds (e.g., dollar amounts) for LP deposits and swap volumes.
[Section 4.1] The term 'T_scores' is used without definition; the reader must infer that it refers to the blueprint scores. Please define this notation.
[Sections 3.1 and 3.2] Section 3.1 states that 'no rigid cutoffs are enforced,' while Section 3.2 states that each sub-category is 'capped by a predefined maximum'; this apparent inconsistency should be clarified.
[External Validation Report] The supplementary validation report is a GitHub link rather than an archival, peer-reviewed artifact. If this report is intended to support the paper's claims, it should be made available in a permanent form with reproducibility instructions, and its contents should be summarized in the manuscript.

Circularity Check

4 steps flagged · score 8.0 of 10

The central behavioral-validity claim reduces to the blueprint that generates the training labels; the 91.79% 'prediction' accuracy and the bin tables measure reproduction of the authors' rubric, not independent evidence.

fitted input called prediction [Section 3.2 (Noise-Aware Label Generation) and Section 4.1 (LP Score)]
"To evaluate the effectiveness of our model in learning liquidity provider behavior, we compare the predicted T_scores against blueprint-defined targets."

The 'ground truth' in this evaluation is the blueprint score produced by the Section 3.1 rubric, and Section 3.2 states that the training labels are exactly those scores plus injected Gaussian noise ('we inject controlled Gaussian noise into the blueprint-generated scores before training'). A deep network trained on noisy copies of the blueprint scores will, by construction, reproduce them on held-out wallets; the reported 91.79% (and 90.83% for swaps) is a fit-to-input measurement, not a prediction of any externally defined behavior. The word 'predicted' is applied to the network's approximation of its own training target.
self definitional [Section 4.3.1 (Behavioral Trends Across LP Score Bins) versus blueprint definition in Section 3.1]
"Wallets in higher bins generally show lower withdrawal activity, higher liquidity retention, and significantly longer holding durations—indicating a stronger alignment with long-term, stable LP behavior."

Section 3.1 states that for liquidity providers 'the blueprint captures dimensions such as deposit volume and frequency, withdrawal activity, and average holding time,' and Table 1 tabulates exactly those dimensions (avg deposit, avg withdraw, holding days, liquidity remaining, deposit frequency). The scores are weighted sums of these components with caps, so higher-score bins having lower withdrawals, higher retention, and longer holding times is entailed by the score construction. The table is therefore a restatement of the blueprint, not independent evidence of behavioral validity.

2 more flagged steps

self definitional [Section 4.3.2 (Behavioral Trends Across Swap Score Bins) versus blueprint definition in Section 3.1]
"Higher scoring wallets consistently demonstrate stronger trading engagement, with significantly higher swap volumes, increased transaction frequency, and greater diversity in token interactions."

The trader blueprint is defined by 'engagement through swap volume and frequency' plus 'token diversity, volatility exposure, and average time between swaps' (Section 3.1), and Table 2 reports swap volume, holding time, swap count, and unique tokens. The monotonic relationship between the score and these metrics is a direct consequence of the hand-set weights and caps used to build the score; it does not validate the score against an independent measure of trading quality or reputation.
self definitional [Section 6 (Conclusion)]
"Empirical evaluations confirm that zScores align closely with strategic behavior patterns. High-scoring users exhibit strong liquidity retention, disciplined trading activity, and broad engagement across pools—indicating that the score meaningfully differentiates long-term, protocol-aligned participants from short-term or exploratory actors."

This is the paper's central claim, but 'strategic behavior' is never measured against an external outcome (e.g., future returns, realized creditworthiness, or honest behavior). The listed behaviors—liquidity retention, disciplined trading, broad engagement—are exactly the components and caps that define the blueprint, so the claimed alignment is true by definition. Without external validation of the blueprint weights, the conclusion restates the authors' rubric rather than confirming an empirical finding.

full rationale

The derivation chain is self-referential at its core. Section 3.1 constructs blueprint scores from hand-chosen behavioral dimensions; Section 3.2 trains the deep network on Gaussian-noise-augmented copies of those same scores; Section 4 evaluates the network against the same blueprint-defined targets; and Section 4.3 validates the scores against tables of the very features that compose the blueprint. The 91.79%/90.83% agreement figures therefore measure how well a flexible regressor recovers its own training labels, not whether the scores predict any external outcome. The bin-wise behavioral summaries are entailed by the score construction, so they cannot serve as confirmation. The reference to the authors' prior zScore work [16] is only a naming disclaimer and is not load-bearing, so it does not add circularity. The GitHub 'External Validation Report' is produced by the same team and consists of descriptive bin-level summaries rather than independent ground truth, so it does not break the circularity. Because no external criterion or independently validated target is introduced, the central claim that high zScores indicate strategic behavior is forced by the definition of the blueprint rather than by empirical discovery.

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

The scoring pipeline rests on hand-chosen blueprint weights, undisclosed noise levels, and ad hoc exclusion rules. The only invented entities are the score metrics themselves and the dusk-wallet category, neither of which is tied to external evidence. The framework is therefore a self-contained engineering construction with no independent anchors.

free parameters (5)

Blueprint sub-category weights and caps = Not disclosed
These define the score and are chosen by domain judgment in Section 3.1 without independent calibration.
Gaussian noise standard deviation for label augmentation = Not stated
Noise scale affects the learned mapping but is not reported in Section 3.2.
Dusk filter thresholds = Vague: 'often below 10... or 50 for swaps'
Excludes wallets with one transaction and low volume; the cutoff is unspecified and affects validation bins.
Tolerance window for accuracy = ±50
Chosen by authors as a hit criterion in Section 4.1 with no justification.
Network hyperparameters = lr 5e-4, weight decay 1e-4, hidden dims 1024->512->256
Learning rate and dims are given, but dropout rate, batch size, and early stopping patience are not.

assumptions (5)

domain assumption A wallet address corresponds to a single user.
Sybil users can split activity across addresses, which would bias scores; the paper does not address this.
ad hoc to paper The feature blueprint encodes true reputation.
No external ground truth links the blueprint to actual lending or trading outcomes; the weights are set by hand in Section 3.1.
ad hoc to paper Gaussian noise injection preserves the ordinal structure of scores.
Stated without proof in Section 3.2; noise could reorder nearby scores in unpredictable ways.
domain assumption Pool context (TVL, fee tier, pool size) is the correct normalizer.
No sensitivity analysis shows that these variables improve the score's behavior.
standard math Standard supervised learning assumptions hold across wallets.
The wallet-level split mitigates leakage, but wallets may still be correlated through shared pools or market conditions.

invented entities (2)

zScore dual metrics (Liquidity Provision Score and Swap Behavior Score)
purpose: Wallet reputation ranking on Uniswap
The metric is defined entirely by the paper's blueprint; no external outcomes or benchmarks establish its validity.
Dusk LPs and dusk swappers category
purpose: Filter out wallets with only one transaction
An ad hoc exclusion defined in Section 4, removing the least active users from validation and making the score look more discriminative.

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Cite this review

Pith. "Pith review of Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals." pith.science (2026). https://pith.science/paper/2ISLXPSK

@misc{pith2026250720494,
  author       = {Pith},
  title        = {Pith review of: Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2ISLXPSK}},
  note         = {Machine review of arXiv:2507.20494}
}

read the original abstract

As decentralized finance (DeFi) evolves, distinguishing between user behaviors - liquidity provision versus active trading - has become vital for risk modeling and on-chain reputation. We propose a behavioral scoring framework for Uniswap that assigns two complementary scores: a Liquidity Provision Score that assesses strategic liquidity contributions, and a Swap Behavior Score that reflects trading intent, volatility exposure, and discipline. The scores are constructed using rule-based blueprints that decompose behavior into volume, frequency, holding time, and withdrawal patterns. To handle edge cases and learn feature interactions, we introduce a deep residual neural network with densely connected skip blocks inspired by the U-Net architecture. We also incorporate pool-level context such as total value locked (TVL), fee tiers, and pool size, allowing the system to differentiate similar user behaviors across pools with varying characteristics. Our framework enables context-aware and scalable DeFi user scoring, supporting improved risk assessment and incentive design. Experiments on Uniswap v3 data show its usefulness for user segmentation and protocol-aligned reputation systems. Although we refer to our metric as zScore, it is independently developed and methodologically different from the cross-protocol system proposed by Udupi et al. Our focus is on role-specific behavioral modeling within Uniswap using blueprint logic and supervised learning.

Figures

Figures reproduced from arXiv: 2507.20494 by the authors.

**Figure 1.** Model Architecture. The top portion illustrates the full DeepMLP_ResNet architecture, which processes input features through an initial projection layer followed by four residual blocks with skip connections. Dimensionality is progressively reduced from 1024 to 256 across the blocks. The final regression head maps the processed representation to a scalar zScore. The bottom inset zooms into a single residual block, s… view at source ↗

**Figure 2.** (Left) Predicted vs. blueprint LP scores on the validation set. (Right) Distribution of residuals (prediction [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** (Left) Predicted vs. blueprint swap scores on the validation set. (Right) Distribution of residuals (prediction [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection
q-fin.GN 2026-07 conditional novelty 5.0 of 10

ZAPs attributes DeFi rewards via percentile-normalized score and a four-layer adversarial defense, reporting 0.923 ensemble ROC-AUC and 30–90% cuts in simulated adversarial reward capture.

Reference graph

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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A reputation scoring framework for lending protocols using the pagerank algorithm

Mau-Tra Nguyen, Tuan-Dat Trinh, and Viet-Bang Pham. A reputation scoring framework for lending protocols using the pagerank algorithm. InInternational Symposium on Information and Communication Technology, pages 478–494. Springer, 2024

work page 2024