This report documents and analyzes a coordinated Ethereum-based ETH distribution system composed of multiple externally owned accounts (EOAs) exhibiting replicated execution behavior, synchronized lifecycle characteristics, and structured fund propagation patterns.

The analysis originated from the identification of a high-activity EOA executing approximately 500 ETH transfer transactions within a tightly bounded temporal window. Although the wallet is externally owned and invokes no smart contracts, its activity displays execution characteristics inconsistent with discretionary human usage, including deterministic gas consumption, uniform transaction structure, and highly concentrated execution timing.

Expanded flow analysis reveals a broader multi-wallet system in which several EOAs were provisioned with identical ETH amounts, created within a narrow time window, and subsequently used to perform near-identical downstream distribution activity. These wallets exhibit closely clustered transaction counts, simple execution paths, and short operational lifespans, indicating replicated operational units rather than independent actors.

Funds propagate across multiple layers of newly created wallets, with minimal retention and immediate forwarding behavior observed at each hop. Execution patterns remain highly uniform throughout the system, with no evidence of interactive contract usage, adaptive execution logic, or discretionary timing variation.

Based on temporal structure, gas determinism, lifecycle synchronization, and fund flow topology, the observed behavior is best classified as a coordinated ETH distribution infrastructure composed of provisioned EOAs with replicated execution footprints, rather than organic user activity or automated DeFi or MEV execution systems.

This case study extends prior behavioral analyses of automated execution controllers and organic EOAs by introducing a distinct infrastructure-level behavioral class. It demonstrates the importance of lifecycle analysis, execution symmetry, and network-level flow reconstruction in distinguishing operational systems from individual wallet behavior in large-scale blockchain analytics.

2. Scope, Methodology & Analytical Constraints

This study examines on-chain transaction behavior on the Ethereum mainnet using an attribution-agnostic, metrics-driven analytical framework. The analysis focuses exclusively on observable execution patterns, fund flow topology, and wallet lifecycle characteristics, without reliance on off-chain data, inferred intent, or entity attribution beyond publicly available infrastructure identifiers.

Scope

• Network: Ethereum mainnet

• Assets analyzed: Native ETH transfers only

• Primary observation window: 60 days

• Extended context: Wallet creation timestamps and full transaction history where required for lifecycle and flow reconstruction

The analysis centers on a set of externally owned accounts (EOAs) identified through anomalous transaction volume and execution symmetry during the observation window. All wallets analyzed are non-contract accounts.

Methodology

The classification framework applies multiple orthogonal metrics to reduce reliance on any single behavioral signal. The following analytical dimensions are evaluated:

Temporal Activity Analysis
Transaction activity was analyzed across daily and hourly intervals to assess execution clustering, persistence, and inactivity windows. Temporal structure was used to distinguish discretionary usage from batch-triggered or infrastructure-driven execution.

Execution Simplicity & Gas Consumption Analysis
Gas usage distributions (p50, p90, p99) were computed to evaluate execution path determinism, branching behavior, and functional complexity. Highly uniform gas usage was treated as evidence of repeated, fixed execution templates.

Transaction Flow Reconstruction
ETH fund flows were traced across multiple hops to identify provisioning patterns, staging behavior, and downstream propagation. Particular attention was paid to identical funding amounts and replicated transfer sequences.

Wallet Lifecycle & Creation Timing Analysis
Wallet creation timestamps, operational lifespans, and post-execution behavior were examined to assess whether wallets exhibited organic accumulation patterns or appeared provisioned for single-purpose execution.

Recipient Behavior Analysis
Downstream recipient wallets were evaluated for immediate forwarding behavior, lifecycle symmetry, and further propagation to newly created wallets, enabling identification of multi-layer distribution structures.

Each metric was evaluated independently and interpreted collectively to avoid overfitting conclusions to any single signal.

Analytical Constraints & Exclusions

To maintain institutional rigor and avoid interpretive overreach, the following constraints apply:

• No attribution of real-world identity, ownership, or coordination

• No inference regarding intent, legality, or economic purpose

• No use of off-chain data, private mempool data, or relay information

• No assumptions of control relationships beyond observable execution behavior

• Centralized exchange wallets treated solely as liquidity sources

All conclusions are derived strictly from observable on-chain behavior within the defined analytical scope.

3. Wallet Discovery & Initial Anomaly Detection

The investigation originated from the identification of an externally owned account exhibiting unusually high transaction activity concentrated within a narrow temporal window. The wallet
0x5B00D16a8004017A00F5F6Aa5EDec1E668fE2F34 was surfaced during routine transaction-volume screening of EOAs active within the observation period.

Initial Observations

At initial inspection, the wallet exhibited the following characteristics:

• Approximately 500 outbound transactions

• Execution concentrated within a single calendar day

• Transactions consisting exclusively of native ETH transfers

• No smart contract interactions

• Uniform gas usage across all transactions

While high transaction volume alone is not sufficient for behavioral classification, the combination of extreme temporal concentration, execution simplicity, and interaction exclusivity constituted a structural anomaly relative to typical discretionary EOA usage.

Gas Usage Signal

Preliminary gas analysis revealed identical gas consumption across all observed transactions:

• Gas used per transaction: 21,000 units

This value corresponds to the minimum gas required for a simple ETH transfer and indicates the absence of conditional execution, contract invocation, or dynamic calldata construction. The complete lack of gas variance immediately excluded a broad class of behaviors, including DeFi interaction, automated trading strategies, and MEV execution infrastructure.

Temporal Concentration

Hourly aggregation of transaction activity showed execution tightly clustered within a contiguous set of hours, with no observable persistence before or after the execution window. The wallet exhibited no ramp-up, cooldown, or sustained activity pattern.

This burst-style execution profile is inconsistent with discretionary human usage and instead suggests batch-triggered execution.

Rationale for Expanded Analysis

Individually, each observed signal—high transaction count, simple ETH transfers, or temporal clustering—could plausibly occur in isolation. However, their simultaneous presence within a newly active EOA warranted deeper investigation.

Specifically, the following questions motivated further analysis:

• Why was capital distributed within such a compressed execution window?

• Why were transaction structures identical across hundreds of transfers?

• Why did the wallet exhibit no functional behavior beyond ETH distribution?

These questions informed the subsequent analysis of funding provenance, wallet lifecycle timing, transaction flow topology, and downstream recipient behavior.

4. Temporal Execution Characteristics

To distinguish discretionary EOA usage from automated or infrastructure-driven execution, transaction activity was analyzed across daily and hourly resolutions. Temporal structure is a primary indicator of execution modality: human-operated wallets typically exhibit availability-constrained patterns, while infrastructure systems execute according to task completion or batch logic.

4.1 Daily Activity Structure

Across the observation window, wallet activity is overwhelmingly concentrated within a single calendar day. Outside this execution window, the wallet exhibits no meaningful transaction activity, with no evidence of gradual buildup, intermittent usage, or post-execution persistence.

This lack of longitudinal activity distinguishes the wallet from both organic EOAs, which typically show irregular but recurring usage, and continuously operating automated systems, which often maintain activity across multiple days or weeks. The observed profile instead reflects a single-purpose activation consistent with batch-oriented execution.

4.2 Hourly Transaction Distribution

Hourly aggregation shows transactions tightly clustered within a narrow band of consecutive hours. Activity ramps up rapidly, sustains high throughput for a short interval, and then terminates abruptly.

No evidence is observed of:

• Circadian rhythm alignment

• Time-of-day drift

• Interspersed inactivity associated with human attention constraints

The compression and uniformity of execution indicate transactions were submitted as part of a preconfigured process rather than interactively initiated.

4.3 Temporal Behavior Interpretation

Combined daily and hourly patterns indicate execution governed by task completion rather than discretion or availability. The wallet appears to activate, perform a bounded distribution process, and then cease activity entirely.

Such temporal signatures are inconsistent with discretionary wallet behavior and are more commonly associated with logistics-oriented execution systems, where time structure reflects process boundaries rather than human decision cycles. This temporal analysis provides a foundational signal for the infrastructure-level classification developed in subsequent sections.

5. Execution Simplicity & Gas Determinism

Execution complexity and gas consumption behavior were analyzed to assess whether transaction execution paths exhibit variability consistent with discretionary interaction or deterministic repetition consistent with scripted execution.

Gas usage serves as a direct proxy for execution branching: discretionary or contract-interactive behavior typically produces dispersed gas distributions, while repeated ETH transfer execution produces tightly compressed profiles.

5.1 Gas Usage Distribution

Across the observation window, the wallet’s gas consumption exhibits complete determinism:

All observed transactions consume exactly 21,000 gas units, corresponding to the base cost of a standard ETH transfer.

No variance is observed across the distribution, indicating the absence of:

• Contract interactions

• Conditional execution paths

• Calldata variability

• Fallback or error-handling behavior

5.2 Interpretation of Execution Determinism

The complete collapse of gas variance implies that every transaction follows an identical execution path from initiation to completion. This excludes discretionary interaction patterns, where even repetitive human activity typically introduces minor variance through retries, timing differences, or incidental contract calls.

Instead, the observed profile reflects a fixed execution template applied repeatedly across a bounded transaction set. Execution behavior appears invariant to context, timing, or downstream state, consistent with preconfigured transfer logic rather than interactive use.

5.3 Execution Complexity Summary

The wallet’s execution behavior is characterized by:

• Functionally atomic execution (native ETH transfer only)

• Identical execution paths across all transactions

• Absence of branching, retries, or adaptive logic

Such execution simplicity is characteristic of operational nodes performing predefined logistical tasks rather than independently operated user wallets engaging in exploratory or reactive activity.

This section provides decisive evidence that the wallet’s activity is execution-driven rather than interaction-driven.

6. Funding Origin & Capital Provisioning Path

To assess whether the observed execution behavior emerged organically or was provisioned as part of a coordinated system, upstream transaction flows were reconstructed for the initial funding events associated with the wallet cluster. The purpose of this analysis is structural characterization of capital movement preceding execution, not attribution of ownership or intent.

The reconstructed funding topology is summarized in Figure C1 (Appendix C), which illustrates the upstream capital path from a centralized exchange withdrawal through staging and provisioning wallets into multiple execution wallets.

6.1 Initial Funding Trace

The first observed inbound transaction to the wallet under analysis consisted of 0.22 ETH, received shortly after wallet creation.

Upstream flow reconstruction identifies the following sequence:

• A centralized exchange–associated wallet (Binance 14) initiated an ETH withdrawal

• Funds were routed through one or more intermediary wallets

• A staging wallet subsequently disbursed identical 0.22 ETH amounts to multiple newly created EOAs, including the wallet under analysis

This funding pattern is notable for its uniformity and replication, rather than its point of origin.

6.2 Identical Provisioning Amounts

The same 0.22 ETH funding amount was observed being distributed from the staging wallet to multiple recipient wallets within a narrow temporal window.

Key characteristics of this provisioning pattern include:

• Identical funding amounts across recipients

• Recipient wallets created shortly before funding

• Funding occurring prior to any outbound activity

• No evidence of incremental top-ups or discretionary balance adjustments

This structure contrasts with organic wallet funding, which typically exhibits variation in funding size, timing, and sourcing.

6.3 Intermediary Wallet Function

Intermediary wallets within the funding path function as staging and provisioning nodes, aggregating capital and redistributing it in standardized increments.

These wallets do not exhibit:

• Sustained operational lifespans

• Diverse execution behavior

• Independent transactional objectives

Instead, their activity is tightly scoped to short-lived capital redistribution. The presence of a centralized exchange wallet is treated strictly as a liquidity source, not as evidence of coordination or ownership.

6.4 Provisioning Interpretation

The observed funding structure is consistent with a capital provisioning process, characterized by:

• Predefined funding amounts

• Replicated initialization across multiple wallets

• Temporal alignment between wallet creation and funding

• Immediate transition from funding to execution

Such patterns are characteristic of infrastructure deployment, where wallets are initialized with sufficient capital to perform a predefined operational role, rather than discretionary users incrementally funding accounts over time.

6.5 Funding Path Summary

The reconstructed funding topology supports classification of the wallet as part of a coordinated ETH distribution system, in which capital is provisioned centrally and execution is delegated to multiple short-lived EOAs operating under identical constraints.

This analysis establishes the upstream context required to evaluate lifecycle synchronization and replicated execution behavior in subsequent sections.

7. Wallet Lifecycle Synchronization & Creation Timing

To evaluate whether the observed wallets operate independently or as part of a coordinated system, wallet lifecycle characteristics were analyzed, including creation timing, operational duration, and post-execution behavior.

Lifecycle analysis complements transaction-level metrics by capturing coordination signals that emerge through synchronized provisioning and short, task-bounded lifetimes rather than through complex execution logic.

7.1 Wallet Creation Timing

Wallets participating in the observed distribution pattern were created within a tightly clustered timeframe, with creation dates falling within a narrow multi-day window.

Key observations include:

• Wallets were created shortly before receiving initial funding

• No historical transaction activity exists prior to provisioning

• Creation timestamps are closely aligned across wallets exhibiting near-identical execution behavior

Such temporal clustering is inconsistent with organically accumulated EOAs, which typically display broad dispersion in creation dates and heterogeneous activity histories.

7.2 Operational Lifespan

Following initial funding, each wallet exhibits a brief and highly concentrated operational phase, characterized by:

• Immediate transition from funding to execution

• High transaction throughput over a short duration

• Abrupt cessation of activity upon task completion

No wallet demonstrates gradual ramp-up, intermittent usage, or sustained persistence beyond its execution window. This lifecycle profile indicates single-purpose instantiation rather than ongoing discretionary use.

7.3 Post-Execution Dormancy

After completing outbound transfer activity, wallets exhibit no meaningful post-execution behavior, including:

• Continued balance management

• Contract interaction

• Secondary execution phases

• Opportunistic reuse

Instead, wallets enter persistent dormancy immediately after fulfilling their apparent operational role. This behavior contrasts sharply with human-operated EOAs, which typically show residual activity, balance consolidation, or reuse across unrelated transactions.

7.4 Lifecycle Symmetry Across Wallets

When evaluated collectively, the wallets display near-identical lifecycle trajectories:

Creation → Funding → Burst execution → Dormancy

Observed symmetry includes:

• Similar execution durations

• Comparable transaction counts

• Aligned transition into inactivity

This level of uniformity strongly indicates coordination at the system design level, where wallets are instantiated, provisioned, and retired according to a predefined operational template.

7.5 Lifecycle Interpretation

The observed lifecycle characteristics are consistent with provisioned EOAs operating as disposable execution units within a broader infrastructure system.

Rather than representing autonomous actors, these wallets function as interchangeable components deployed to execute a bounded distribution task.

Lifecycle Summary

Wallet lifecycle synchronization reinforces classification of the observed wallets as infrastructure-level execution nodes, provisioned and retired according to a shared operational schedule rather than independent discretionary usage.

This section establishes the temporal foundation required to analyze replicated downstream distribution behavior.

8. Replicated Distribution Nodes

Beyond individual wallet behavior, the analysis identifies multiple downstream wallets exhibiting near-identical execution characteristics. The degree of similarity observed across these wallets exceeds what would be expected from independent discretionary activity and instead indicates deliberate replication within a coordinated system.

This section examines the structural symmetry across these wallets and explains why they are best understood as repeated instances of the same operational role.

While Two of the provisioned EOAs execute distribution directly upon funding, One acted as relay nodes, forwarding standardized funding amounts to a secondary wallet that subsequently performed identical high-volume ETH transfer activity. Both patterns result in indistinguishable execution footprints and identical lifecycle characteristics at the execution layer, indicating functional equivalence rather than behavioral divergence.

The downstream wallets labeled as distribution nodes in Figure C1 (Appendix C) represent multiple instances of the same execution role, each exhibiting near-identical transaction counts, deterministic gas usage, and short operational lifespans.

8.1 Identification of Distribution Nodes

Multiple wallets were observed that share the following characteristics:

• Receipt of identical initial funding amounts

• Execution of several hundred outbound ETH transfers

• Operation within overlapping, short-lived execution windows

• Minimal interaction diversity and exclusive use of native ETH transfers

Representative wallets exhibit total transaction counts tightly clustered around the same range (approximately 500 transactions per wallet). While exact counts vary marginally, the overall execution footprint remains narrowly bounded.

8.2 Transaction Count Symmetry

Transaction volume across distribution nodes displays a high degree of symmetry:

• Each wallet executes hundreds of outbound ETH transfers

• Total transaction counts differ only slightly (e.g., ~501, ~504, ~506)

• Activity is front-loaded and completed within a narrow time horizon

Such clustering is statistically implausible for discretionary EOAs, which typically exhibit wide variance in activity levels, pacing, and duration. Minor deviations in total count are more consistent with operational tolerances—such as gas failures or recipient exhaustion—than with behavioral divergence.

8.3 Execution Homogeneity

Execution behavior across all identified distribution nodes is structurally uniform:

• Pure ETH transfers with no contract interaction

• Identical gas usage per transaction

• No evidence of branching, retries, or adaptive execution paths

• Uniform transaction formatting

This level of homogeneity indicates that wallets are executing a fixed task under identical constraints rather than making independent, context-dependent decisions.

8.4 Functional Role Consistency

Each distribution node performs the same functional role within the system:

• Receive provisioned capital

• Distribute ETH across a downstream recipient set

• Cease activity immediately upon task completion

No wallet deviates from this role to engage in unrelated activity such as contract interaction, balance consolidation, or secondary execution phases. The absence of role drift further supports interpretation of these wallets as purpose-built execution units.

8.5 Replication Interpretation

When evaluated collectively, similarities in funding structure, execution behavior, transaction volume, and operational timing demonstrate that these wallets function as replicated distribution nodes within a coordinated ETH distribution system.

Rather than representing independent EOAs, they operate as interchangeable execution units instantiated to perform the same bounded distribution task.

Replicated Node Summary

The presence of multiple wallets with near-identical execution footprints constitutes strong evidence of system-level coordination.

This replication establishes the basis for analyzing multi-layer propagation and recipient relay behavior, which is addressed in the following section.

9. Multi-Layer Propagation & Recipient Behavior

Following identification of replicated distribution nodes, the analysis examined downstream recipient behavior to determine whether ETH distribution terminated at first-order recipients or propagated further through additional wallet layers.

Observed execution patterns demonstrate intentional multi-layer propagation, reinforcing classification of the system as coordinated distribution infrastructure rather than isolated batch execution.

While Figure C1 (Appendix C) abstracts downstream activity for readability, multi-layer propagation was confirmed analytically through complete transaction trace analysis, with successive wallet generations exhibiting immediate forwarding and identical lifecycle characteristics.

9.1 Recipient Wallet Characteristics

First-order recipient wallets exhibit consistent structural characteristics:

• Newly created within a tightly clustered timeframe

• No transaction history prior to receiving ETH

• Short operational lifespans

• Absence of unrelated or exploratory activity

These properties indicate that recipient wallets were not organically accumulated addresses but were provisioned for participation in a predefined execution process.

9.2 Immediate Forwarding Behavior

Upon receipt of ETH, recipient wallets consistently:

• Forward funds to another wallet with minimal delay

• Retain negligible balances

• Execute no discretionary actions between receipt and forwarding

The absence of holding behavior, timing variance, or secondary decision points indicates automated or pre-scripted execution rather than discretionary use.

9.3 Secondary Recipient Symmetry

Second-layer recipient wallets mirror first-layer behavior:

• Creation within the same narrow temporal window

• Receipt of comparable ETH amounts

• Immediate forwarding behavior

• Lack of independent operational activity

This recursive execution pattern demonstrates deliberate layering rather than incidental chaining or ad-hoc redistribution.

9.4 Propagation Depth Interpretation

The presence of multiple forwarding layers, combined with uniform execution behavior at each hop, indicates a system designed for capital propagation rather than terminal use.

Such depth is inconsistent with organic payment flows, tipping, or casual redistribution, and instead reflects structured fund movement through provisioned execution nodes.

9.5 Behavioral Implications

Multi-layer propagation provides decisive evidence of:

• Coordinated wallet provisioning

• Predefined, non-discretionary execution logic

• System-level intent to distribute capital rather than interact with on-chain services

While no inference is made regarding motive, destination, or economic purpose, the observed structure constitutes a distinct infrastructure-level behavioral class.

Multi-Layer Summary

Consistent forwarding behavior across multiple wallet layers confirms that the observed system operates as a multi-hop ETH distribution infrastructure.

This establishes the basis for formal network-level classification and system-wide interpretation, addressed in the following section.

10. Network-Level Behavioral Classification

Based on cumulative evidence across temporal execution structure, gas determinism, funding topology, wallet lifecycle synchronization, replicated execution nodes, and multi-layer fund propagation, the analyzed system is best classified as a coordinated ETH distribution network composed of provisioned externally owned accounts.

This classification is derived exclusively from observable on-chain behavior. No attribution, intent inference, or external labeling is applied.

10.1 Exclusion of Organic EOA Activity

The observed behavior diverges materially from discretionary EOA usage across multiple dimensions:

• Wallet creation and activation occur within tightly clustered timeframes

• Execution is fully deterministic, with no structural variance

• Activity is short-lived and bounded to a single operational phase

• Recipient wallets consistently forward funds immediately without retention or diversification

Organic EOAs typically exhibit heterogeneous creation histories, variable timing, mixed interaction patterns, and discretionary balance management. These characteristics are absent at scale within the analyzed system.

10.2 Exclusion of MEV or Trading Infrastructure

Although automated, the system does not exhibit behavioral traits associated with MEV execution or trading infrastructure:

• No smart contract interactions or protocol engagement

• No adaptive or competitive gas bidding behavior

• No repeated execution under adversarial or time-sensitive conditions

• No evidence of mempool-reactive execution logic

The execution pattern is purely logistical, focused on ETH transfer propagation rather than strategy execution or value extraction.

10.3 Distinction from Simple Batch Execution

While batch ETH transfers may occur in benign contexts, the system analyzed here exceeds simple scripting due to the following structural properties:

• Multiple replicated execution nodes with near-identical transaction counts

• Layered fund forwarding across successive generations of wallets

• Uniform provisioning amounts reused across independent nodes

• Coordinated lifecycle timing across the network

These characteristics indicate infrastructure-level design rather than ad hoc or single-instance batch execution.

10.4 Formal Classification Statement

The analyzed wallets collectively represent:

A coordinated, multi-layer ETH distribution infrastructure composed of provisioned EOAs with replicated execution footprints.

This classification reflects system-level organization and intentional execution symmetry, without asserting motive, ownership, or downstream purpose.

10.5 Classification Confidence

Classification confidence is high due to:

• Consistency across independent analytical dimensions

• Replication across multiple wallets and execution layers

• Absence of signals indicative of discretionary or interactive usage

The classification remains robust under conservative interpretive assumptions.

11. Implications for On-Chain Behavioral Analysis

This case study illustrates the limitations of relying on isolated metrics—such as transaction count, wallet age, or contract interaction presence—when classifying Ethereum wallets. Despite appearing superficially similar to active EOAs, the analyzed wallets exhibit infrastructure-level behavior that only becomes visible through multi-dimensional analysis.

11.1 Transaction Count Is Not a Behavioral Signal

High transaction volume alone is insufficient for behavioral classification. In this case, wallets executing approximately 500 transactions appear active by volume yet display no discretionary behavior, protocol interaction, or longitudinal persistence.

Absent execution- and lifecycle-level context, such wallets risk misclassification as power users, traders, or independently operating automated agents.

11.2 Lifecycle and Timing as Primary Signals

Wallet lifecycle characteristics proved more informative than transaction frequency. Tightly clustered creation dates, brief operational lifespans, and immediate post-execution dormancy indicate intentional provisioning rather than organic usage.

Lifecycle symmetry across multiple wallets emerges as a strong indicator of coordinated system design.

11.3 Flow Topology Reveals Coordination Beyond Single Wallets

Fund flow reconstruction exposes coordination that is not observable at the individual wallet level. Identical funding amounts, staging wallets, and repeated multi-hop forwarding demonstrate that behavioral intent is embedded in flow topology rather than transaction metadata alone.

Topology-aware analysis is therefore essential for detecting infrastructure-level systems.

11.4 Gas Determinism as an Execution Fingerprint

Uniform gas consumption provides a low-noise fingerprint of deterministic execution. Unlike discretionary or adaptive wallets, deterministic gas profiles indicate constrained execution logic and exclude a broad class of organic behaviors.

Gas distributions should be treated as primary classification signals rather than ancillary metrics.

11.5 Implications for Wallet Taxonomy Design

This analysis underscores the need for wallet taxonomies that distinguish between:

• Discretionary EOAs

• Automated execution controllers

• Provisioned logistical infrastructure

Each category exhibits distinct temporal, structural, and execution signatures that cannot be reliably captured through labels or single-metric heuristics.

11.6 Relevance to Analytics, Compliance, and Research

Accurate identification of provisioned infrastructure wallets has direct implications for:

• Wallet clustering and de-duplication

• Automation and coordination detection

• Risk modeling and exposure analysis

• Longitudinal network behavior research

Failure to distinguish such systems can materially distort user metrics, attribution models, and ecosystem-level analyses.

12. Limitations & Non-Claims

This analysis is limited to observable on-chain activity on the Ethereum mainnet within the defined observation window(s). All findings are derived exclusively from transaction-level data, wallet creation metadata, and on-chain fund flow reconstruction.

Accordingly, the report makes no claims regarding:

• Real-world identity, ownership, or control of the wallets analyzed

• Economic intent, strategy, or motivation underlying the observed behavior

• Legality, compliance status, or regulatory implications

• Off-chain coordination, communications, or agreements

• The ultimate destination or end use of distributed funds

No attribution is attempted beyond publicly observable on-chain relationships. The presence of centralized exchange withdrawals is treated strictly as a liquidity source and does not imply exchange involvement, knowledge, or coordination in downstream activity.

While strong behavioral symmetry and coordination are observed, conclusions are explicitly limited to classification of execution structure and network organization, not inference of purpose or intent. Similar distribution architectures may plausibly support a range of legitimate, neutral, or otherwise unobservable use cases.

Finally, the findings presented here may not generalize to all ETH distribution systems. This case study is intended to document and analyze a specific, well-defined behavioral pattern rather than establish a universal or exhaustive model.

13. Conclusion

This study examined a cluster of Ethereum externally owned accounts exhibiting synchronized creation timing, identical capital provisioning, deterministic execution paths, and replicated transaction behavior. Through transaction flow reconstruction, temporal analysis, gas usage profiling, and lifecycle symmetry assessment, the wallets were classified as a coordinated ETH distribution infrastructure composed of provisioned EOAs.

The observed behavior diverges materially from both organic discretionary wallet usage and automated MEV execution systems. Rather than interaction- or extraction-driven activity, the system reflects a logistics-oriented architecture characterized by short-lived operational wallets, batch-triggered execution, and multi-layer fund propagation.

In combination with prior behavioral analyses of organic EOAs and automated execution controllers, this case study extends the on-chain wallet taxonomy by identifying a distinct infrastructure-level class whose primary function is capital distribution rather than interaction, accumulation, or strategy execution.

More broadly, this analysis reinforces the importance of execution structure, lifecycle design, and fund flow topology in behavioral classification. It demonstrates how attribution-agnostic, metrics-driven methodologies can surface meaningful distinctions between users, automation, and infrastructure within large-scale blockchain systems.

Appendix A — Wallets Referenced

This appendix lists representative wallets referenced in the analysis; it is not an exhaustive enumeration of all downstream recipients.

Appendix B — Data Sources & Query Logic

Interactive Dashboard:

https://dune.com/notes0x/distribution-eoa-network

Wallet Selection Query:

https://dune.com/queries/6506254

Cluster Gas Distribution Profile:

https://dune.com/queries/6506263/10298972

Temporal Execution Heatmap:

Daily Transactions: https://dune.com/queries/6506442/10299202

Hourly Transaction: https://dune.com/queries/6506539/10314690

Transaction Volume Aggregator:

https://dune.com/queries/6543467/10347295

Appendix C — Conceptual Flow Topology

Figure C1. Conceptual transaction flow topology of the coordinated ETH distribution system.

This appendix describes the conceptual transaction flow topology reconstructed from on-chain activity within the analyzed wallet cluster. The diagram is intended to illustrate structural relationships and execution roles, not causality, ownership, or economic intent.

To preserve readability, the topology abstracts transaction multiplicity and downstream fan-out, collapsing repeated execution behavior into functional nodes rather than enumerating all individual transfers.

Diagram Structure

1. Liquidity Source Layer

• Centralized exchange–labeled withdrawal wallet (Binance)

• Treated strictly as a capital source, without attribution of downstream activity

2. Staging / Provisioning Layer

• One or more intermediate wallets

• Receipt of bulk ETH

• Redistribution of identical funding amounts (0.22 ETH) to newly created EOAs

3. Primary Distribution Nodes

• Multiple EOAs exhibiting:

  • Near-identical transaction counts (≈500 ETH transfers per wallet)

  • Deterministic execution paths (21,000 gas per transaction)

  • Exclusive use of native ETH transfers

  • Short, task-bounded operational lifespans

4. Downstream Recipient Layer

• Newly created wallets

• Immediate forwarding behavior upon receipt

• Further propagation to additional newly provisioned wallets

Arrows represent observed on-chain ETH transfers only. No speculative relationships or off-chain components are included.

An interactive version of this topology, enabling inspection of individual wallets and transfers, is available via the public Breadcrumbs report:

https://www.breadcrumbs.app/reports/29061?share=ed90d23c-3f02-4809-8961-2e7d6e55a005

Previously published report: Behavioral Classification Case Study of a High-Frequency Ethereum Externally Owned Account

Behavioral Classification Case Study of an Organic Ethereum EOA

Scope Statement

This report presents a behavioral classification case study of an Ethereum externally owned account (EOA) exhibiting low-frequency, episodic transaction activity, using reproducible on-chain metrics.

The analysis focuses strictly on observable on-chain behaviour rather than attribution, profitability, or intent. Metrics include temporal activity patterns, interaction concentration, method-level execution characteristics, and execution complexity and gas consumption behaviour, with the objective of establishing a baseline behavioral profile for discretionary, non-automated EOAs.

Preliminary Finding

The analyzed wallet exhibits sparse, irregular activity with pronounced inactivity windows, limited interaction scope, extreme method dominance, and stable execution complexity. These characteristics are consistent with discretionary, non-automated EOA usage rather than continuously operating execution infrastructure.

Observation Window

The primary analysis covers transactions from block 23,750,828 to block 24,186,241, spanning 60 days from 2025-12-08 (inclusive) to 2026-01-08 (exclusive).

A nested 30-day subwindow from 2025-12-08 (inclusive) to 2026-01-08 (exclusive) was used solely as a selection criterion to confirm short-term behavioral persistence during wallet identification.

All subsequent analyses and reported metrics are computed exclusively over the 60-day observation window, unless explicitly stated otherwise.

1. Executive Summary

This report analyzes the on-chain behaviour of a low-activity Ethereum externally owned account 0x6958e3d0c29b2cc7fbf5a4d199c3bddde1b487dd, selected through a transparent, rule-based filtering process. The wallet was intentionally chosen from the lower end of the activity spectrum among eligible EOAs in order to reduce automation bias and establish an organic baseline profile.

Using temporal activity patterns, contract interaction concentration, method-level execution analysis, and execution complexity and gas consumption behaviour, the study evaluates whether the wallet’s activity aligns with discretionary EOA usage or automated execution infrastructure.

The findings indicate that the wallet is consistent with an organic, non-automated EOA exhibiting episodic transaction behaviour, limited functional scope, and stable execution paths. This case study serves as a complementary baseline reference for contrasting automated execution infrastructure with discretionary EOA usage in large-scale blockchain analytics.

2. Wallet Selection Criteria

To ensure analytical rigor and avoid cherry-picking, the wallet was selected using a structured, rule-based filtering process applied to Ethereum transaction data.

Chain

• Ethereum mainnet

Selection Logic

The wallet satisfied all of the following criteria:

• Minimum activity threshold of ≥100 transactions within a 30-day window

• Inclusion within both 30-day and 60-day activity windows, indicating non-zero persistence

• Classified as an Externally Owned Account (EOA) (non-contract)

• Explicit exclusion of labeled entities, including:

  • Centralized exchanges (CEX wallets and user deposits)

  • Institutional and fund wallets

  • Protocol-owned, infrastructure, system, validator, miner, and DAO wallets

  • Multisig and Safe wallets

Wallet Selection Note

From the filtered universe of externally owned accounts exceeding the minimum activity threshold, wallets were ordered by total transaction count in ascending order. A low-activity candidate was then selected for qualitative behavioral analysis. The selection was not informed by downstream behavioral metrics and was intended to serve as an illustrative organic baseline rather than a statistically representative sample. This approach was chosen to minimize automation bias and avoid power-user or bot-adjacent EOAs while preserving methodological transparency.

This filtering framework ensures that the analyzed wallet represents an independently operating EOA without structural indicators of custodial control or execution delegation. As a result, observed behavioural patterns reflect discretionary usage rather than infrastructural execution strategy.

3. Identity & Context

Wallet Address

0x6958e3d0c29b2cc7fbf5a4d199c3bddde1b487dd

Wallet Type

Externally Owned Account (EOA)

Primary Interaction Context

Across the 60-day observation window, the wallet interacts primarily with a single smart contract, alongside a small number of secondary contracts, including standard ERC-20 token contracts.

Lifetime interaction history shows gradual accumulation of contract interactions over time, with no evidence of abrupt operational migration, exclusive delegation to execution infrastructure, or coordinated contract handoff behaviour.

The interaction profile reflects selective usage rather than structured execution routing.

Public Attribution Context

This report does not attempt to attribute real-world identity, economic intent, or off-chain coordination. The analysis is strictly limited to observable on-chain behaviour for the purpose of execution pattern classification.

4. Temporal Activity Analysis

To distinguish discretionary EOA usage from automated execution, the wallet’s transaction behaviour was analyzed across daily and hourly time intervals.

4.1 Daily Transaction Activity

Over the 60-day observation window, daily transaction activity is sparse and irregular. Many days exhibit low single-digit transaction counts, while activity is concentrated within a limited number of short-lived bursts.

Extended inactivity windows are present between active days, with no evidence of sustained day-over-day execution or continuous operational persistence.

This pattern indicates episodic transaction submission rather than continuously operating execution logic.

4.2 Hourly Transaction Distribution

Hourly transaction distribution shows pronounced clustering within a narrow subset of hours. Several hours exhibit minimal or near-zero activity, while a small number of hours account for the majority of observed transactions.

The distribution lacks 24-hour coverage and displays clear inactivity intervals, indicating execution constrained by availability rather than time-agnostic system operation.

4.3 Temporal Behaviour Summary

Combined daily and hourly patterns indicate episodic, availability-constrained execution characterized by inactivity windows and burst-driven usage. This temporal structure diverges materially from continuously active execution controllers and is consistent with discretionary transaction behaviour.

5. Interaction Profile

5.1 Contract Interaction Concentration (60-Day Observation Window)

Within the observation window, the wallet demonstrates moderate interaction concentration toward a primary contract, accounting for the majority of observed transactions.

However, additional interactions with multiple unrelated contracts are present, including standard token contracts and low-frequency secondary interactions. No exclusive or near-total interaction dominance is observed.

5.2 Interaction Profile Summary

The interaction profile demonstrates:

• Selective contract usage rather than exclusive delegation

• Presence of auxiliary interactions beyond a primary contract

• Absence of operational migration or execution routing behaviour

This structure is characteristic of discretionary EOA usage rather than controller-style execution infrastructure.

6. Method Diversity & Execution Patterns

6.1 Method Usage Distribution

Method-level analysis reveals extreme functional concentration:

• Total unique methods observed: 1

• Method dominance ratio: 1.0

All observed transactions invoke the same function selector (0xa9059cbb, ERC-20 transfer).

Per-transaction execution remains highly repetitive, with no evidence of dynamic method composition or conditional calldata assembly across transactions.

6.2 Weekly Method Churn

Weekly method counts fluctuate in alignment with episodic activity bursts but do not exhibit cumulative growth, sustained innovation, or persistent expansion of the method set.

Periods of higher observed method counts correspond to transaction clustering rather than structural changes in execution behaviour.

This pattern reflects reuse of a fixed execution template rather than continuous strategy mutation.

6.3 Execution Profile Summary

Method-level behaviour is characterized by extreme repetition, narrow functional scope, and burst-aligned reuse of a single execution pattern. Unlike automated execution systems, method diversity reflects transactional batching rather than adaptive execution logic.

7. Execution Complexity & Gas Consumption Behaviour

Gas execution behaviour was analyzed using percentile distributions of gas units consumed per transaction, reflecting execution path complexity rather than fee pricing or bidding behaviour.

• p50: 59,557

• p90: 59,557

• p99: 262,877

The identical median and 90th percentile values indicate highly stable execution paths with minimal conditional branching. Higher-complexity transactions remain isolated and do not form a persistent fat-tailed distribution.

Such tightly clustered gas usage is consistent with repeated invocation of simple, deterministic execution paths rather than programmatic branching based on dynamic conditions.

8. Final Classification & Conclusion

Based on temporal activity patterns, interaction structure, method dominance, execution repetition, and gas consumption behaviour, the analyzed wallet is best classified as:

A discretionary, non-automated externally owned account exhibiting episodic transaction behaviour and functionally narrow execution patterns.

Key distinguishing characteristics include:

• Irregular, discontinuous temporal activity

• Pronounced inactivity windows and circadian clustering

• Limited interaction scope without execution routing

• Complete dominance of a single method selector

• Stable execution complexity with compressed gas distributions

The observed behaviour aligns with organic EOA usage and diverges materially from patterns commonly associated with automated execution controllers or MEV infrastructure.

9. Why This Case Study Matters

This analysis provides a behavioral baseline profile for organic Ethereum EOAs using the same on-chain metrics applied to automated execution infrastructure.

By demonstrating how identical analytical procedures yield structurally different behavioural signatures, the study strengthens the methodological foundation for comparative wallet classification, automation detection, and execution infrastructure analysis.

This framework can be generalized for wallet taxonomy development, behavioral clustering, and advanced blockchain analytics research.

When read alongside the previously published high-frequency EOA case study, the contrasting behavioral signatures emerge mechanically from identical on-chain metrics rather than narrative interpretation.

10. Scope & Limitations

This analysis is limited to Ethereum mainnet activity within the defined observation window. It does not assess profitability, off-chain coordination, private relay usage, cross-chain behaviour, or user intent.

All conclusions are derived solely from observable on-chain behavioural patterns rather than attribution or inferred motivation. Similar low-activity EOAs within the constrained selection universe exhibit comparable temporal sparsity and interaction dispersion, suggesting the analyzed wallet is not anomalous within its cohort.

Appendix A – Data Sources & Query Reproducibility

Query links are provided strictly for verification and reproducibility. All conclusions in this report are derived from observable behavioural patterns rather than labels, attribution, or inferred intent.

Wallet Selection & Filtering

• EOA selection and exclusion framework (30-day and 60-day persistence):
  https://dune.com/queries/6505160

Temporal Activity Analysis

• Daily transaction activity (per-day aggregation)

  https://dune.com/queries/6493748/10297583

• Hourly transaction distribution (24-hour aggregation)

  https://dune.com/queries/6493778/10283564

Interaction Profile & Contract Concentration

• Contract interaction concentration (60-day)

  https://dune.com/queries/6493755

Method Diversity & Execution Patterns

• Unique method extraction and dominance analysis

  https://dune.com/queries/6500351

• Weekly method count aggregation

  https://dune.com/queries/6500335

Execution Complexity & Gas Consumption Behaviour

• Gas consumption percentile analysis (p50 / p90 / p99)

  https://dune.com/queries/6500306
  

Reproducibility Notes

All queries reference the observation window defined in this report unless explicitly stated otherwise. Label tables were used exclusively for wallet selection and exclusion, not for behavioral inference or classification.

This report presents a behavioral classification case study of a high-frequency Ethereum externally owned account (EOA) using reproducible on-chain metrics.
The analysis focuses strictly on observable on-chain behaviour rather than attribution, profitability, or intent. Metrics include temporal activity patterns, interaction concentration, method-level execution characteristics, and execution complexity and gas consumption behaviour, with the objective of distinguishing automated MEV execution infrastructure from discretionary human-operated wallets.

Preliminary Finding

ENS Presence: The wallet has an ENS name (jaredfromsubway.eth). This does not alter or influence the behavioral classification.

Observation Window

The primary analysis covers transactions from block 23,750,828 to block 24,186,241, spanning 60 days from 2025-11-08 to 2026-01-08 (inclusive).

A nested 30-day subwindow from 2025-12-08 to 2026-01-08 (inclusive) was used solely as a selection criterion to confirm short-term behavioral persistence during wallet identification.

All subsequent analyses and reported metrics are computed exclusively over the 60-day observation window, unless explicitly stated otherwise.

1. Executive Summary

This report analyzes the on-chain behaviour of the Ethereum wallet jaredfromsubway.eth, a high-frequency externally owned account exhibiting extreme transaction volume and interaction concentration. The wallet was selected through systematic filtering criteria designed to identify consistently active EOAs while excluding known centralized, infrastructural, and institutional entities.

Using temporal activity patterns, contract interaction concentration, method-level execution analysis, and execution complexity and gas consumption behaviour, the study evaluates whether the wallet’s activity aligns with organic human usage or automated execution infrastructure.

The findings indicate that the wallet is consistent with a controller EOA coordinating automated MEV-related execution through specialized smart contracts, rather than functioning as a discretionary human-operated account. This case study serves as a baseline reference for distinguishing automated MEV infrastructure wallets from organic EOAs in large-scale blockchain analytics.

2. Wallet Selection Criteria

To ensure analytical rigor and avoid cherry-picking, the wallet was selected using a structured, rule-based filtering process applied to Ethereum transaction data.

Chain

• Ethereum mainnet

Selection Logic

The wallet satisfied all of the following criteria:

• Minimum activity threshold of ≥100 transactions within a 30-day window

• Consistent appearance across both 30-day and 60-day activity windows, indicating sustained behaviour rather than short-lived bursts

• Classified as an Externally Owned Account (EOA) (non-contract)

• Explicit exclusion of labeled entities, including:

  • Centralized exchanges (CEX wallets and user deposits)

  • Institutional and fund wallets

  • Protocol-owned, infrastructure, system, validator, miner, and DAO wallets

  • Multisig and Safe wallets

This filtering framework ensures that the analyzed wallet represents an independently operating EOA with persistent on-chain activity, rather than a known custodial, infrastructural, or governance-controlled entity. As a result, the observed behavioral patterns can be attributed to execution strategy rather than organizational artifacts.

3. Identity & Context

ENS Name

jaredfromsubway.eth

Wallet Type

Externally Owned Account (EOA)

Primary Interaction Context

Across its observable history, the wallet has interacted predominantly with two smart contracts labeled on Etherscan as:

• jaredfromsubway: MEV Bot
  0x6b75d8af000000e20b7a7ddf000ba900b4009a80

• jaredfromsubway: MEV Bot 2
  0x1f2f10d1c40777ae1da742455c65828ff36df387

However, interaction patterns are temporally segmented. Historical activity includes substantial interaction with both contracts, while the most recent 60-day observation window shows exclusive interaction with jaredfromsubway: MEV Bot 2, with no concurrent usage of the earlier contract.

This progression indicates an operational migration between execution contracts rather than parallel strategy deployment. The interaction structure is consistent with a controller EOA routing execution through specialized MEV infrastructure rather than directly engaging with a diverse set of DeFi protocols.

Public Attribution Context

The wallet and associated contracts have been referenced in prior public MEV research, including attribution work by EigenPhi, which has documented the operational behaviour of MEV extraction infrastructure on Ethereum.

This report does not attempt to attribute real-world identity, profitability, or intent. The analysis is strictly limited to observable on-chain behaviour for the purpose of execution pattern classification.

4. Temporal Activity Analysis

To distinguish discretionary human activity from automated execution, the wallet’s transaction behaviour was analyzed across daily and hourly time intervals.

4.1 Daily Transaction Activity

Over the 60-day observation window, the wallet executed 293,223 transactions, with daily counts consistently ranging between roughly 1,000 and 3,200 transactions during sampled subperiods.

Activity remains continuous across consecutive days with no observable downtime, inactivity gaps, or burst-only behaviour. This persistence indicates execution governed by continuously operating logic rather than event-driven or manually triggered actions.

4.2 Hourly Transaction Distribution

Hourly analysis shows transactions occurring across all 24 hours of the day, with no hour exhibiting inactivity. While volume fluctuates modestly, the distribution lacks sharp drops or clustering consistent with human circadian patterns such as sleep or work schedules.

The absence of circadian bias suggests execution independent of geographic location or human temporal constraints.

4.3 Temporal Behaviour Summary

Combined daily and hourly patterns indicate a continuously active execution controller operating in response to network or market conditions rather than human discretion.

5. Interaction Profile

5.1 Contract Interaction Concentration (60-Day Observation Window)

Across the full 60-day dataset, the wallet demonstrates near-total interaction concentration.

Out of 293,223 total transactions:

• 293,222 transactions (≈100.00%) were directed to
  jaredfromsubway: MEV Bot 2
  (0x1f2f10d1c40777ae1da742455c65828ff36df387)

• 1 transaction was directed to an externally owned address

No interactions with jaredfromsubway: MEV Bot were observed during this period, indicating a complete operational migration rather than concurrent usage.

5.2 Interaction Profile Summary

The interaction profile demonstrates:

• Extreme execution specialization

• Contract-level operational migration

• Absence of exploratory or discretionary protocol usage

This structure is characteristic of controller EOAs delegating execution to dedicated infrastructure contracts.

6. Method Diversity & Execution Patterns

6.1 Method Usage Distribution

Method-level analysis reveals:

• Total unique methods observed: 25,040

• Method dominance ratio: negligible (no single method materially dominates execution)

Here, “unique methods” refer to distinct calldata signatures observed across transactions rather than contract-level function selectors.

Per-transaction execution remains tightly scoped:

• Median (p50): 2 methods

• 90th percentile (p90): 15 methods

• 99th percentile (p99): 154 methods

This combination of narrow per-transaction execution paths with broad longitudinal method diversity is characteristic of automated systems dynamically assembling calldata based on market state.

6.2 Weekly Method Churn

Weekly method churn remains consistently high, ranging approximately between 2,500 and 7,300 unique methods per week, without sharp spikes or sustained decay.

This pattern suggests continuous strategy mutation and refresh, rather than episodic experimentation or static deployment.

6.3 Execution Profile Summary

Method-level behaviour reflects a system optimized for repeatable execution under stable conditions while maintaining dynamic adaptability over time.

7. Execution Complexity & Gas Consumption Behaviour

Gas execution behaviour was analyzed using percentile distributions of gas units consumed per transaction, reflecting execution path complexity rather than fee pricing or bidding behaviour.

• p50: 159,527

• p90: 424,576

• p99: 1,096,588

The distribution indicates that most transactions follow narrowly scoped and repeatable execution paths, while a smaller subset invoke significantly more complex logic under specific conditions. This fat-tailed execution profile is consistent with automated systems that dynamically branch execution flows based on market state or strategy conditions.

Such variability in execution complexity is unlikely to arise from manual or discretionary transaction submission and instead reflects programmatic control over conditional execution paths within specialized smart contract infrastructure.

8. Final Classification & Conclusion

Based on temporal activity, interaction concentration, execution complexity, method churn, and execution complexity and gas consumption behaviour, jaredfromsubway.eth is best classified as:

An automated controller EOA coordinating MEV-oriented execution through specialized smart contract infrastructure.

Key distinguishing characteristics include:

• Continuous, non-circadian execution

• Near-total interaction concentration

• Clear operational migration between execution contracts

• Narrow per-transaction execution with broad strategic coverage

• Conditional execution complexity reflected in fat-tailed gas consumption

In contrast, discretionary human-operated EOAs typically exhibit temporally clustered activity aligned with user availability, including observable inactivity windows; interaction dispersion across multiple protocols reflecting exploratory or portfolio-driven usage; limited execution-path diversity over time with repeated invocation of a small set of contract functions; and gas bidding behaviour characterized by coarse, manually adjusted fee tiers rather than continuous conditional escalation.

The observed behaviour diverges materially from patterns commonly associated with organic human usage, discretionary DeFi participation, or exploratory activity.

9. Why This Case Study Matters

This analysis provides a baseline behavioral template for identifying automated MEV controller wallets on Ethereum using purely on-chain metrics.

Rather than relying on labels, attribution, or reputation, the methodology demonstrates how execution structure, temporal persistence, and interaction concentration can reliably distinguish infrastructure wallets from organic EOAs.

This framework can be generalized for MEV detection, wallet classification systems, risk scoring, infrastructure mapping, and advanced blockchain analytics research.

10. Scope & Limitations

This analysis is limited to Ethereum mainnet activity within the defined observation window. It does not assess profitability, off-chain coordination, private relay usage, cross-chain behaviour, or the specific MEV strategies employed. Conclusions are derived solely from observable behavioral patterns rather than attribution or inferred intent.

Appendix A – Data Sources & Query Reproducibility

Query links are provided strictly for verification and reproducibility. All conclusions in this report are derived from observable behavioral patterns rather than labels, attribution, or inferred intent.

Wallet Selection & Filtering

• EOA selection and exclusion framework (30-day and 60-day persistence):
  https://dune.com/queries/6457753

Temporal Activity Analysis

• Daily transaction activity (per-day aggregation):
  https://dune.com/queries/6474802/10259218?sidebar=none

• Hourly transaction distribution (24-hour aggregation):
  https://dune.com/queries/6474841/10260293?sidebar=none

Interaction Profile & Contract Concentration

• Contract interaction concentration (full observation window):
  https://dune.com/queries/6493583

• Contract dominance analysis (60-day window):
  https://dune.com/queries/6475411?sidebar=none

Method Diversity & Execution Patterns

• Unique method extraction and percentile distribution:
  https://dune.com/queries/6477336?sidebar=none

• Weekly method churn analysis:
  https://dune.com/queries/6477351?sidebar=none

Execution Complexity & Gas Consumption Behaviour

• Gas consumption percentile analysis (p50 / p90 / p99):
  https://dune.com/queries/6477399

Reproducibility Notes

All queries reference the observation window defined in this report unless explicitly stated otherwise. Label tables were used exclusively for wallet selection and exclusion, not for behavioral inference or classification.