PaperlayerView exact revision on arXiv

Source-linked AI summary

Breaking Cycles for Scalable Fair Ordering in Blockchain Systems

Jinchun He, Wangjie Qiu, Yizhong Liu, Shengda Zhuo, Kwok-Yan Lam

arXiv:2609.00837v1cs.DC

TL;DR

Condorcet cycles can force fair-ordering protocols into costly batch-wide cycle resolution. FlashOrder localizes ambiguous transactions before serialization, and its prototype achieves up to 10.5× higher throughput than Themis and 4.8× higher throughput than Rashnu.

  • Problem

    Existing fair-ordering protocols face costly global cycle resolution when Condorcet cycles form, which can collapse transactions into one batch and severely degrade latency.

    Source: §2.2 · p. 3

  • Method

    FlashOrder embeds pairwise evidence into canonical one-dimensional positions, clusters nearby ambiguities in a partition hypergraph, then serializes hierarchically across and within clusters.

    Source: §1 · p. 2

  • Results

    In prototype evaluation, FlashOrder achieves up to 10.5× higher throughput than Themis and 4.8× higher throughput than Rashnu.

    Source: §9 · p. 12

  • Takeaways & Limitations

    Under explicit cluster-order conditions, FlashOrder lowers the practical cost of fair ordering while improving fairness quality.

    Source: §1 · p. 2

    FlashOrder does not claim unconditional γ-batch-order-fairness for arbitrary cyclic regions.

    Source: §8 · p. 12

Abstract

from arXiv · show

In blockchain systems, transaction order directly determines financial outcomes: unfair ordering enables front-running and sandwich attacks that have extracted over \$686M from Ethereum users. Current fair-ordering protocols aggregate pairwise receive-order evidence from replicas. Under contention or adversarial manipulation, however, Condorcet cycles force them into global strongly connected component (SCC) condensation, causing delays, coarse batches, and scaling failures. We present FlashOrder, a deterministic fair-ordering engine that localizes cyclic ambiguity before it propagates across the batch. FlashOrder embeds pairwise preferences into one-dimensional canonical positions, clusters nearby transactions with a partition hypergraph, and performs hierarchical inter- and intra-cluster serialization, replacing batch-wide SCC condensation with localized sorting and aggregation. Evaluated against Themis (CCS '23) and Rashnu (VLDB '24) on a libhotstuff-based prototype, FlashOrder achieves up to 10.5$\times$ higher throughput than Themis and 4.8$\times$ higher than Rashnu, with the latency gap widening as network scales. In controlled adversarial simulation, it reduces maximum rank displacement by 88.7\%, and under Condorcet attacks it sustains 12.0$\times$ and 9.7$\times$ higher throughput than Themis and Rashnu on average. These results show that localizing cyclic ambiguity yields stronger fairness at substantially higher throughput.

1 INTRODUCTION

FlashOrder localizes cyclic ambiguity in fair ordering, replacing batch-wide SCC condensation with canonical embedding, clustering, and hierarchical serialization. The introduction argues that this design improves throughput and fairness compared with Themis and Rashnu in the reported evaluations.

Introduction

  • Pairwise receive-order evidence supports fair precedence when sufficiently many honest replicas observe one transaction before another, without requiring synchronized clocks, trusted hardware, or strong cryptographic assumptions.The motivation is that transaction ordering determines economic outcomes, and MEV has extracted more than $686M on Ethereum alone.Source: §1 · p. 1
  • Themis [22] and Rashnu use global dependency graphs and SCC-based cycle resolution; under contention or Condorcet attacks, cyclic ambiguity can cause delays, coarse batches, unstable latency, and poor scalability.Minimizing order distortion inside cyclic regions is NP-hard and has no constant-factor polynomial-time approximation.Source: §1 · p. 2
  • FlashOrder embeds thresholded pairwise evidence into a one-dimensional signal, adaptively clusters nearby ambiguous transactions as a partition hypergraph, and hierarchically serializes across and within clusters.It replaces batch-wide SCC condensation with localized deterministic sorting and aggregation without redefining fairness or redesigning the consensus substrate.Source: §1 · p. 2
  • At n=9, FlashOrder achieves 30,680 txs/s, 10.5× over Themis and 4.8× over Rashnu; at n=101, latency is 180 ms versus 779 ms for Themis and 401 ms for Rashnu.The paper attributes the widening latency gap to localized ordering in FlashOrder versus increasingly expensive global graph processing in Themis and Rashnu.Source: §1 · p. 2
  • At γ=0.8, FlashOrder reduces maximum rank displacement to 34 positions from 301 for Themis and 287 for Rashnu; under 50 ms WAN Condorcet attacks at γ=0.6, it sustains 12.0× and 9.7× higher throughput, respectively, on average.Source: §1 · p. 2

2 BACKGROUND

Fair ordering builds on honest replicas’ receive-order evidence, but majority preferences can cycle, making strict pairwise fairness impossible. SCC-based protocols resolve cycles through batches that can grow under contention; FlashOrder instead localizes cyclic conflicts into compact clusters.

2.1 Fair Ordering Preliminaries

  • BFT consensus ensures honest replicas execute transactions in the same deterministic order and guarantees safety and liveness, but does not prevent malicious leaders from manipulating transaction sequencing.Order fairness adds the requirement that committed order preserve precedence when sufficiently many honest replicas observe one transaction before another.Source: §2.1 · p. 3
  • Pairwise receive-order evidence underlies Aequitas, Themis, and Rashnu: replicas record local transaction arrival order, and protocols aggregate those observations without synchronized clocks or trusted hardware.Timestamp schemes such as Pompe and Wendy remain vulnerable to clock manipulation and Byzantine timestamp injection, while asynchronous delays limit precise propagation-time estimation.Source: §2.1 · p. 3
  • A Condorcet cycle makes strict pairwise fairness impossible; γ-batch-order-fairness places cycle-involved transactions in one batch without an internal ordering guarantee.A deterministic tie-break, such as lexicographic transaction-identifier order, then produces a total order within each batch.Source: §2.1 · p. 3

2.2 From Fairness to Bottleneck

  • Themis and similar protocols construct pairwise-preference dependency graphs and use Tarjan SCC condensation to form indivisible batches, while recent communication- or definition-layer refinements retain dense graphs and expensive condensation.The graph-centric approach is practically fragile under contention.Source: §2.2 · p. 3
  • Under contention or Condorcet attacks, conflicting client sequences can create an intransitive cycle that forces SCC-based protocols to collapse the entire transaction set into one batch.This can cause severe latency degradation or unresponsive ordering; excessively large batches can also worsen frontrunning risks within the batch.Source: §2.2 · p. 3, Figure 1 · p. 3
  • FlashOrder localizes cyclic conflicts into small, bounded regions before they propagate, replacing dense O(m^2) graph traversal with sorting within and across compact clusters.The paper identifies global propagation of cyclic dependencies as the root cause of SCC-based fragility.Source: §2.2 · p. 3

3 SYSTEM MODEL AND PROBLEM SETUP

The section defines FlashOrder’s Byzantine fault-tolerant setting and a thresholded pairwise fairness objective: robust preferences must be ordered across batches, though Condorcet cycles can make satisfying every pair impossible.

3.1 System Model and Threat Model

  • FlashOrder assumes a partially synchronous BFT network with n known replicas and at most f Byzantine replicas, which may send conflicting transaction sequences to disrupt ordering [6] [21] [38].Each replica has a local transaction receive-order sequence; network delays, reordering, and adversarial behavior can make those observations differ. The threat model identifies induced Condorcet-style cycles as a critical stress case for SCC-based engines.Source: §3.1 · pp. 3–4
  • Order-fairness requires n > 4f/(2γ−1), equivalently γ > 1/2 + 2f/n, stricter than the standard n > 3f BFT bound.Here γ is a feasibility/slack parameter: values closer to its lower bound impose a stricter evidence-admission threshold and require larger deployments, while γ = 1 is most permissive.Source: §3.1 · p. 4

3.2 Thresholded Pairwise Fairness Objective

  • γ-batch-order-fairness orders robust transaction pairs across batches but guarantees no pairwise order within batches; Condorcet cycles can make satisfying every robust pair impossible.A pair is admitted as robust at τ = max(1, ⌊n(1 − γ)⌋ + f + 1); the section frames the challenge as meeting these constraints without global cycle condensation on the critical path.Source: §3.2 · p. 4

4 THE FLASHORDER ENGINE

FlashOrder replaces batch-wide SCC condensation with a deterministic three-phase pipeline that embeds pairwise preferences, clusters nearby transactions, and serializes them hierarchically. It localizes cycle resolution while retaining worst-case quadratic processing of pairwise evidence.

4 THE FLASHORDER ENGINE

  • FlashOrder combines robust canonical embedding, adaptive hypergraph modeling, and hierarchical order resolution to replace global SCC condensation with localized sorting and aggregation.The engine is designed as a plug-and-play deterministic state machine for BFT consensus protocols.Source: §4 · p. 4

4.1 System Architecture and Processing Pipeline

  • BFT consensus safely replicates raw observation sequences, while FlashOrder deterministically orders the agreed input as an independent state machine atop the consensus layer.Replicas derive the same global ordering from the same certified input.Source: §4.1 · p. 5

4.2 Phase 1: Robust Canonical Embedding

  • A sequential scan assigns each transaction a canonical position from robust precedence evidence, then sorts by position to form a linear baseline that localizes cycles to nearby transactions.The robust-pair threshold is τ = max(1, ⌊n(1 − γ)⌋ + f + 1); equal positions are broken by transaction identifier.Source: §4.2 · p. 5, §4.1 · p. 5

4.3 Phase 2: Adaptive Hypergraph Modeling

  • FlashOrder clusters adjacent transactions with nearby canonical coordinates into a disjoint partition hypergraph, confining cyclic dependencies within clusters instead of allowing them to grow into global SCCs.The clustering threshold δ is parameterized using fairness relaxation and measurable network latency and transaction-generation interval; clusters become nodes in a quotient graph whose edges encode inter-cluster preference dominance.Source: §4.3 · p. 5, // Phase 1: Robust Canonical Embedding · p. 6
  • The quotient graph uses a dominance margin λ satisfying 1 < λ < γn−f / ((1−γ)n+f), and residual macro-cycles are deterministically merged into super-clusters.This produces an acyclic quotient graph for lexicographic topological sorting by deterministic cluster key.Source: // Phase 1: Robust Canonical Embedding · p. 6, §4.4 · p. 7

4.4 Phase 3: Hierarchical Order Resolution

  • FlashOrder orders clusters topologically, then refines each cluster by descending net-preference score, using canonical position and transaction identifier to break ties.This refinement addresses contradictory or weakly separated precedence evidence; its fairness quality is evaluated empirically rather than guaranteed for arbitrary cyclic regions.Source: §4.4 · p. 7

4.5 Complexity Analysis

  • Pairwise evidence processing remains Θ(m^2) in the worst case, but localized serialization reduces practical cycle-resolution costs by replacing graph traversal with sequential array processing, sorting, and clustering.The quadratic lower bound follows from the number of transaction pairs; FlashOrder restructures systems-level overhead rather than eliminating pairwise relation processing.Source: §4.5 · p. 7

5 CORRECTNESS ARGUMENTS

FlashOrder’s correctness arguments establish deterministic replayability, conditional cluster-order fairness, and exact preservation of robust preferences under separation conditions.

Proposition 5.1: Deterministic Replayability

  • For fixed parameters and identical certified observation inputs, all honest replicas deterministically output the same total order L.The preference matrix, clustering, SCC merging, topological traversal, and intra-cluster sorting use deterministic rules and tie-breakers.Source: §5 · pp. 7–8

Proposition 5.2: Conditional Cluster-Order Fairness

  • When its stated conditions hold for every robust pair, FlashOrder orders each pair’s final clusters in the preferred batch order.The resulting cluster-level relation follows from same-cluster placement or a directed path in the acyclic quotient graph.Source: §5 · p. 8

Proposition 5.3: Acyclic Exactness under Separation

  • If the robust preference graph and initial quotient graph are acyclic, CP ordering is valid, and clustering preserves robust-edge separation, FlashOrder preserves every robust pair.Under these conditions, no SCC merging occurs, and topological sorting preserves the quotient edges corresponding to robust preferences.Source: §5 · p. 8

Replayable Order Verification

  • For replayable order verification, the leader supplies the n−f signed observation sequences as a certificate, and replicas reject mismatches through the consensus layer.Each honest replica replays the certified input through its local engine.Source: §5 · p. 8

6 EVALUATION … 6.3 End-to-End Performance in Deployment

The deployment evaluation finds that FlashOrder’s throughput remains closer to non-fair HotStuff than to Themis or Rashnu as replica scale and operating difficulty increase, while its ordering path localizes cyclic ambiguity.

6.1 Experimental Setup

  • The libhotstuff prototype compares FlashOrder with HotStuff, Themis, and Rashnu, using the two open-source HotStuff-integrated fair-ordering engines for apples-to-apples comparison.Deployment runs on CloudLab bare-metal machines and uses SmallBank with extreme Zipfian skew (SB_SKEW=0.99) and high interaction probability (SB_PROB=0.95); fairness-quality simulation covers larger networks and controlled adversarial sweeps beyond CloudLab scale.Source: §6.1 · p. 8

6.2 Evaluation Questions

  • The evaluation asks whether FlashOrder delivers high throughput and low latency, remains stable under stricter robust-pair admission, preserves thresholded-pair fairness, and withstands ordering-manipulation attacks.These are the questions assigned to Sections 6.3–6.6, respectively.Source: §6.2 · p. 8

6.3.1 Scaling with Replica Count.

  • At n=9, FlashOrder delivers 10.5× Themis’s and 4.8× Rashnu’s throughput; at n=101, it sustains 5,696 txs/s, or 77.2% throughput retention.At n=9, FlashOrder achieves 30,680 txs/s versus 2,920 for Themis and 6,330 for Rashnu. At n=101, HotStuff achieves 7,372 txs/s, while Themis and Rashnu drop to 1,299 and 2,504 txs/s, respectively.Source: Figure 3 · p. 8, §6.3.1 · p. 8

6.3 End-to-End Performance in Deployment

  • Across 9–101 replicas, FlashOrder maintains stable performance and a smaller throughput gap to non-fair HotStuff than either fair-ordering baseline.At n=101, FlashOrder’s average latency is 180.0 ms (p95: 213.0 ms), while HotStuff’s is 136.4 ms (p95: 154.4 ms).Source: §6.3.1 · p. 8

6.3.2 Performance under Challenging Operating Conditions.

  • At block size 800 in a 50-ms WAN, FlashOrder reaches 2,373 TPS, achieving 4.94× Themis’s and 2.25× Rashnu’s throughput.FlashOrder’s average latency is 1,650 ms (p95: 3,608 ms), versus Themis’s 7,880 ms (p95: 11,667 ms) and Rashnu’s 3,915 ms (p95: 4,883 ms).Source: Figure 4 · p. 9, §6.3.2 · p. 9
  • FlashOrder localizes cyclic ambiguity through clustering and serialization, while Themis and Rashnu incur global graph-processing costs that grow with replica count.The deployment results indicate FlashOrder’s advantage is largest when contention, batch size, and WAN delay make global SCC-style cycle resolution more expensive.Source: §6.3.1 · p. 9, §6.3.2 · p. 9

6.4 Sensitivity to Order-Fairness Parameter

As γ decreases, stricter robust-pair admission leaves FlashOrder stable while Themis and Rashnu incur performance costs under stronger evidence thresholds and cyclic structure.

6.4 Sensitivity to Order-Fairness Parameter

  • Scaling γ down from 1.0 to 0.6 raises the robust-precedence evidence threshold under τ=⌊n(1−γ)⌋+f+1, reducing tolerance for weak receive-order evidence.The stricter regime increases sensitivity to high-confidence cyclic structure from contention and Condorcet-style manipulation.Source: §6.4 · p. 9
  • At γ=0.6, FlashOrder sustains over 21.6 × 10^3 txs/s with 31.4–37.3 ms latency, resolving remaining ambiguity within bounded clusters.Its localized execution avoids larger global cycle-resolution routines under stricter robust-pair admission.Source: §6.4 · p. 9, Figure 5 · p. 10
  • As γ falls, Themis throughput drops from 3.0 × 10^3 txs/s to 2.4 × 10^3 txs/s and latency rises non-linearly from 334.3 ms to over 411.4 ms.Themis degrades sharply as the robust-pair threshold becomes stricter.Source: §6.4 · p. 9, Figure 5 · p. 10
  • Under severe topological stress, Rashnu incurs approximately a 35% latency penalty, reaching 205.8 ms, despite restricting graphs to data-dependent transactions.It still relies on graph traversal and cycle elimination when stricter thresholds leave high-confidence cycles.Source: §6.4 · p. 9, Figure 5 · p. 10

6.5 Fairness Quality under Thresholded Pairwise Constraints

Under adversarial simulation, FlashOrder improves robust-pair satisfaction and limits worst-case rank displacement compared with Themis and Rashnu. Its smaller clusters preserve robust-pair ordering through quotient paths, and its fairness lead grows under stricter admission at larger scale.

6.5 Fairness Quality under Thresholded Pairwise Constraints

  • RPS measures the fraction of strongly agreed transaction pairs ordered correctly, using threshold τ=max(1,⌊n(1−γ)⌋+f+1); MaxDisp measures worst-case positional shift from the send-time ground truth.Together, the metrics capture robust-constraint satisfaction and worst-case output distortion.Source: §6.5.1 · p. 10, §6.5 · p. 10

6.5.1 Robust-Pair Satisfaction Rate.

  • At γ=0.8, FlashOrder’s RPS is 0.873 versus Themis’s 0.813 and Rashnu’s 0.790; at γ=0.51 with n=401, it reaches 0.996.The strict-setting RPS values for Themis and Rashnu are 0.915 and 0.853, respectively.Source: Table 2 · p. 10, §6.5.1 · p. 10
  • At γ=0.8, FlashOrder’s MaxDisp is 34 positions versus 301 for Themis and 287 for Rashnu, an 88.7% reduction from Themis.At γ=0.51 with n=401, FlashOrder’s MaxDisp is 10 positions versus 300 for Themis, a 30× advantage.Source: Table 2 · p. 10, §6.5.1 · p. 10
  • At γ=0.8, FlashOrder’s largest cluster contains 54 transactions and its quotient-path coverage is 0.815, while Themis’s 339-transaction SCC has coverage 0.212.Rashnu’s conflict-restricted coverage is 0.816, near FlashOrder’s 0.812 on conflicting pairs, but its coverage across all robust pairs is 0.462; at γ=0.51 FlashOrder leads with coverage 0.910. At γ=1.0, FlashOrder maintains coverage 0.590 and MaxDisp 87, while Themis has coverage 0.185 and MaxDisp 301.Source: §6.5.1 · pp. 10–11, Table 2 · p. 10
  • From γ=0.8 to γ=0.51 at n=401, FlashOrder’s RPS rises from 0.873 to 0.996 and MaxDisp falls from 34 to 10, while Themis’s MaxDisp stays near 300.The passage reports that Fig. 7 shows how the metrics vary across γ values.Source: §6.5.1 · p. 11

6.6 Robustness under Condorcet Attacks

Under Condorcet attacks, FlashOrder sustains higher throughput than Themis and Rashnu while preserving more robust precedence relations and limiting rank reversals.

6.6 Robustness under Condorcet Attacks

  • The evaluation combines simulation with a 50 ms geo-distributed prototype deployment, injecting inverted Byzantine sequences and explicit A>B, B>C, C>A cycles amid SmallBank traffic.The deployment sweeps cycle lengths 3, 10, and 20 and fairness endpoints γ∈{0.6, 1.0}.Source: §6.6 · p. 11
  • At Dist=1, FlashOrder limits successfully flipped pairs to 49 in standard scenarios and 63 under peak adversarial load, with a 23.15× execution-time speedup over Themis.Reversal rates decline as receive-order vote-margin distance increases; a structural cluster graph avoids the cycle explosion associated with O(m^2) binary dependency graphs and helps prevent computational saturation.Source: §6.6 · p. 11
  • At γ=0.6, FlashOrder averages 1121 TPS, 12.0× higher than Themis and 9.7× higher than Rashnu; at γ=1.0, average gains are 10.1× and 5.3×, respectively.Figure 8 reports throughput across attack cycle lengths 3, 10, and 20 in the 50 ms WAN setting.Source: Figure 8 · p. 11, §6.6 · p. 11
  • FlashOrder latency stays lower in almost all attack settings: 867–1292 ms at γ=0.6 and 790–814 ms at γ=1.0.Themis latency is 1389–1600 ms at γ=0.6 and 1002–1082 ms at γ=1.0; Rashnu latency is 1139–1963 ms and 784–995 ms, respectively. FlashOrder’s 1292 ms outlier occurs at γ=0.6, cycle length 3, while throughput remains far above both baselines.Source: §6.6 · p. 11
  • At γ=1.0 and cycle length 20, Themis abandons fair ordering after the Condorcet attack breaks its tournament-graph assumption, while Rashnu falls back after over-pruning.Rashnu does not complete its intended ordering procedure in this case.Source: §6.6 · p. 12

7 RELATED WORK

FlashOrder does not redefine fairness; it provides a deterministic execution path for γ-batch-order-fairness. It localizes ambiguity through adaptive clustering and hierarchical serialization, complementing work on consensus, dissemination, proposal, and sequencer architecture.

Fairness definitions

  • FlashOrder preserves the γ-batch-order-fairness regime rather than redefining fairness, building on pairwise receive-order constraints formalized by Aequitas and adopted by Themis.Other approaches explore timestamp-style fairness, efficiency boundaries [7], decentralized clocks, distortion limits, and composable cryptographic abstractions.Source: §7 · p. 12

Ordering engines

  • Unlike SCC-based ordering engines such as Themis and Rashnu or Auncel’s weight-based linearization, FlashOrder localizes ambiguity through adaptive clustering and hierarchical serialization.Its intra-cluster Net-Preference Score uses aggregate preference rules from social choice theory as deterministic local refinement, not an optimal solution to cyclic aggregation.Source: §7 · p. 12

Protocol architectures

  • DAG-based BFT systems [14, 42, 2, 3, 40, 19, 17] improve throughput but do not solve fair ordering; FairDAG [20], DAG of DAGs, and Fides [48] integrate it into the proposer layer.SpeedyFair also decouples ordering from consensus, while Travelers [49] reduces fair-ordering communication complexity through probabilistic techniques; these approaches complement FlashOrder’s ordering-engine changes.Source: §7 · p. 12

Layer 2 sequencers

  • FlashOrder is orthogonal to centralized L2 sequencing and its MEV risks, auction-based priority such as Timeboost, and decentralized sequencer networks such as Espresso and Astria.It targets the algorithmic cost of converting replicated evidence into a deterministic fair order, regardless of who controls sequencing.Source: §7 · p. 12

8 LIMITATIONS

FlashOrder’s fairness guarantees are conditional because it aggregates pairwise evidence at the cluster level. Its intra-cluster refinement provides a reproducible order for cyclic regions, but does not guarantee all robust pairs there.

Limitations

  • Preserving a robust pair across clusters requires its signal to induce the correct quotient edge or for both transactions to be absorbed into the same final cluster.FlashOrder’s correctness arguments state this separation condition, aligning the guarantee with the quotient graph it constructs.Source: §8 · p. 12
  • FlashOrder does not claim unconditional γ-batch-order-fairness for arbitrary cyclic regions or satisfaction of all robust pairs inside arbitrary Condorcet cycles.Intra-cluster Net-Preference Score is a deterministic refinement for ambiguous or cyclic regions.Source: §8 · p. 12
  • Fairness quality in cyclic regions is measured empirically through robust-pair satisfaction and rank displacement, rather than guaranteed for every robust pair.The refinement’s role is to provide a reproducible total order.Source: §8 · p. 12

9 CONCLUSION

FlashOrder replaces batch-wide SCC condensation with canonical-position embedding, adaptive clustering, and hierarchical serialization that localize cyclic ambiguity. Its prototype and simulations report higher throughput, stronger fairness under attacks, and a replay-verifiable ordering path without changing the pairwise evidence model or consensus substrate.

9 CONCLUSION

  • FlashOrder achieves up to 10.5× higher throughput than Themis and 4.8× higher than Rashnu, retaining 77.2% of non-fairness-baseline throughput at n=101.The ordering engine shifts its critical path from batch-wide graph traversal to localized sorting and aggregation.Source: §9 · p. 12
  • Under Condorcet attacks in a 50 ms WAN, FlashOrder sustains 12.0× and 9.7× higher throughput than Themis and Rashnu on average, reducing latency from 13.0 s to 0.56 s over Themis.Source: §9 · p. 12
  • At γ=0.8, maximum rank displacement falls from 301 and 287 to 34 positions, an 88.7% reduction; at γ=0.51, RPS reaches 0.996 and MaxDisp is 10.At γ=0.8, FlashOrder’s RPS is 0.873.Source: §9 · p. 12
  • FlashOrder makes fair ordering more practical and replay-verifiable by restructuring the ordering engine without changing the pairwise evidence model or redesigning the consensus substrate.Source: §9 · p. 12
  • Future work will integrate localized ordering with DAG-based consensus, connect it more tightly to composable fairness frameworks, and extend it to broader shared-sequencing settings.Source: §9 · p. 12
Loading 2609.00837v1…