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arxiv: 2605.23648 · v1 · pith:R776HQL6new · submitted 2026-05-22 · 💻 cs.DC

Herring: Parallel Batch-Order-Fairness on DAG-based Blockchain Consensus

Marko Putnik , J\'er\'emie Decouchant This is my paper

Pith reviewed 2026-05-25 02:56 UTC · model grok-4.3

classification 💻 cs.DC
keywords batch-order-fairnessDAG-based BFTparallel graph constructionreliable broadcast piggybackingMEV mitigationNarwhal and Tuskfair ordering
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The pith

Herring parallelizes the fairness computation in DAG BFT consensus by moving graph construction after ordering and resolving missing edges via the existing broadcast layer.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Herring as the first batch-order-fairness protocol for multi-proposer DAG BFT that avoids serial execution of its fairness layer. It achieves this by performing graph construction after consensus commits and by resolving missing edges through messages already carried on the reliable broadcast primitive. The design converts the fairness step from a per-round serial bottleneck into a CPU-bound task that can run across committed subdags in parallel. Evaluations on a Narwhal & Tusk base show that Herring maintains the base protocol's throughput up to roughly 10,000 transactions per second while delivering substantially higher saturation throughput than prior batch-OF DAG protocols.

Core claim

Herring is the first γ-batch-OF DAG BFT protocol whose fairness layer parallelizes the dominant graph construction cost across committed subdags by combining post-consensus graph construction with explicit missing edge resolution piggybacked on the DAG's reliable broadcast layer, turning fair ordering from a per-round serial bottleneck into a CPU-bound task.

What carries the argument

The post-consensus graph construction paired with piggybacked missing-edge resolution on the reliable broadcast layer, which distributes fairness work across subdags.

If this is right

  • Herring tracks Narwhal & Tusk throughput closely up to roughly 10,000 tx/s.
  • It reaches roughly 90% higher saturation throughput than FairDAG-RL.
  • It reaches roughly 100% higher saturation throughput than DoD-W.
  • It substantially lowers execution latency at saturation compared with prior batch-OF designs.
  • It supplies patches that close previously unreported liveness vulnerabilities in FairDAG-RL and DoD.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same piggyback technique could be reused in other DAG protocols that need auxiliary graph information without adding a separate communication round.
  • Because the fairness step is now CPU-bound rather than latency-bound, further gains may come from multi-core scheduling of the graph-construction workers.
  • The approach removes the single-leader bottleneck that appears in both leader-based and serial DAG fairness designs, suggesting similar restructuring may apply to other ordering properties.

Load-bearing premise

Piggybacking missing edge resolution on the reliable broadcast layer adds negligible overhead and creates no new liveness or safety problems.

What would settle it

A workload in which the added messages for missing-edge resolution measurably reduce the base DAG's throughput or allow a malicious client to stall the fairness layer indefinitely.

Figures

Figures reproduced from arXiv: 2605.23648 by J\'er\'emie Decouchant, Marko Putnik.

Figure 1
Figure 1. Figure 1: Average CPU time per phase of FairDAG-RL’s fair [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Herring system overview [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Impact of the batch size (𝐵) parameter on performance of Herring, FairDAG-RL, DoD-W and Themis at an input rate of 7,000 tx/s with 𝑁 = 13, 𝑓 = 3 and 𝛾 = 1.0. the fundamental throughput gap between leader-based and DAG￾based batch-OF designs. The latency results show that Herring and DoD-W achieve the lowest execution latencies at small batch sizes (both around 2 s at 𝐵 = 25), whereas both FairDAG-RL and Th… view at source ↗
Figure 4
Figure 4. Figure 4: illustrates the throughput and latency across Herring, FairDAG-RL and DoD-W for different 𝛾 values. At 𝛾 ∈ {1.0, 0.9, 0.8}, Herring consistently tracks the offered load of 7,000 tx/s, sustaining roughly 6,900 tx/s across all three settings at sub-second latency (around 700–750 ms). FairDAG-RL reaches comparable throughput at 𝛾 = 1.0 and 𝛾 = 0.8 (around 6,800 tx/s), but with significantly higher variance an… view at source ↗
Figure 5
Figure 5. Figure 5: Impact of the client input rate on performance of Herring, FairDAG-RL, DoD-W, [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Impact of the network size 𝑁 on performance of Herring, FairDAG-RL, DoD-W and Narwhal&Tusk at an input rate of 7,000 tx/s with 𝛾 = 1.0. network size. At 𝑁 = 18, Herring achieves around 70% higher throughput than FairDAG-RL and over 175% higher throughput than DoD-W, while sustaining roughly 85% lower latency than both. These results confirm that Herring scales substantially better with network size than th… view at source ↗
Figure 7
Figure 7. Figure 7: Impact of different ratios of Byzantine nodes on performance of Herring, FairDAG-RL, DoD-W and Themis at an [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Robustness against the reversing order adversarial [PITH_FULL_IMAGE:figures/full_fig_p013_8.png] view at source ↗
read the original abstract

Transaction ordering attacks extract billions of dollars annually from decentralized finance users in the form of Maximal Extractable Value (MEV). Byzantine Fault-Tolerant (BFT) consensus protocols guarantee total order but place no constraint on how that order is chosen, leaving the door open for adversarial reordering. Batch-order-fairness (batch-OF) protocols close this gap, but existing designs pay a steep performance price for this guarantee. Leader-based protocols such as Themis concentrate all fairness decisions at a single replica, while recent DAG-based proposals FairDAG and DAG of DAGs (DoD) force their fairness layer into strictly serial execution despite running on multi-proposer DAGs. We present Herring, the first $\gamma$-batch-OF DAG BFT protocol whose fairness layer parallelizes the dominant graph construction cost across committed subdags. Herring combines post-consensus graph construction with explicit missing edge resolution piggybacked on the DAG's reliable broadcast layer, a pairing that turns fair ordering from a per-round serial bottleneck into a CPU-bound task. We also uncover previously unreported liveness vulnerabilities in both FairDAG-RL and DoD that a malicious client can trigger to halt the fairness layer indefinitely, and propose patches that we integrate into our reimplementations. We implement Herring on top of the Rust implementation of Narwhal \& Tusk and evaluate it against FairDAG-RL, DoD-W, and Themis. Herring tracks the throughput of Narwhal \& Tusk closely up to roughly $10{,}000$\,tx/s, achieves roughly $90\%$ higher saturation throughput than FairDAG-RL and $100\%$ higher than DoD-W, and substantially reduces execution latency at saturation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents Herring, the first γ-batch-OF DAG BFT protocol whose fairness layer parallelizes the dominant graph construction cost across committed subdags by combining post-consensus graph construction with explicit missing edge resolution piggybacked on the DAG's reliable broadcast layer. This is claimed to convert fair ordering from a per-round serial bottleneck into a CPU-bound task. The paper also identifies previously unreported liveness vulnerabilities in FairDAG-RL and DoD that a malicious client can trigger to halt the fairness layer, proposes patches, and reports an implementation on Narwhal & Tusk that tracks base throughput up to ~10,000 tx/s while achieving ~90% higher saturation throughput than FairDAG-RL and ~100% higher than DoD-W with reduced latency.

Significance. If the design holds and the piggybacking assumption is validated, this would be a meaningful contribution to practical batch-order-fairness in DAG-based BFT systems, enabling better MEV resistance without the serial bottlenecks of prior fairness layers. The parallelization approach, identification of liveness issues in existing protocols, and concrete implementation provide tangible value for the field.

major comments (2)
  1. [Design of fairness layer] Design description (turning fair ordering into a CPU-bound task): The central claim that explicit missing-edge resolution piggybacked on reliable broadcast adds negligible overhead and introduces no new liveness or safety issues lacks any message-complexity bounds, formal argument that the augmented broadcast preserves the original properties under Byzantine clients, or micro-benchmarks isolating the piggyback cost. This is load-bearing for the parallelization benefit, as the manuscript states that the underlying DAG construction remains the throughput bottleneck.
  2. [Evaluation] Evaluation claims (throughput and latency numbers): The reported gains (90% higher saturation throughput than FairDAG-RL, 100% higher than DoD-W) are presented without error bars, full experimental setup details, or statistical analysis. This limits assessment of whether the parallelization delivers the claimed practical benefits.
minor comments (1)
  1. The abstract references concrete implementation and evaluation but the manuscript should include explicit pseudocode or algorithmic steps for the piggybacking mechanism to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The two major comments identify areas where additional rigor would strengthen the presentation, and we address each point below with plans for revision.

read point-by-point responses

  1. Referee: [Design of fairness layer] Design description (turning fair ordering into a CPU-bound task): The central claim that explicit missing-edge resolution piggybacked on reliable broadcast adds negligible overhead and introduces no new liveness or safety issues lacks any message-complexity bounds, formal argument that the augmented broadcast preserves the original properties under Byzantine clients, or micro-benchmarks isolating the piggyback cost. This is load-bearing for the parallelization benefit, as the manuscript states that the underlying DAG construction remains the throughput bottleneck.

    Authors: We agree that the manuscript would benefit from a more formal treatment of this load-bearing claim. Section 4 explains that missing-edge information is appended to existing reliable broadcast messages of the underlying DAG layer and authenticated using the same mechanisms, preserving the original broadcast properties without introducing new message types or rounds. However, we did not supply explicit message-complexity bounds or a lemma establishing preservation under Byzantine clients. We will add a dedicated paragraph and lemma in the design section providing these bounds and the preservation argument. We will also include micro-benchmarks isolating the piggyback cost in the evaluation. These additions will be incorporated in the revised version. revision: yes

  2. Referee: [Evaluation] Evaluation claims (throughput and latency numbers): The reported gains (90% higher saturation throughput than FairDAG-RL, 100% higher than DoD-W) are presented without error bars, full experimental setup details, or statistical analysis. This limits assessment of whether the parallelization delivers the claimed practical benefits.

    Authors: We concur that greater statistical detail would improve interpretability of the results. The experiments were conducted on a 10-replica cluster using the Rust Narwhal & Tusk codebase with synthetic workloads ramped to saturation; the reported saturation points are averages across runs. We did not include error bars or variance analysis in the submitted version. In revision we will expand the experimental setup subsection with full hardware and workload parameters, add error bars derived from at least five independent runs per configuration, and include a short statistical note on the significance of the observed throughput differences. These changes will be made. revision: yes

Circularity Check

0 steps flagged

No circularity: new protocol construction with empirical evaluation

full rationale

The paper presents Herring as a novel combination of post-consensus graph construction and piggybacked missing-edge resolution on an existing reliable broadcast layer. No equations, fitted parameters, or predictions are defined in terms of the claimed outputs. No self-citations are used to justify uniqueness theorems or load-bearing assumptions. The performance claims rest on direct implementation and benchmarking against external baselines (Narwhal & Tusk, FairDAG-RL, DoD-W, Themis), which are independent of any internal fitting. The design is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The protocol rests on standard BFT assumptions and introduces no new invented entities; γ appears as a configurable fairness parameter but is not fitted to data in the abstract.

free parameters (1)
  • γ
    Batch fairness parameter that defines the order-fairness guarantee; its concrete value is not derived from data in the abstract.
axioms (1)
  • domain assumption Byzantine fault model with fewer than one-third faulty replicas
    Standard assumption invoked for all BFT consensus protocols including the base Narwhal & Tusk layer.

pith-pipeline@v0.9.0 · 5844 in / 1367 out tokens · 21155 ms · 2026-05-25T02:56:59.990437+00:00 · methodology

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