# The cost of artificial latency in the PBS context

Umberto Natale111Contact Author
Chorus One
<EMAIL_ADDRESS>Michael Moser
Chorus One
<EMAIL_ADDRESS>
###### Abstract

We present a comprehensive analysis of the implications of artificial latency
in the Proposer-Builder Separation framework on the Ethereum network. Focusing
on the MEV-Boost auction system, we analyze how strategic latency manipulation
affects Maximum Extractable Value yields and network integrity. Our findings
reveal both increased profitability for node operators and significant
systemic challenges, including heightened network inefficiencies and
centralization risks. We empirically validates these insights with a pilot
that Chorus One has been operating on Ethereum mainnet. We demonstrate the
nuanced effects of latency on bid selection and validator dynamics.
Ultimately, this research underscores the need for balanced strategies that
optimize Maximum Extractable Value capture while preserving the Ethereum
network’s decentralization ethos.

Keywords— MEV, PBS

##  1 Introduction

In music, a piece played “adagio” is performed slowly, and with great
expression. This concept extends to an MEV-Boost [9] pilot that Chorus One
[16] has been operating on Ethereum mainnet. Our setup strategically delays
getHeader requests to maximize Maximum Extractable Value (MEV) capture;
correspondingly, we’ve dubbed it Adagio. We are committed to operational
honesty and rational competition. This study will disclose our results and
initial parameters, alongside an extensive discussion of node operator
incentives and potential adverse knock-on effects on the Ethereum network.

We argue that strategic latency optimization is a zero sum game which may
increasingly compromise node operator independence in favor of statistical
arbitrageurs. The practical consequence is centralization pressure over the
entire Proposer-Builder Separation (PBS) value chain, leading to ongoing and
escalating costs for Ethereum users, and ultimately node operators themselves.
This effect is likely to perpetuate as a self-reinforcing cycle, and favors
large node operators systematically through reduced payoff variance and a data
edge that scales with voting power, leading to centralization.

The goal of the article is to address and mitigate these competitive dynamics
by providing an extensive analysis informed by proprietary data from our
Adagio study. Our primary objective is to describe the auction dynamics that
give rise to latency strategies, and the associated externalities imposed on
the Ethereum network. Our secondary objective is to provide practical context
through a discussion of the Adagio setup, and node operator incentives.

PBS inefficiencies and MEV returns is discussed in Sec. 2. The concept of MEV
is intrinsically linked to time via the inherent volatility of asset prices.
In the current PBS landscape, there exists an inefficiency with regards to
auction timing, which can be strategically exploited to consistently generate
excess MEV returns [17]. Our study highlights this inefficiency by examining
the MEV-Boost auction’s bid dynamics in the context of timing games.

The cost of artificial latency is discussed in Sec. 3. We examine how latency
optimization generates excess MEV rewards at the expense of subsequent
proposers, while increasing the Ethereum burn rate, contributing to gas cost.
Further, we discuss how a delayed auction imposes additional Loss-Versus-
Rebalancing (LVR) [12] burden on liquidity providers (LPs) by exacerbating the
disconnect between on-chain asset prices and other venues, while decreasing
inventory risk for statistical arbitrageurs. We argue that all of the above
contributes to node operator centralization, and that not engaging in timing
games imposes an excess opportunity cost on node operators of any voting
power.

Empirical results from the Adagio pilot are discussed in Sec. 4. We present
our experimental setup and benchmark its performance against the network.
Further, the chapter discusses the idiosyncrasies of optimistic- and non-
optimistic relays in the context of timing games. We close with a statistical
analysis of the additional MEV revenue captured by Adagio versus a non-
optimized setup.

Through this structured approach, we aim to provide a comprehensive overview
of the effects of latency optimization within the PBS framework, highlighting
the opportunities and challenges it presents to the Ethereum ecosystem.

##  2 PBS dynamics, and the MEV-Boost auction

The Ethereum network seeks to maximize decentralization by allowing hobbyists
to run validators at a competitive cost / benefit ratio. Proposer-Builder
Separation (PBS) [7] enables this by separating the block proposal and
construction roles, with builders competing to provide maximally valuable
blocks to proposers. Complexity is pushed downstream to sophisticated
builders, whereas validators participating in consensus can access optimized
blocks featuring Maximum Extractable Value (MEV)[5] transactions, irrespective
of their degree of sophistication.

MEV-Boost works through a commit and reveal scheme where proposers commit to
blocks created by builders without seeing their contents, by signing block
headers. Only after a block header is signed, the block body and corresponding
transactions are revealed. A trusted third party called a relay facilitates
this process. This mechanism ensures that validators cannot steal the content
of a builder’s block, but can still commit to proposing said block by signing
the corresponding header.

The MEV-Boost block auction initiates one slot ahead of the block’s intended
slot. Builders submit block proposals and bids to relays to determine who gets
to construct the next block and capture MEV profits. Relays log when each bid
is received (receiving time) and, after checks, make them available to the
proposer (eligibility time). The proposer then requests the best bid’s block
header (getHeader request) from the relays, checks for the best bid, signs it
to commit to the block, and asks the relay for the full block content
(getPayload and submitBlindedBlock). Once the relay receives the signed
header, it broadcasts the full block to the network – cfr. Fig. 1 of [17].
After a delay [10], the relay sends the payload to the proposer.

The subsequent section will examine the auction dynamics inherent to the MEV-
Boost architecture, expanding on builder competition and the resultant network
behaviors. It is important to note that in our analysis, we often refer to the
receiving time of a bid [2] as its eligibility time. This conflation is
intentional and based on the close correlation between the two, with the
eligibility time typically lagging the receiving time by an estimated 100 to
200 milliseconds [3]. While relays do not explicitly provide eligibility
timestamps, our references to eligibility time are grounded on the logical
inference of this correlation.

The insights presented here are not static; they contribute to the ongoing
discourse surrounding Ethereum’s path towards a more equitable and robust,
incentive compatible setup.

###  2.1 PBS auction dynamics

Fig. 1: Representation of bid value evolution as a function of time into the
slot for different builders account. Bid values are represented with dots,
where the red line stands for the linear regression fit.

The competitive dynamics between builders give rise to the MEV-Boost auction.
Notably, a given builder’s bid performance is not solely a function of its
capital and computational efficiency, but of informational reach. Figure 1
illustrates this effect for a single slot and relay; the pattern extends to
the general case (i.e. all builders, relays, and slots). Here, builder
0xafcde4f... (pictured top left) undercompetes due to lack of information vs.
builders 0x945fc51..., 0xacfdcf4..., 0x8aab0ed..., and 0x978a35c.... All of
the latter are associated with the same entity: rsync (cfr. [18]). The last
builder featured in the graph, 0xb67eaa5..., is Titan, a highly competitive
neutral builder. While the prototypical example of an information edge is
private order flow, the definition extends to capture other systematic edges
that can increase bids, e.g. a proprietary statistical arbitrage model for an
integrated builder-searcher entity. Ultimately, information manifests as
bargaining power.

The structure we see on a granular scale – i.e. per slot and per builder –
manifests also on the aggregate level – i.e. considering all slots and all
builders. To allow for a standardized comparison of bids irrespective of their
absolute size, we define the following normalized estimator as the ratio
between a given bid and the maximum bid in the auction. That is

$R=\frac{b(t_{E})}{\max_{t_{E}\leq 2\,s}b}\,,$ (1)

where $b$ is the bid value, and $t_{E}$ is the corresponding time at which the
bid was made eligible. Here we use the 2 s mark since there is no evidence for
the network going above this limit, cfr. [6, 18].

Consider a scenario where the winning bid is 700 ETH. If, after 100
milliseconds, the relay makes a higher bid of 770 ETH eligible, this would
conventionally represent a 10% increase. In our normalized system, the $R$
value for the initial winning bid when compared to the maximum of 770 ETH
would be approximately $R=\frac{700}{770}\sim 0.9$. Conversely, if the winning
bid is only 0.1 ETH and a subsequent bid of 0.11 ETH is made eligible after
the same time interval, the increase is proportionally identical, and the $R$
value remains the same at $R=\frac{0.1}{0.11}\sim 0.9$.

Fig. 2: The top panel shows how the bid value compared to the maximum bid in
the auction evolves with time into the slot (removing bids made eligible after
2 s). The lower left panel shows how gas used to build the block evolves as a
function of eligibility. The lower right panel shows how gas used relates to
bid value appreciation. For all panels, the red line represents the median of
the distribution, the blue line represents the 25%-quantile, and the green
line represents the 95%-quantile.

Figure 2 illustrates the relationship between bid values, gas usage, and bid
eligibility over time within the MEV-Boost auction environment. The
visualization is divided into three panels, each providing a distinct
perspective on the auction’s dynamics. In the first panel, the $R$ value,
defined as the ratio of a bid to the maximum bid within a slot, is plotted
against the eligibility time in milliseconds. This panel highlights the trend
that as the eligibility time approaches the 2-second mark, the $R$ value
increases, indicating that bids made closer to the 2-second cutoff tend to be
larger relative to the maximum bid of the slot. Notably, we discern two
distinct phase transitions around -4 seconds and -3 seconds into the slot,
where the bid value sharply increases – changing the slope of the bid value
increase – suggesting the flow of new information into the strategies
implemented by builders. The second panel shows the gas used in the Ethereum
network as a function of eligibility time. This graph shows an upward trend in
gas usage as the eligibility time increases, suggesting that bids made later
in the slot consume more gas. The third panel correlates the $R$ value
directly with the gas used, illustrating how the proportionate size of a bid
compared to the maximum bid impacts the amount of gas consumed. We see that
$R$ values are generally associated with higher gas usage, reinforcing the
idea that blocks which consume more gas are more valuable. Regardless of
timing games, we emphasize how the resource use of Ethereum always be on
average 15M gas used per slot, due to the EIP-1559 controller dynamics, see
e.g. [11]. However, the impact on for the next proposer might have meaningful
consequences.

Fig. 3: (Left panel) Probability density function of the eligibility time of
the winning bids for the sample used in this analysis. (Right panel)
Cumulative probability of eligibility times for winning bids.

Figure 3 depicts the probability density function (PDF) and the cumulative
probability of eligibility times for winning bids within our analyzed sample.
The 25%-quantile, median, and 95%-quantile eligibility times are respectively
74 ms, 240.5 ms, and 1,410m s. Despite the smaller sample size compared to
[17], our findings regarding the median eligibility time are mutually
consistent, corroborating the 260 ms eligibility figure reported in [17].

Figure 2 and 3 indicate that rational validators acting within the bounds of
protocol honesty may capture additional MEV by deliberately delaying the
auction. Figure 4 extends our analysis to the payoff validators may realize by
introducing artificial latency. On the left, the probability density function
skews towards the lower end of the MEV increase spectrum, suggesting moderate
increases are more common than relatively larger one. The 25%-quantile,
median, and 95%-quantile are 0.07%, 1.28%, and 23.99%, respectively. On the
right, the cumulative probability function reflects this by showing a rapid
initial rise that tapers off. The curve demonstrates that substantial MEV
increases are possible, but statistically less likely.

Fig. 4: (Left panel) Probability density function of MEV increases per block.
(Right panel) Cumulative probability of MEV increases per block.

The data depicted in Fig. 4 has been synthesized via 1,000 simulations. Each
run extracts an eligiblity time from the cumulative distribution in Fig. 3,
and computes how much additional MEV could have been extracted if eligibility
had been delayed up to 950 ms into the slot. This methodology ensures that our
analysis reflects the empirical dynamics of bid eligibility times. The
threshold of 950 ms is used because we believe it is a safe region for
sampling, since we believe it reduces both the chance of missed slots and the
number of forks. It is worth mentioning that this parameter constitutes a
heuristic, and further research is needed.

Going forward, we use a standard delay of 950 ms unless otherwise indicated.
Exceeding this limit would mean slowing down the auction beyond one second,
which could result in an increase in forks, decreasing network efficiency. For
operators, the relative decrease in the slope of the $R$ value in the final
phase of the auction alongside a relative increase in the risk of missing the
slot shifts the risk / reward ratio of artificial latency parameters outside
the sampling region unfavorably.

###  2.2 Expected node operator return through latency optimization

We proceed by establishing an empirical framework for potential annual excess
yields that can accrue to validators optimizing for latency. This will extend
our examination of auction dynamics, and take into account factors including
the frequency and scale of MEV opportunities, network conditions, and
different latency strategies. To do so, this section will step through three
independent variables: MEV rewards per block, the number of blocks a validator
is expected to propose, and the MEV increase per block due to latency
optimization. In this way, we’ll arrive at a robust view of the additional APR
can that be realized by small- and large- latency-aware node operators, i.e.
their magnitude of incentives.

Fig. 5: (Left panel) Cumulative probability of proposer’s MEV rewards per
block from 2023.01.01 to 2023.11.27. (Right panel) zoom of the left panel in
the range 0-1 ETH.

First, we will assess the likely absolute per-block MEV payoff. Fig. 5 shows
the cumulative probability of MEV rewards per block, based on actual payload
data provided to proposers from January 1, 2023, to November 27, 2023 [13].
The steep ascent of the curve at the lower end of the MEV scale demonstrates
that the substantial majority of blocks yields a comparatively small reward.
In practical terms, rare, large opportunities make up a disproportionate share
of aggregate income. Next, to profit from MEV, a node operator needs to be
selected as block proposer. Fig. 6 illustrates the cumulative probability
distribution for block proposer selection in a week, for node operators with
two distinct levels of voting power. For a node operator with a total 13%
voting power, the expected number of block proposals in a week is 6,552. This
decreases to 504 weekly opportunities for a node operator with a total 1%
voting power.

Fig. 6: Cumulative probability for number of selection as block proposer in a
week for a node operator with 13% of voting power (left panel) and 1% of
voting power (right panel).

Combining the latency optimization payoff (Fig. 4), per-block MEV value (Fig.
5), and proposal frequency statistics (Fig. 6), allows us to quantify the
expected weekly increase of validator-side latency optimization. As these
variables are independent events, we can extract values from their respective
distributions to calculate the anticipated weekly MEV increase — see Fig. 7.

Fig. 7: PDF of weekly MEV increase expected by a node operator with 13% voting
power (left panel) and 1% voting power (right panel).

As expected, the probability density function for the validator with lower
voting power exhibits a wider spread, corresponding to comparatively higher
variance in expected MEV increases. Conversely, the validator with a higher
voting power exhibits a more concentrated distribution, corresponding to more
frequent proposals, i.e. the total additional MEV payoff is comparatively more
predictable.

Fig. 8: Cumulative probability of weekly MEV reward increases for a node
operator with 13% voting power (left panel) and 1% voting power (right panel).

Figure 8 illustrates the cumulative probabilities of the percentage-wise MEV
increase for each voting power scenario. Notably, the higher variance in the
lower voting power scenario decreases the steepness of the cumulative
probability curve. This is indicated by the broader spread in the
corresponding probability density function. For a node operator with 13% total
voting power the median weekly MEV reward increase is at 5.47%, with the lower
quartile at 5.13% and the upper quartile at 7.06%. For a node operator with 1%
total voting power, the median increase is lower 5.11%, but with a wider
spread between the 25%-quantile at 4.33% and the 95%-quantile at 9.03%. The
implication is that the return profiles for large- and small- node operators
fit different client (delegator) utility functions as they are situated at
different points of the risk / reward curve. Therefore, latency optimization
is worthwhile for small- and large- node operators.

As the time horizon extends, variance for any voting power profile will
naturally diminish due to the law of large numbers. This will likely
concentrate rewards around the 5% mark for any voting power profile. Overall,
if execution layer rewards constitute roughly to 30% of all rewards, a
latency-aware MEV strategy can enhance APR from 4.2% to 4.27%. This
corresponds to a significant 1.67% uplift of overall APR. In conclusion, this
is a significant margin, and node operators are incentivized to capture it.

##  3 The cost of artificial latency

In the previous section, we have seen how all institutional (i.e. client-
facing) node operators are incentivized to compete for latency-optimized MEV
capture, irrespective of their voting power. While the potential for equitable
competition exists as different points on the risk / return curve, the
introduction of an artificial delay into the block proposal process carries
negative externalities which extend to subsequent proposers, and the network
at large, rendering such a proposition naught.

###  3.1 Negative externalities due to an inflated ETH burn rate

In the Sec. 2.1, Fig. 2 illustrates the upward trend in gas consumption as the
auction progresses. This subsection will examine the direct correlation
between the introduction of artificial latency to the auction and its impact
on the Ethereum network’s gas dynamic; the discussion will focus on the fee
burn mechanism introduced by EIP-1559 [8].

Fig. 9: (Left panel) Burnt ETH increase for the subsequent block as function
of the eligibility of the bid. (Right panel) Burnt ETH increases for the
subsequent block as function of the bid value increase. For both panels, the
red line represents the median of the distribution, the blue line represents
the 25%-quantile, and the green line represents the 95%-quantile.

Figure 9 extends the previous visualization to map ETH burn against the bid
eligibility time. The left panel illustrates how the percentage of burnt ETH
for the next slot increases over time, and the right panel correlates the $R$
value with this percentage increase. The graph explicitly demonstrates that as
bids rise during the auction, so does the gas price for the next slot, leading
to a larger share of burned ETH in subsequent slots. The upshot is that
artificial latency imposes a hidden cost on subsequent proposers, as a
relatively larger share of their income is burned. While the base fee
increases, if the opportunity for MEV extraction remains constant, builders
are compelled to adjust the final portion of rewards they are willing to pay,
effectively burning a part of what could have been the proposer’s income. For
normal transactions, the priority fee (PF) paid remains the same regardless of
the base fee level, assuming the max fee isn’t binding. But if the max fee is
close to the base fee, an increase in base fee can lead to a reduction in the
PF available for normal transactions, affecting again the income of the next
proposer.

While sophisticated validators capture an upside from latency-optimized MEV
capture, the systematic repercussions manifest as increased gas costs and an
accelerated ETH burn rate for the subsequent proposers. The Ethereum network
seeks to maximize decentralization by encouraging hobbyists to run validators.
We demonstrate that these downside risks are significant in scale, and
disproportionately impact solo validators.

Fig. 10: (Left panel) PDF of the burnt ETH increase obtained after applying
the 950 ms standard delay. (Right panel) Cumulative probability of burnt ETH
increase obtained after applying a delay.

Figure 10 demonstrates that the introduction of artificial latency into the
auction increases the percentage of ETH burned meaningfully. The left panel
displays the probability density function for the additional percentage of ETH
burned. While the median increase of around 0.4% appears modest, the breadth
of the distribution indicates a wide spectrum of potential outcomes. Notably,
even a small increase in burnt ETH can disproportionately reduce final rewards
due to the typically larger amount of burnt ETH compared to MEV rewards.

A 0.5% rise in burnt ETH translates into a tangible reduction in MEV rewards.
For example, if an original MEV reward is 0.077 ETH with a burnt fee of 0.633
ETH, a 0.5% increase in burnt ETH would lower the MEV reward to approximately
0.074 ETH. This reduction represents a subtle yet impactful 3.9% decrease in
the proposer’s revenue. For node operators with relatively lower voting power,
i.e. who are relatively less frequently chosen to propose blocks, the tail of
the distribution poses a significant risk. Specifically, the 95%-quantile
settles around a 2% increase in burnt fees, and higher values are possible.
Consider that there is a 1% probability that the increase exceeds 5%. This can
manifest a tangible effect on the overall APR of such a provider, rendering
them non-competitive.

This additional burn rate is consequently most impactful for solo validators,
whose execution layer income would not only decrease, but be subject to
greater variance. In short, the smaller a node operator, the more likely
adverse impacts from latency games are to manifest, with hobbyist solo
validators landing on the least desirable end of the risk / reward spectrum.
The next subsection will extend the analysis to demonstrate that even
sophisticated node operators compete in a zero sum game, for which the
revenues captured from validators that do not optimize for latency serve as
the seed capital.

Before moving on, it is worth noting the presence of a negative tail in the
probability density function shown in Fig. 10. This could be attributable to
builders who place new bids not specifically to outcompete others, but to
effectively supersede, i.e. cancel, a previously leading bid. This would be
relevant when the opportunity the transaction in question seeks to exploit
dissipates.

###  3.2 A zero sum game for node operators

Fig. 11: (Left panel) Dynamics of the number of transactions included in the
block during the auction. (Right panel) Number of transactions included as a
function of the increase in the bid value. For both panel, the red line
represents the median of the distribution, the blue line represents the
25%-quantile, and the green line represents the 95%-quantile.

Figure 11 illustrates how proposer behavior can alter the composition of a
block, and consequently, the distribution of rewards. The left panel of the
figure illustrates a clear trend: as proposers delay the getHeader request,
there is a concomitant increase in the number of transactions included in a
block. This increase is intuitive; more time allows for additional
transactions to be pooled from the mempool into the block, potentially
boosting the MEV available to the proposer. The right panel of the curve
contextualizes this analysis further, demonstrating that as the reward value
of a block nears its maximum ($R$ value approaching 1), the rate at which
transactions are included rises (i.e. the slope of the curve). This suggests
that in the latter stages of a slot, as new transactions continue to enter the
mempool, builders have a larger opportunity space, increasing block value.

This dynamic is twofold. First, vertically integrated builders (who are also
searchers) can afford to place higher bids. As the time gap between
centralized exchange (CEX) and decentralized exchange (DEX) settlement
narrows, the price risk of inventory diminishes, allowing builders to bid more
aggressively and widely. The next subsection will illustrate the downside
impact of this in more detail. Secondly, this pattern implies that
transactions that may have been included in the next slot land in the current
one. This shifts potential MEV revenue from the future proposer to the
incumbent, and gives rise to a zero sum game: the gain of one player is the
loss of another.

###  3.3 An increased LVR burden on LPs

Liquidity providers (LPs) grapple with Loss-Versus-Rebalancing (LVR) [12],
where arbitrageurs exploit stale prices to the detriment of LPs. The LVR
metric captures the losses incurred by Automated Market Maker (AMM) LPs when
their liquidity is traded against by arbitrageurs reacting to price movements
between CEXs and DEXs.

LVR is sensitive to block times [1]; higher durations exacerbate the
information disconnect between venues and thus increase LP losses. The
consequence is that delaying the getHeader request extend the opportunity
window and decreases the risk for such arbitrage, imposing additional losses
on LPs that would not be incurred under standard conditions. Research has
consistently demonstrated a correlation between the first transactions in a
block, and LP losses (see e.g. [4, 14]); these transactions generally involve
cross-venue arbitrage. A successful arbitrageur has consistent access to the
early slots in the block, and requires competitive execution on the CEX-side
(i.e. fee tiers; infrastructure) to competitively bid.

In the first section of this study, we demonstrate that some builders profit
from a consistent information advantage. This can materialize as the ability
to execute CEX to DEX arbitrage competitively. The introduction of artificial
latency by validators increases the range and profitability available to such
a builder, at the expense of LPs, thus raising the aggregate cost of providing
on-chain liquidity.

###  3.4 An increase in centralization pressure

The previous sections highlighted multiple ways dynamics intrinsic to PBS
influence the Ethereum ecosystem. This section will synthesize the preceding
discussion into specific ways these lead to centralization.

We examined how strategic delays by validators in submitting getHeader
requests result in an increased ETH burn rate. This knock-on effect benefits
node operators engaging in such timing games, to the detriment of others, that
are net exposed to a higher base fee if proposing in subsequent slots.
Additionally, node operators with a relatively lower voting power are exposed
to disproportionately more variance from the long tail of percentage increases
in ETH burned. In summary, large node operators playing timing games benefit
from comparatively higher APR at lower variance to the detriment of other
operators. We also examined how late block proposals require builders to
include relatively more transactions to keep their bids competitive, thereby
draining potential MEV profit from future blocks. This again manifests a
disadvantage for smaller node operators, who propose blocks less frequently,
and are therefore more exposed to individual block payoff variance. Finally,
we highlighted how validator-side strategic timing games lead to higher LP
losses from increased LVR, shifting profit to sophisticated CEX to DEX
arbitrageurs, which can capitalize on more opportunities and bid more
aggressively due to decreased inventory risk. A share of the direct upside
again accrues to latency optimized node operators, reinforcing centralization
pressure. Additionally, LPs may diversify their capital deployment to include
a mix of liquidity provisioning and hedging through MEV-optimized staking
[14], manifesting further centralization pressure.

Across the board, the nature of MEV favors node operators with relatively
higher voting power, who naturally capture returns at lower variance.
Strategic latency games compound this effect in the ways highlighted above,
potentially manifesting risks for the Ethereum network at large due to
increased centralization, and potentially higher gas fees. Additionally, large
node operators enjoy access to a larger pool of in-client data (e.g. bid
timings from MEV-Boost), which allows more efficient hypothesis testing, and
reflects as more effective latency parameters. This edge scales with voting
power.

Within this context, node operators are compelled to employ latency
optimization as a matter of strategic necessity. As more node operators
exploit these inefficiencies, they progressively increase the benchmark rate
for returns, giving capital providers a simple heuristic via which to select
for latency-optimized setups. This further perpetuates the latency game,
ossifying it into standard practice, upping the pressure on node operators
reluctant to participate, in a self-reinforcing cycle. Ultimately, this
manifests as an environment where a node operator’s competitive edge is
defined by its willingness to exploit this systematic inefficiency.

##  4 Empirical results from the Adagio pilot

In late August 2023, Chorus One [16] launched a latency-optimized setup —
internally dubbed Adagio on Ethereum mainnet. It’s goal was to gather
actionable data in a sane manner, minimizing any potential disruptions to the
network. Until this point, Adagio has not been a client-facing product, but an
internal research initiative running on approximately 100 self-funded
validators. We are committed to both operational honesty and rational
competition, and are therefore disclosing our findings via this study.

Our pilot comprises four distinct setups, each representing a variable (i.e. a
relay) in our experiment:

  * •

The Benchmark Setup: Two relays operate without latency modifications, serving
as a control group. These allow us to measure the impact of our experimental
variables against a vanilla setup, ensuring a baseline for comparison. Both
relays are non-optimistic. Further, these function as a safety net in case the
artificially delayed relays fail to deliver blocks on time.

  * •

The Aggressive Setup: This approach pushes the boundaries of the auction’s
timing, delaying the getHeader request as much as reasonably possible on a
risk-adjusted basis (i.e. to the brink of the auction’s temporal limit). It is
designed to capture the maximum possible MEV. This setup is features a non-
optimistic relay.

  * •

The Normal Setup: This setup rationally balances MEV capture against potential
adverse impacts, and only delays the auction within a “safe” parameter space.
It carries a favorable risk-reward profile. This setup features a optimistic
relay.

  * •

The Moderate Setup: This is our most conservative setup; it terminates the
auction slightly ahead of our estimated safety threshold (100 ms prior),
minimizing risk to the network while still engaging in competitive
optimization. This setup features a optimistic relay.

The Adagio pilot is an exploration – its purpose extends beyond understanding
how varying degrees of latency optimization affect both our aggregate
profitability and the network at large. It also aims to mitigate the risk of
bid cancellation [15] when the same block is sent to multiple relays. By
employing different latency setups, we can sample in distinct time regions,
effectively preventing overlap and ensuring a more robust and efficient
bidding process. If successful, it balances between rational self-interest as
a node operators with our responsibility to the network’s integrity and
performance, i.e. exists within the context of decentralized incentive
alignment.

###  4.1 Results

In this section, we present a comprehensive outcome analysis for our Adagio
pilot. Our primary objective is to examine the influence of different relay
configurations on the timing of bid selection and eligibility; this is central
to the dynamics of the MEV-Boost auction.

First, we examine how the intrinsic latency of each relay shapes the overall
auction dynamic. Figure 12 shows the timestamps of bid selection, and the
corresponding bid eligibility times. Notably, the graphs highlight instances
where the relay response times do not correspond to the expected latency, e.g.
in the case of the normal setup’s unexpectedly quicker responses versus the
aggressive setup. This can be indicative of performance differences between
relays; a future study may analyze this further in the context of timely
payload delivery.

Irrespectively, the data confirms that our parameters generally and safely
insure bid selection within the 1-second mark of the slot. This operational
threshold is central, as it minimizes the risk of network congestion, and the
risk of forks caused by late block propagation. On the operator side, it also
minimizes the risk of a 0 payoff via a missed slot.

Fig. 12: (Left panel) Box plot of the bid selection time from the relays with
different setup. The time is with respect the slot time. (Right panel) Box
plot of the eligibility of received bid from relays with different setup. For
both panels, the red lines represent the medians of the distributions,
meanwhile the boxes represent the distributions between the 25% and 75%
quantiles.

We proceed with an analysis of each setup’s behavior:

  * •

The Benchmark Setup adheres to expected performance standards, with median bid
eligibility occurring at 344.5 milliseconds into the slot, and the 95%
quantile at 575.75 milliseconds. This seems to indicate that the intrinsic
latency (i.e. network topology or relay idiosyncrasies) can organically mirror
artificial latency. Our benchmark setup seems to select bids in the right tail
of the network distribution (see Fig. 3).

  * •

The Aggressive and Normal Setups exhibit competitive bid eligibility timings,
with the aggressive setup surprisingly under-performing the normal setup
despite its higher latency parameter. This is indicative of inherent
differences between relays, such as the time to process bids, and their
geographical placement.

  * •

The Moderate Setup outperforms the benchmark in terms of delay but lags behind
the aggressive and normal setups. This result is particularly interesting as
it indicates that non-optimistic relays could be introducing an artificial
latency parameter to remain competitive with the optimistic relays.

In summary, the data suggests that latency strategies within relay operations
carry significant implications for relay competitiveness. The aggressive setup
in particular appears to enable non-optimistic relays to operate on par with
optimistic peers. The practical upshot is that some relays can only
consistently compete through an artificial delay. An extreme case of this
would be a relay which is technically consistently non-competitive, but
captures exclusive order flow – in this case, a rational node operator will
always query it with an artificial latency parameter. This competition among
relays hints at a potential evolutionary shift in the game, where relays might
strategically delay responses to enhance their chances of providing the best
bid. This adaptation could be likely incentivized by the benefits of optimized
delay, particularly for vertically-integrated builders who may be inclined to
pay a premium for more reliable bid inclusion.

These results provide valuable insights into how strategic latency
implementation within the relay infrastructure could influence the aggregate
efficacy and competitive landscape of the MEV-Boost auction, by leveling the
playing field between different relays via custom latency parameter.

Fig. 13: Box plot of the eligibility time of winning bids. The red lines
represent the medians of the distributions, meanwhile the boxes represent the
distributions between the 25% and 75% quantiles.

Figure 13 shows the eligibility of winning bids for Adagio, compared with the
network distribution. Despite the heterogeneous latency settings for relay
setups, the data reveals a consistent pattern: our strategy predominantly
selects bids that become eligible after 500 milliseconds, with the
25%-quantile marked at 589.5 ms. This consistency highlights the Normal and
Aggressive setups as the primary contributors to winning bids, with a median
selection time of 656.0 ms and a 95%-quantile at 886.35 ms.

Critically, these findings underscore our decision to sample below the 950 ms
threshold posited in our theoretical framework; instead, we aimed for an
average delay of 700 ms. There are two reasons for this. First, in a realistic
setting, sampling around the 950ms mark can cause selections beyond the 1s
mark due a given relay’s inherent latency, or network topology. Operating
without a safety margin risks congestion and forks via late blocks, and
increases the risk of a missed slot. Second, there is insufficient statistical
evidence to justify pushing beyond this threshold. As the bid increase
flattens out after 950 ms (see Fig. 2), i.e. warps the risk / reward ratio
unfavorably. To be exact, while the median value for the ratio defined in Eq.
1 increased by 3.39% from 250 ms to 950 ms, it increased by only 0.18% between
950 ms and 1 s.

We will conclude by estimating the profit uplift realized by Adagio. In our
theoretical framework, we assumed a static delay (see. Fig. 4); in practice,
we observe a fluctuation in the eligibility time distribution. To offset this,
we will sample eligibility times from the cumulative distribution in Fig.3,
and compute how much additional MEV revenue has likely been realized for an
eligibility delay corresponding to our actual observations (i.e. see Fig. 13).

Fig. 14: (Left panel) Probability density function of MEV increases per block.
(Right panel) Cumulative probability of MEV increases per block. In both
panels, the blue line represents the theoretical model obtained assuming we
always hit the 950 ms value for the eligibility time of bids. The green line
represents the expectation obtained using the Adagio data.

Figure 14 plots the result of these simulations, comparing the theoretically
expected MEV revenue against the empirical data from the Adagio pilot. The
probability density function shows a wider variance with a higher cumulative
probability. This indicates that while the experiment leads to a broader range
of outcomes, it skews towards relatively lower MEV increases in the practical
setting. Combining the latency optimization payoff (Fig. 14), per-block MEV
value (Fig. 5), and proposal frequency statistics (i.e. using the Adagio VP),
allows us to quantify the expected annual increase of validator-side latency
optimization (i.e. empirically mirroring Fig. 7).

The simulation results plotted in Fig. 15 indicate a median MEV increase per
block at 4.75%, with the interquartile range extending from 3.92% to 9.27%.
This correspond to an APR 1.58% higher than the vanilla case, with
interquartile range from 1.30% to 3.09%. The increased spread primarily arises
from the pilot’s constrained voting power. However, a portion of it is due to
a fluctuation in bids eligibility. Further, the observed median comes in 5%
than the theoretical projection. To bridge this gap, we will update our
approach so as to minimize variance in bid selections, and maintain
eligibility times below the 950 ms threshold.

Fig. 15: PDF of annual MEV increase expected by adopting the Adagio setup. The
high spread is due to the low voting power we have with the current pilot.

##  5 Conclusions

This study has examined how strategic timing games in the PBS context take
advantage of the dynamics of the MEV-Boost auction to yield additional MEV
revenue for node operators. In this context, it discussed node operator
incentives, and highlighted how the externalities of such latency optimization
create systemic challenges for the Ethereum network. Specifically, these
include node operator centralization and associated risks, network
inefficiencies including potentially higher gas prices, and an increased LVR
burden on LPs.

We illustrated that all node operators are incentivized to compete for
latency-optimized MEV capture, irrespective of their voting power. While in
principle, the potential for equitable competition between node operators of
different size exists as different points on the risk / return curve (i.e.
variance), the introduction of artificial latency carries negative
externalities which affect subsequent proposers, rendering such a proposition
naught.

Further, we argued that, as an increasing share of node operators employs
latency optimization, these progressively increase the benchmark rate for
returns heuristic via latency-optimized setups. In this way, the opportunity
cost of not engaging in timing games increases in a self-reinforcing cycle.
Ultimately, this normalizes such optimization as standard operating procedure,
and manifests an environment in which any node operator’s competitive edge is
defined by its willingness and ability to exploit systematic inefficiencies.

We highlight how strategic timing games lead to higher LP losses from
increased LVR, benefiting statistical arbitrageurs. As the auction duration
extends, these can capitalize on more opportunities due to decreased inventory
risk and a larger disconnect between CEX and DEX pricing. A profit share
directly accrues to latency optimized node operators; additionally, LPs may
stake with such node operators to offset some of their LVR risk, manifesting
further centralization pressure.

We argued that, large node operators enjoy a systematic edge through reduced
payoff variance via a higher block proposal frequency, and access to a larger
pool of in-client data that reflects as more effective latency parameters. We
demonstrated that artificial latency can result in a higher base fee, and that
the long tail of the percentage increase in ETH burned poses a significant
risk to small node operators and hobbyist validators.

Lastly, we presented our Adagio pilot and demonstrated that latency
optimization significantly increases node operator revenue, at an estimated
1.58% boost to APR versus a non-optimized setup. We illustrated how practical
latency parameters can balance competitiveness with network health, and thus
provide insights to smaller node operators which may struggle to find
sufficient data to stay competitive. Our research emphasizes the need to take
a cautious and informed approach to latency optimization, rationally weighing
competitive need versus potential drawbacks.

Overall, this study provides insight into node operator incentives, the
dynamics of the MEV-Boost auction, and the cost of artificial latency in a PBS
context. Future work may examine specific centralization risks in depth, such
as cross-block MEV, and contribute to specific mitigation strategies, such as
MEV-burn.

## Acknowledgments

This research has been granted by Chorus One. We are grateful to G. Sofia, B.
Crain, and F. Lutsch for useful discussions and comments. We acknowledge
support from the Chorus One enginering team for the implementation of the MEV-
Boost changes. We also thank B. Monnot and C. Schwarz-Shilling for the rewiev
of the entire document.

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