Uma Oval's OEV solution outlines a new blueprint for the future of DeFi
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Uma Oval's OEV solution outlines a new blueprint for the future of DeFi
Deep dive into OEV, exploring its origins and how it works.
Navigating Web3 tides with focused insightsAuthor: Joey Shin, IOSG Ventures
Let’s imagine a world where every financial action is more than just a simple transaction.
This is a complex world composed of information, value, and timing—all guided by the “invisible hand” of blockchain oracles. In the dynamic world of DeFi, there’s one particularly noteworthy phenomenon known as Oracle Extractable Value (OEV)—a unique form of value that can be captured due to how blockchain oracles update prices—or sometimes fail to update them. This article dives deep into OEV, exploring its origins, mechanics, and how sophisticated actors exploit the tiny gaps between real-world asset prices and their delayed reflections on-chain.
But the story of OEV doesn’t end there. We should also pay attention to innovative platforms like Uma Oval, which are exploring ways to ensure that the pursuit of OEV benefits everyone in DeFi—not just a select few. By examining both the intricacies of OEV and emerging solutions like Uma Oval, I present here my thoughts and reflections on this evolving landscape.
TL;DR
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OEV Definition: OEV arises when discrepancies exist between real-world asset prices and their lagged on-chain updates via oracles, creating profit opportunities for searchers who act based on these oracle-triggered events.
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Uma Oval Overview: Uma's Oval introduces a novel approach to managing OEV by wrapping Chainlink oracle updates and enabling searchers to bid on price feed access. These bids are then routed through MEV-Share to facilitate a private order auction process, ultimately returning value back to the protocol.
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Key Challenges Facing Oval: Oval operates within a delicate incentive balance among different entities typically involved in MEV dynamics. However, it will need real-world testing and refinement regarding potential price delays, trust assumptions tied to centralization, and other low-level parameter settings.
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Theory Behind Solving OEV: My analysis suggests that while OEV presents significant challenges, innovative solutions like Uma Oval can mitigate its negative impacts, offering a blueprint for a fairer and more sustainable DeFi future.
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Personal Insight on DeFi’s Future: I advocate for developing and implementing mechanisms that combine protocol-layer and infrastructure-layer solutions to foster a healthier ecosystem and a more balanced MEV game theory model.
A Beginner’s Guide to OEV
What Exactly Is OEV?
Oracle Extractable Value (OEV) refers to the maximum extractable value generated due to oracle price feed updates—or lack thereof. Oracles provide external data such as asset prices to smart contracts on blockchains. However, these updates are discrete rather than continuous, creating temporary information asymmetries and MEV opportunities—commonly referred to as OEV. Search bots exploit fleeting differences between on-chain prices and real-world spot prices across venues before oracle updates occur, allowing them to generate profits.
Note that this isn't limited only to externally triggered oracle operations. For instance, a large trade on a DEX like Uniswap that significantly shifts the price may create an "internal oracle update."
Common OEV strategies include front-running, where searchers monitor pending transactions and insert higher-fee trades ahead of scheduled ones, profiting from price discrepancies during latency windows; arbitrage, where arbitrageurs trade across assets using lagging oracle prices before updates finalize, locking in guaranteed profits; and most commonly, liquidations, where searchers identify undercollateralized positions based on price changes and quickly liquidate them for rewards.
OEV represents profits captured by exploiting the discreteness of oracle price feeds. Search bots extract value without contributing any utility to the underlying protocol. This value accrues to successful searchers, builders incentivized to include large transactions in blocks, and validators proposing those blocks—often at the expense of protocol users who suffer from high liquidation penalties, lost arbitrage opportunities, and degraded experience.
The Negative Impacts of OEV—and Why It Matters
OEV negatively affects dApps and harms end-users. Excessive bot activity exploiting oracle-driven arbitrage and liquidations increases overall transaction costs, as bots consistently outbid legitimate transactions to gain priority inclusion in blocks—directly inflating gas fees for real users.
Moreover, external arbitrage trades triggered by temporary oracle price mismatches reduce profits for liquidity providers within DeFi ecosystems. Even if current spot prices offer meaningful spreads, LPs are forced to accept suboptimal pricing on one side of the pool. Over time, persistent trading imbalances lead to increased impermanent loss for liquidity pools and providers. Users attempting swaps face deteriorated experiences including delayed execution, significantly higher slippage, and larger losses from forced liquidations.
Here are several common examples illustrating how OEV activities create these problems:
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Liquidations: MEV bots actively monitor decentralized lending platforms and rapidly liquidate undercollateralized loans by exploiting oracle price discrepancies, capturing reward payouts. This relies on clearing loans before oracle updates resolve data inconsistencies that expose favorable liquidation opportunities.
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Arbitrage: Bots continuously trade against lagging oracle prices on one DeFi platform, then immediately sell acquired assets on another platform reflecting current market prices. This repetitive arbitrage extracts value without providing meaningful volume or liquidity to affected applications.
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Front-running: To maximize profits from predictable oracle events, MEV bots submit high-fee transactions timed to execute just before anticipated user trades. By confirming their extraction transactions within short delay windows prior to major price updates, bots exploit price differences ahead of competing user transactions.
More troubling still is that these bots extract value without engaging in reciprocal interactions or supporting the underlying DeFi protocols. They leverage temporary oracle inaccuracies without actually trading on or providing liquidity to these platforms, further reinforcing dominant builder ecosystems. The tips paid by bots serve only to prioritize transaction placement, intensifying competition for block space and promoting infrastructure centralization instead of benefiting end-users or applications.
Overall, substantial value accumulates with oracle data hunters and top blockchain validators rather than flowing back to nourish ecosystem growth or sustainability. Draining revenue lifelines to external actors seeking unilateral profits severely hampers the growth trajectory of decentralized finance. Redirecting the capture of oracle-extractable value toward value-generating applications offers a path to transform DeFi’s core economic sustainability.
What Are Order Flow Auctions?
Order Flow Auctions (OFAs) aggregate swap intents and transactions, sorting them according to fair sequencing standards. This model aims to minimize the negative externalities of MEV strategies.
OFAs allow traders to easily publish their desired swap intent, which is then fulfilled by competing external fillers. This provides traders optimal pricing across various decentralized and centralized liquidity sources without manual rate hunting.
In an OFA structure, swappers simply post their transaction intent, while specialized fillers optimize and execute trades across multiple liquidity sources—including automated market makers and private liquidity pools—to meet demand.
Fillers actively compete to offer initial swappers the most favorable exchange rates. Their profit comes from the spread between the actual execution price and the rate offered to the intent publisher.
Key benefits of using OFAs include reduced MEV-related negative externalities through fairer transaction ordering, better pricing and efficiency for original traders, simplified access across fragmented liquidity sources, and batched transactions for improved execution.
By outsourcing order execution to competitive fillers, OFA structures simplify the swapping process across complex liquidity landscapes while delivering consistently favorable pricing to traders.
Protocol Examples Addressing OEV
API3
API3 addresses OEV issues through a groundbreaking mechanism called OEV-Share—an oracle-specific Order Flow Auction. It allows searchers to bid for exclusive rights to execute updates from API3’s data sources, which are off-chain first-party oracles owned and operated directly by API providers. The OEV profits associated with these transactions are captured and redistributed. Cryptographically signed meta-transactions from API3 oracles enable winning bidders to perform the data source update.
Introducing a competitive OEV auction into existing oracle infrastructure brings several key advantages:
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The auction maximizes extraction efficiency by aligning incentives around oracle events.
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Second, by returning revenues to affected dApps instead of allowing external accumulation, the model prevents value leakage from the network.
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Third, competitive pressure naturally lowers costs and improves timeliness of updates. This enables API3 to deliver cheap, accurate, low-latency data feeds at scale—a cornerstone for broader DeFi adoption.
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Broadly speaking, API3’s OEV architecture creates a sustainable closed-loop model benefiting all parties: search bots gain access to overlooked OEV opportunities beyond basic transaction-level MEV. DApps receive new revenue streams and pay lower fees for critical oracle services. API3 itself benefits from a profitable model that sustainably funds oracle development and operations.
How does this work within today’s “balanced” MEV incentive structure (which isn’t fully balanced due to introduced negative externalities, but whose interactions among entities are somewhat fixed)?
Searchers gain an organized pathway to capture neglected OEV opportunities beyond transaction-level MEV. While the structured bidding process might introduce minor procedural friction, improved efficiency and reduced competition ultimately increase net income. Since updates are assigned to specific searchers for execution, the system remains compatible with any block production and validation scheme—no private mempool required. Auction proceeds are redirected back to the protocol, ensuring gains that would otherwise leak out are retained.
Source: Multicoin Capital
Pyth Network is pioneering a new approach to solving OEV, leveraging its leadership position in delivering proprietary financial data from established market participants. Pyth recognizes that proprietary data sourced directly from market makers, liquidity providers, exchanges, and other core ecosystem players offers superior accuracy and freshness compared to third-party aggregated pricing.
By tapping into these high-quality data streams, Pyth’s oracles deliver significantly higher-fidelity, lower-latency pricing information to contracts requiring real-world valuations. Pyth also employs a pull-based demand model, allowing contracts to request precise price updates on-demand rather than relying on intermittent push-based delivery. This enhances flexibility while reducing network overhead.
Positioned at the crucial intersection of blockchain pricing data and contract execution logic, Pyth is well-suited to mediate access to valuable price information. By aggregating access opportunities from embedded applications utilizing its oracle feeds, Pyth intends to facilitate global order flow auctions, allocating trade access to specialized bots. Unlike models where value is strictly accumulated externally, Pyth can return profits from contract interactions back to integrated dApps.
For Pyth’s neutral oracle network, benefits include generating new revenue streams without compromising independence within the ecosystem. By consolidating information access across networks, it avoids fragmented, application-specific auctions. More competitive pricing during OEV events leads to more comprehensive value capture.
Interactions within the MEV ecosystem allow the protocol to achieve better mechanical trade-offs than current OEV lifecycle flows. Pyth’s core distinction lies in explicitly recognizing the oracle’s role by establishing proprietary data-sharing incentives between first-party data producers and contract platforms. By sourcing on-chain prices directly from market-making participants, Pyth strengthens reliability through minimized latency while aligning ecosystem incentives between data-consuming applications and data-producing platforms. Searchers achieve efficiency by gaining organized access to valuable instances of blockspace linked to oracles. Builders exchange unlimited profitability for reputational privilege in overseeing key market events. Crucially, Pyth’s strategic position enables redistribution of extracted profits back to integrated applications via aggregated data stream auctions—nourishing the ecosystem through recycled revenue growth rather than wasted leakage.
UMA Oval (Oracle Value Aggregation Layer)
Source: https://medium.com/uma-project/announcing-oval-earn-protocol-revenue-by-capturing-oracle-mev-877192c51fe2
How It Works
UMA Oval integrates with Chainlink’s existing price feed infrastructure and leverages Flashbots’ MEV-Share architecture to facilitate order flow auctions around oracle updates.
When Chainlink submits a price update to the blockchain, Oval effectively wraps access to the latest data. This allows search bots to bid competitively for unlocking rights and the ability to “pre-run” these price-feed transactions, capitalizing on OEV opportunities.
Trusted intermediary nodes, known as Oval Nodes, validate searcher bids and configure refund rules for value distribution. They submit unlock transactions releasing the held update and associated pre-run bid as a bundle via MEV-Share.
MEV-Share runs a standardized private order flow auction, coordinating across a broader Builder and Validator network. The winning bidder includes their bundled pre-run transaction along with the price feed unlock, enabling them to exploit arbitrage or liquidation events.
Then, per refund rules set by the Oval Node, a portion of the profits is redirected back to lending platforms and other protocols integrated with Oval, while standard amounts are allocated to Builders and Validators (achieved through improved clearance bonus rates inherent in the Oval mechanism). Thus, value returns to applications instead of being entirely captured by search bots and external validators.
It’s important to note that apart from Builders and the protocol itself, no other party is directly impacted in the current MEV flow. Searchers use existing technology, making integration seamless, while fees are reallocated from Builder profits back to the protocol—controlled via metadata in bundled transactions. Validators still get paid for proposing blocks, funded from Builder profits, though this may slightly increase block inclusion latency during periods of high congestion (to be discussed further). However, Builders gain access to a steady stream of private order flow via MEV-Share, incentivizing block production—especially when MEV value is high—leading to higher fee allocations. It also discourages malicious behavior, as MEV-Share can blacklist bad actors from the protocol.
In summary, Oval leverages existing oracle and MEV architectures to access valuable data feed updates. By controlling release timing, it enables search auctions and redirects a portion of generated profits back to affected applications.
Oval’s Trust Assumptions
The Oval mechanism involves three core components: the integrating protocol, Oval Nodes that control the auction, and Builders/Miners responsible for transaction ordering and confirmation. This introduces potential trust concerns:
Protocols rely on Oval Nodes to set accurate refund rules and timely publish price updates without delay or censorship. In the worst case, the protocol may lose revenue that would have gone to Builders and face minor price update delays—but core functionality relying on Chainlink’s base feed remains intact.
Oval depends on MEV-Share and Builders not leaking updated values, altering searcher preferences, or modifying pre-run payloads. Worst-case scenarios don’t break protocol operations but could result in lost Builder revenue and slight delays.
Both Oval and MEV-Share trust Builders to follow bundling rules and refrain from splitting transactions to steal profits. Oval allows users to choose trusted Builders. From a Builder’s perspective, the incentive to earn OEV is outweighed by the risk of being banned from receiving future private auction flows. Flashbots has thoroughly explored and tested this equilibrium, where incentives prevent malicious Builders from stealing MEV profits:
(Github: https://github.com/flashbots/dowg/blob/main/fair-market-principles.md)
In the worst case, a specific liquidation unfolds exactly as it does today—a Builder stealing OEV is equivalent to a Builder capturing MEV as they do now.
While reputation and financial incentives usually enforce good behavior, reliance on intermediaries introduces risks. If Oval Nodes fail to publish updates or redirect revenue, income capture halts—but core pricing continues via Chainlink’s underlying feed.
In summary, Oval leverages existing oracle and MEV infrastructures to access valuable data feed updates. By controlling release timing, it enables search auctions and redirects a portion of generated profits back to affected applications.
Potential Risks and Counterarguments
A key question is why UMA chose an intermediary auction model via Oval instead of implementing on-chain Dutch auctions directly within lending protocols for liquidation events. Compared to automated liquidation incentives, Dutch auctions may yield slower and lower revenues for platforms. For high-risk scenarios like undercollateralized loans, maximizing speed and reliability is crucial. Oval’s use of existing MEV infrastructure helps ensure liquidity in such cases.
Another concern is whether users might bribe validators to avoid proposing blocks containing new data unlocks, thereby censoring updates. However, sustaining such attacks over multiple blocks would be prohibitively expensive. Users must vastly outbid the existing tips already received by Builders and Validators to prioritize their transaction bundles. Unless in extreme cases, revenue-maximizing incentives still favor inclusion over censorship.
Another risk is whether Chainlink itself could build an alternative proprietary MEV capture system around its own feeds instead of integrating with intermediary solutions like Oval. A mitigating factor is that redirecting MEV revenue back to oracle providers serves as a useful funding mechanism for Chainlink’s ongoing development. Oval offers a validated pathway at the protocol level to achieve this goal.
Additionally, trust assumptions are largely mitigated by potentially minimal price delays—as previously noted, at most 3 blocks in likely scenarios. Within normal operation of lending protocols, a 3-block delay is not expected to have measurable impact. This differs significantly from how price delays affect fast-moving markets or rapidly evolving product types. When liquidations are needed, inclusion in the next block (zero delay) occurs 90% of the time, and within two blocks, 99%. UMA experts do not believe such delays cause price movements large enough to exhaust existing liquidation buffers.
Finally, a potential vulnerability is whether Builders responsible for order and transaction confirmation could steal OEV profits via backruns instead of respecting the auction mechanism. Yet, aligned incentives still favor compliance with Oval’s system to maintain access to Flashbots’ private order flow. Reputational consequences and the risk of ecosystem-wide exclusion provide strong deterrents, and the potential one-time gain pales in comparison to sustained income from rule-following participation.
Our Thoughts on OEV
OEV – Overall Reflections
Although numerous solutions exist to address OEV—particularly those aiming to recycle value back into protocols and ecosystems—users still bear some negative impact. Solutions like Broadcaster Extractable Value (BEV) aim to alleviate MEV pressures on the user side and may represent an interesting direction for future OFA-based protocol designs. To further mitigate certain trust assumptions inherent in OFA models, we look forward to seeing new OFA mechanisms implemented directly at the protocol layer.
For example, generalizing OEV to include even internal price changes (as introduced earlier) allows protocols to further reduce negative externalities. Taking Oval as inspiration, just as wrappers can intermediate access to external oracle events for value redistribution, protocols can treat impactful transactions as internal data updates.
For instance, Uniswap could set a threshold where any trade exceeding $X must route through an Oval-like wrapper system. This would allow Uniswap to auction access rights, letting bots backrun or arbitrage these large trades.
Then, just as Oval returns value from liquidations to lending platforms, such a Uniswap implementation could return a portion of the profits from large-trade impacts back to the Uniswap protocol, liquidity pools, LPs, or even end-users.
Thoughts on Uma Oval
While UMA Oval cleverly leverages existing infrastructure to capture and redirect OEV, the system relies on fragile incentive alignments and trusted intermediaries, introducing security risks.
Oval Nodes and the order flow mechanism offer optimizations but open attack vectors. In worst-case failures of intermediary trust or incentive models, critical data flow delays may occur, enabling more arbitrage-related value extraction.
Nevertheless, this approach does mitigate certain negative externalities of the current paradigm. As a transitional solution enhancing sustainability, Oval may bring meaningful revenue to affected applications. Still, concerns about increased centralization, transparency, and latency remain—and without thorough real-world testing, these could become future attack vectors.
Overall, UMA Oval represents an innovative attempt to reclaim value leakage, but it may not fundamentally resolve all core incentive issues enabling extraction opportunities. Like any novel crypto-economic system, these mechanisms require extensive review, audits, and real-world testing under diverse operational conditions before their true robustness and resistance to exploitation can be assessed.
I am excited to see Oval shifting the conversation and inspiring continued research, as it tackles prominent issues in the OEV space that have yet to be directly addressed. But as adoption considerations unfold, a comprehensive understanding of risks versus rewards will be essential.
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