Searcher-Builder Relationships

Updated 22 July 2025

Searcher-builder relationships are dynamic interactions between users who seek information and systems that assemble and deliver it, critical in web search, knowledge graphs, and blockchain ecosystems.
Methodologies such as flow-based, probabilistic, and auction-theoretic models quantify these interactions, using metrics like hub scores, profit margins, and adaptive query refinements.
Integration and exclusive partnerships, exemplified by Ethereum arbitrage and adaptive search systems, drive strategic centralization and influence market dynamics.

Searcher-builder relationships delineate the spectrum of interactions and dependencies between those who originate, specify, or pursue information, value, or transactions (“searchers”) and the systems, agents, or entities that structure, assemble, and deliver results (“builders”). This conceptual dichotomy arises in diverse fields: information retrieval, web navigation, knowledge graph search, and, more recently, blockchain systems and online market infrastructures. Contemporary research characterizes these relationships as dynamic, context-dependent, and, increasingly, as sites of strategic integration, economic centralization, and evolving interaction paradigms.

1. Conceptual Foundations and Classifications

Early formalizations in information retrieval conceptualized the web ecosystem as comprising searchers—users with explicit or implicit intents—and builders, such as search engines or content creators, that produce, aggregate, and relate data or services. Relationships are not merely static associations between artifacts; rather, they are functions of user intent and context. For example, three main types of relationships were introduced to support information-gathering tasks (0810.5428):

FactRel: Connects pages providing similar factual information, focusing on content authority.
SeekRel: Captures similarity in navigational scaffolds (i.e., outlinks), emphasizing the role of pages as guides.
SurfRel: Quantifies multi-step traversability—how naturally one can surf between two conceptual or information nodes.

Within knowledge bases and web-scale data, the notion of explanation (as in entity relationship explanation, or “REX”) makes explicit the rationale for presenting two entities as connected, bridging opaque statistical inferences and user-understandable patterns (Fang et al., 2011).

In decentralized finance (DeFi), especially Ethereum, “searcher” refers to agents that seek and exploit on-chain and off-chain arbitrage (such as CEX–DEX arbitrage), while “builders” (e.g., block builders) control transaction ordering and block composition. The searcher-builder relationship defines who captures value and how integration structures the competitive landscape (Wu et al., 17 Jul 2025, Gupta et al., 2023).

2. Methodologies for Modeling and Measuring Relationships

A rich array of computational, network-theoretic, and economic models formalize and quantify searcher-builder relationships:

Flow-based and Subnetwork Models: In web navigation, relationships are computed via maximum flow on keyword-capacitated subnetworks, combining link structure (via hub and authority scores) and content significance ((0810.5428), equations for SeekRel, FactRel, SurfRel).
Set-Theoretic and Adaptive Models: Modeling searcher input as singleton events (sets of matching documents), set inclusion and union properties illuminate how refined or overlapping queries influence engine response and expose the system’s adaptive characteristics (Nasution, 2012). For example, if one term is included in another, their singleton spaces combine additively—a foundational property for semantic coherence and bias analysis.
Probabilistic and Graphical Models: For entity-relationship (E-R) search, queries are decomposed into sub-queries over entities and relationships, modeled as nodes in probabilistic graphical models (e.g., Markov Random Fields in the Entity Relationship Dependence Model, ERDM (Saleiro et al., 2018)). This enables dynamic fusion of searcher queries with builder-provided representations, emphasizing dependency structures and learning-to-rank strategies.
Auction-Theoretic and Economic Frameworks: In blockchain systems, integration and competition between searchers and builders are modeled via auction theory. Key findings include quantifying how private order flow auctions (OFAs) and builder-searcher integration amplify advantages, exacerbate centralization, and can be described by explicit surplus formulas and probability integrals (Gupta et al., 2023). Empirical frameworks in MEV analysis operationalize profit, revenue allocation, and the impact of exclusive relationships through markout, gross return, and profit margin formulas (Wu et al., 17 Jul 2025).

3. Integration, Exclusivity, and Strategic Coupling

One critical dimension is the emergence of tightly coupled, sometimes “exclusive,” relationships between searchers and builders. This is evident in multiple domains:

CEX–DEX Arbitrage (Ethereum): Searchers form vertical integrations with specific block builders—routing most of their arbitrage volume to a partner builder. These exclusive relationships enable builders to bid more aggressively and win blocks, while searchers secure block inclusion even as they transfer a sizable share of revenue (as high as 90%) in the form of builder tips. The profit margins for searchers contracting exclusively are thus lower than for neutral searchers (10–15% vs. 30–70%) (Wu et al., 17 Jul 2025).
Order Flow Auctions and Block Proposer Separation: Integrated builder–searchers leverage private order flow to gain a discontinuous surplus advantage in block auctions, consistently outbidding non-integrated competitors. Even small differences in extraction ability are magnified when access to private order flow and top-of-block opportunities can be bundled (Gupta et al., 2023).
Information Retrieval: Builders (i.e., search engines) employ hybrid strategies—integrating textual and link-based information—to optimize for both content and navigational relevance. Precomputed subnetworks for significant keywords support efficient and scalable recommendation generation, bridging user search behavior and system architecture (0810.5428).

Beyond technical structuring, social interaction is inherent in searcher–builder relationships:

Social Search Paradigms: The conventional solitary model of querying is supplanted by frameworks where social input pervades all search phases: context framing, requirement refinement, foraging, sensemaking, and post-search distribution (0908.0595). Social interactions (brainstorming, feedback, sharing) help shape requirements, mediate sensemaking, and reinforce the validity and relevance of retrieved results. A plausible implication is that future systems incorporating community-driven or collaborative features may further blur the searcher-builder boundary.
Adaptive Search Systems: Engines modeled as adaptive systems process inclusion, semantic overlap, and the evolution of query intent, using set-theoretic or probabilistic mechanisms to continually align search results with user needs (Nasution, 2012, Saleiro et al., 2018).

5. Transparency, Explanation, and User Trust

A recurring challenge is bridging the opacity of system-generated recommendations, entity associations, or transaction sequences:

Entity Relationship Explanation: Systems such as REX formally define and rank explanation patterns—using structural, aggregate, and distribution-based “interestingness” measures—to illuminate why entities are connected (Fang et al., 2011). Presenting ranked, interpretable explanations fosters user trust and enables exploration.
Interactive Search and Feedback Loops: Real-time interactive systems (e.g., Seeker) employ embedding-based search, sequential user feedback, and Bayesian likelihood estimation to incrementally refine results, strengthening the alignment between user intent and builder output (Biswas et al., 2019). Adaptive sampling (e.g., Boltzmann exploration) allows balancing exploitation and exploration.
User-Centric Recommendations: Integrating textual and link-driven analysis, preranked subnetworks, and contextual feedback ensures that output reflects both explicit queries and inferred behaviors, enabling more focused, intent-driven navigation and discovery (0810.5428).

6. Centralization, Market Impact, and Decentralization Risks

Empirical studies underscore the trend toward centralization, reinforcement loops, and market concentration:

Market Concentration: In Ethereum CEX–DEX arbitrage, three searchers captured approximately 73% of extracted value over a 19-month period, with exclusive relationships strongly correlated with builder market share (Wu et al., 17 Jul 2025).
Centralizing Effects in PBS: Private order flow and OFAs enhance integrated builder–searchers’ dominance, which, if unchecked, reduces competitive pluralism and may undermine the decentralization ethos in block production (Gupta et al., 2023).
Walled Gardens and “Leakage”: Exclusive arrangements and integration can create “walled gardens,” distorting revenue flows, diminishing overall market efficiency, and raising systemic risks related to censorship or manipulation.
Correcting Builder Profitability Estimates: The profitability of vertically integrated builder–searcher entities is often underestimated when accounting only for explicit searcher PnL. Aggregated, reconciled profit margins—considering builder tips and block-level markouts—reveal that builders benefit from both explicit revenue sharing and market share feedback effects (Wu et al., 17 Jul 2025).

7. Design Implications and Future Directions

The research surveyed highlights a move toward systems that are simultaneously more adaptive, transparent, and prone to strategic centralization. Recommendations include:

Unbundling Access in Auction Systems: Proposals to “unbundle” access to top-of-block and block-body opportunities in Ethereum (e.g., PEPC–Boost) seek to reduce the centralization arising from integration and exclusivity (Gupta et al., 2023).
Leveraging Transparency and Explanation: Systems that provide explanation (e.g., REX) or facilitate interactive refinement (e.g., Seeker) can enhance both user trust and system robustness (Fang et al., 2011, Biswas et al., 2019).
Hybrid and Multi-Signal Models: Combining textual, structural, probabilistic, and social evidence enables more resilient, intent-sensitive, and user-centric recommendations and retrieval.
Monitoring and Mitigating Centralization: Continued empirical measurement of searcher and builder participation, profit margins, and integration patterns is essential for regulating and maintaining healthy competition and decentralization in dynamically evolving ecosystems (Wu et al., 17 Jul 2025).

In sum, searcher-builder relationships are multifaceted, shaped by interplay among user intent, adaptive computation, social context, strategic economic behavior, and the technological design of information and transaction systems. Robust modeling, transparent interfaces, and vigilant attention to centralization risks are key to sustaining balanced and effective ecosystems.