Finder Benchmark Overview

• Finder Benchmark is a rigorous protocol that uses metrics like findability, recall, and bias to evaluate retrieval systems.
• It standardizes dataset construction, query generation, and reporting to enable reproducible, system-level comparisons.
• It applies across fields such as information retrieval, astronomy, and genomics, driving empirical progress with precise evaluations.

A Finder Benchmark is a rigorous protocol, dataset, or suite of metrics designed to quantitatively evaluate the performance of algorithms and systems whose core task is “finding” or retrieving target objects, patterns, or structures within large or noisy collections. This concept underpins empirical progress in broad areas including information retrieval, software analysis, astronomy, genomics, machine learning for noisy data, and explainable AI, where the primary scientific and engineering challenge lies in reliably locating, extracting, or aligning relevant entities. Finder Benchmarks are characterized by precise definitions of recall, accuracy, coverage, or accessibility, alongside standardized testbed collections, query sets, and reporting guidelines for reproducible, system-level comparison.

1. Foundational Principles and Metric Formalization

The definition of a Finder Benchmark hinges on the explicit construction of evaluation metrics reflecting the realistic task difficulty and user expectations. In information retrieval (IR), the Findability score $f(d)$ for a document $d$ is formalized as:

$$f(d) = \frac{1}{|Q_d|}\sum_{q \in Q_d} \xi(p_{d,q}, c)$$

where $Q_d$ is the set of all queries for which $d$ is relevant, $p_{d,q}$ is the rank of $d$ in the retrieval output for $q$, $c$ is a scan-depth cutoff, and $\xi$ is a user-convenience (effort) function such as the inverse rank $\xi_\mathrm{inv}(p, c) = 1/p$ for $p \leq c$ or $0$ otherwise. This protocol yields a per-document probability of being found by a user with realistic search behavior, offering a user-centric rather than system-centric perspective on accessibility (Sinha et al., 2023).

In the context of astrophysics and cosmology, completeness and purity functions measure the probability that real clusters, halos, or groupings (true positive targets) are detected and that detected candidates are genuine, respectively. For example, the Voronoi Tessellation finder employs completeness $C(M, z)$ and purity $P(M, z)$ as fundamental parameters in its selection function (Soares-Santos et al., 2010). Software analysis benchmarks rely on metrics such as $F_1$ score, defined in terms of true/false positives and negatives (Zhao et al., 4 Aug 2025).

2. Benchmarking Protocols and Workflow

A rigorous Finder Benchmark prescribes end-to-end protocols, including dataset construction, query or target generation, execution of candidate finders, and statistical reporting:

• IR Findability: For each document, generate up to 50 known-item queries via an automated, discriminative strategy. For each query, run the retrieval system, record the rank or absence of the target, aggregate $\xi(p, c)$, and compute $f(d)$. Aggregated metrics include mean findability $\langle f \rangle$ and findability bias (Gini coefficient $G$) for fairness and accessibility inequality (Sinha et al., 2023).
• Scientific Source Finding: In spectral-line astronomy, input data cubes are synthesized with known sources, realistic noise, and beam convolution. Source finders like Duchamp are assessed using completeness ($C = N_\mathrm{genuine}/N_\mathrm{true}$), reliability ($R = N_\mathrm{genuine}/N_\mathrm{detected}$), and parametric accuracy as a function of SNR, position, width, and integrated flux (Westmeier et al., 2011).
• Software Analysis: For code clone and TPL (third-party library) detection, ground-truth labels are manually curated over randomized project samples, allowing precise estimation of precision, recall, and $F_1$ at confidence levels (Zhao et al., 4 Aug 2025).
• Domain-Specific Retrieval: For financial RAG systems, FinDER provides 5,703 query-evidence-answer triplets, each tied to annotated passages from 10-K filings, enabling Recall@K, MRR@K, and answer correctness assessment (Choi et al., 22 Apr 2025).

These protocols are typically fixed with respect to datasets, parameter cap settings, and workflow, ensuring benchmark reproducibility and result comparability.

3. Applications Across Domains

Finder Benchmarks have become indispensable across a wide spectrum of research areas:

• Information Retrieval: Findability as defined above is implemented to probe the accessibility of items in large benchmarks such as TREC Robust04, WT10g, and MS MARCO, each with tens of millions of queries and documents. The protocol generalizes to click-based convenience models, variants of $\xi$, and other user behaviors (Sinha et al., 2023).
• Software Engineering: JC-Finder measures clone-based library reuse in Java against large Maven corpora and thousands of GitHub projects, directly confronting the gap between package manager metadata and unauthorized code cloning (Zhao et al., 4 Aug 2025).
• Astronomy and Cosmology: Benchmarks for group/halo finders, e.g., the “Haloes gone MAD” project and the Extended Halo-based Group Finder, leverage massive N-body simulations and mock observations to systematize cross-finder performance for completeness, mass/richness assignments, and spatial recovery (Yang et al., 2020, Knebe et al., 2011).
• Genomics: Benchmarks for FM-index based pattern matchers (such as FindeR) standardize short- and long-read alignment throughput, energy efficiency, and comparative speedups across CPU, GPU, ASIC, and PIM-based architectures (Zokaee et al., 2019).
• Explainability in Machine Learning: CAMBench-QR introduces structure-aware metrics, including the Finder Mass Ratio (FMR), to systematically measure if visual explanations in QR code recognition localize required “finder patterns” without spilling saliency into the background (Chakraborty et al., 20 Sep 2025).
• Noisy Data Analysis: The FINDER framework benchmarks feature extraction and classification in low-SNR, small-sample regimes using biomedical and remote sensing datasets, using AUC and accuracy metrics under Leave-Pair-Out Cross-Validation (Murphy et al., 22 Oct 2025).

4. Interpretation of Results and System Comparison

Finder Benchmarks quantitatively enable system comparison with multidimensional insight:

Domain	Main Metric(s)	Notable Result
IR (Findability)	$\langle f \rangle$, $G$	DFR-PL2 achieves highest findability and lowest bias (Sinha et al., 2023)
SW (JC-Finder)	$F_1$, Precision/Recall	JC-Finder outperforms CENTRIS4J by $\Delta F_1=0.427$ (Zhao et al., 4 Aug 2025)
Astronomy (Duchamp)	Completeness, Reliability, Positional Accuracy	$>80\%$ completeness at $>3\sigma$ SNR (Westmeier et al., 2011)
Genomics (FindeR)	Throughput, Throughput/W	$28.2\times$ speedup over ASIC, $>10^4$ qps/W (Zokaee et al., 2019)
ML Explainability (CAMBench-QR)	Finder Mass Ratio, Background Leakage	EigenGrad-CAM balances FMR and low leakage (Chakraborty et al., 20 Sep 2025)

Key findings often include system ranking across parameter settings, effect of data scale, statistical significance (e.g., $p<0.01$), trade-offs between accuracy and fairness, and identification of \textit{regimes} for optimal performance.

5. Recommendations, Limitations, and Extensions

A well-designed Finder Benchmark protocol prescribes best practices and highlights pitfalls:

• Always report both overall accessibility (mean) and equity (Gini or Lorenz curves).
• Extensions include varying user models, scan depth, or error models to simulate realistic heterogeneity (Sinha et al., 2023).
• Complementary analysis of “retrievability” and “findability” can reveal system-specific biases invisible to traditional metrics.
• For software and source detection, aggressive filtering of trivial or duplicated units greatly improves precision while preserving recall (Zhao et al., 4 Aug 2025).
• In noisy-data regimes (biomarkers, remote sensing), benchmarks clarified that Finder-style eigenspace projection methods yield substantial AUC and accuracy gains precisely in data-deficient or SNR-limited settings, with diminishing returns in linearly separable cases (Murphy et al., 22 Oct 2025).

Open limitations include dependence on parameter heuristics (e.g., query/target generation, eigenvalue truncation), binary-only classification in some frameworks, and sensitivity to the completeness of ground truth in real-world, label-scarce scenarios.

6. Scientific Impact and Future Directions

Finder Benchmarks have formalized cross-system evaluation along axes inaccessible to classical precision/recall or aggregate effectiveness, introducing accessibility, user-centricity, and structural fidelity into empirical practice. They enable:

• Principled comparison of algorithms even when legacy metrics saturate or offer misleading rankings.
• Domain-adaptive extensions, e.g., for click models in IR, energy scaling in genomics, structure-aware saliency in visual AI, and fairness in document accessibility.
• New research in bias mitigation, system robustness, and human-aligned evaluation (e.g., using user click-through data or session-based convenience estimates).

Ongoing challenges involve automating and standardizing ground-truth construction at scale, developing holistic multi-metric dashboards, and incorporating Finder Benchmarks into iterative system development pipelines across scientific domains.