CLEAR Metrics: A Multi-Domain Evaluation Framework

Updated 31 December 2025

CLEAR Metrics are a multipurpose, domain-specific evaluation framework that integrates distinct metrics to assess performance in fields like genomics, AI, radiology, and energy systems.
They incorporate mathematically-founded measures that quantify performance nuances, enabling precise cross-domain comparisons and targeted system improvements.
Empirical outcomes demonstrate CLEAR’s superiority over legacy metrics by offering actionable diagnostics and robust benchmarks for quality and efficiency enhancements.

CLEAR Metrics

The acronym "CLEAR" encompasses a diverse array of methodologies and metrics developed independently across multiple domains—ranging from genomics, vision–language navigation, machine unlearning, radiology report evaluation, green AI, semiconductor technology assessment, and deep field galaxy spectroscopy. Despite distinct instantiations, all CLEAR systems operationalize rigorous, multidimensional evaluation frameworks, often with specialized metrics engineered to address the unique demands and failure modes of their application settings. Below, representative CLEAR metric systems are described, with emphasis on their formal definitions, operational procedures, quantitative performance, and scientific purposes.

1. Multidimensional Metrics Across Domains

CLEAR metrics are instantiated in domain-specific fashion, each targeting critical quality axes:

RNA-seq (lcRNA-seq) Filtering: In "Coverage-based Limiting-cell Experiment Analysis for RNA-seq", metrics quantify per-transcript positional coverage bias (μₖ), model distributional bimodality of coverage along transcript length, and introduce explicit cutoff criteria anchored to the onset of bimodality in low-coverage bins. This yields sample-specific, empirical filtering of transcripts prior to DEG analysis, targeting increased reproducibility and robustness in low-input RNA-seq (Walker et al., 2018).
Sentence Representation (Contrastive Learning): CLEAR introduces a full suite of downstream evaluation metrics to assess sentence embeddings, including: classification accuracy, F₁ score, Matthews Correlation Coefficient, Pearson/Spearman correlation with human similarity ratings. Task-specific averaging and aggregation strategies are used to produce interpretable, robust system-level scores (Wu et al., 2020).
Vision-Language Navigation (VLN): Four principal metrics—Success Rate (SR), Success weighted by Path Length (SPL), normalized Dynamic Time Warping (nDTW), and Success weighted by DTW (sDTW)—systematically evaluate multilingual, cross-environment generalization and trajectory fidelity (Li et al., 2022).
Radiology Report Evaluation: The CLEAR tabular framework scores across six axes (presence, first occurrence, change, severity, location, recommendation) per medical condition, using per-attribute accuracies and text similarity measures (BLEU-4, ROUGE-L, LLM scorer), and aggregates via attribute-averaged precision, recall, and composite scoring. This enables granular analysis of report fidelity aligned to expert radiologists (Jiang et al., 22 May 2025).
Machine Unlearning: CLEAR benchmarks compute ROUGE-L (exact-match recall), probability scores, truth ratio (robust answer preference), and a forget quality score via two-sample KS statistic, producing harmonic-mean summaries for "Real", "Retain", and "Forget" performance regimes, and supporting strict statistical indistinguishability with retrained gold baselines (Dontsov et al., 2024).
Green AI (Componentwise Energy): In transformer inference, CLEAR employs repeated modular execution and ms-level telemetry to extract per-component energy (σ_c/ Ē_c), %Capture, and energy-per-FLOP, enabling benchmarking and comparative analysis at the level of attention, MLP, norm, and embedding layers (Jain et al., 3 Oct 2025).

2. Formal Definitions and Metric Formulas

Each CLEAR system introduces formally defined, reproducible metrics:

Domain	Key Metric(s)	Core Formula(s)
lcRNA-seq Filtering	Positional mean μᵢ, bimodality params	μᵢ = [2/(L–1)]·(∑ₖk·dₖ / ∑ₖdₖ) – 1
Sentence Representation	Accuracy, F₁, MCC, Pearson/Spearman	e.g., Accuracy = (1/N)∑ 𝟙(ŷᵢ = yᵢ)
VLN	SR, SPL, nDTW, sDTW	SPL = (1/N)∑ Sᵢ * (Lᵢ / max(Pᵢ, Lᵢ))
Radiology Report Eval	F1, Attribute Acc, Similarity	F1 = 2·Prec·Rec/(Prec+Rec); Sim(e,ĕ)
Machine Unlearning	ROUGE-L, ProbScore, TruthRatio, KS	ROUGE-L, ProbScore = p(y₁
Green AI (Energy)	Ē_c, E/FLOP, %Capture	E(c;L) ≈ E₀_c + k_c·FLOPs(c;L)

Metric computation is domain-adapted: e.g., RNA-seq positional mean quantifies the relative 5'/3' coverage bias to flag noise, while radiology CLEAR tabulates attribute correctness per diagnosis, and transformer CLEAR decomposes end-to-end energy by intercepted component execution.

3. Aggregation and Composite Scoring

CLEAR frameworks aggregate fine-grained measurements into summary statistics, employing:

Attribute or Condition Averaging: In radiology, attribute-level (presence, severity, etc.) and condition-level (CheXpert classes) means support multidimensional scoring, targeting not only detection but clinical actionability (Jiang et al., 22 May 2025).
Harmonic Means: In machine unlearning, the harmonic mean of ROUGE-L, confidence, and truth ratio per split ensures neither surface-form collapse nor hidden leakage dominates total performance assessment (Dontsov et al., 2024).
Aggregated Path/Trajectory Statistics: In VLN, per-episode navigation metrics are averaged over unseen splits and languages, supporting cross-language and environmental generalization assessment (Li et al., 2022).
Energy Model Fitting: Transformer energy CLEAR fits a linear model (E = E₀ + k·FLOPs) to extract marginal energy cost constants per component; %Capture enables validation of measurement completeness (Jain et al., 3 Oct 2025).

4. Empirical Outcomes and Comparative Analysis

Empirical analysis across CLE[A]R instantiations documents substantial benefit and discriminative power relative to baseline or legacy metrics:

RNA-seq: CLEAR filtering improves overlap of DEGs across input amounts (e.g., 135/189 DEGs shared 100 pg/1 000 pg with CLEAR vs 24/898 under baseline), strengthens PCA separation, and has utility in QC of single-cell libraries, especially in imputation-augmented workflows (Walker et al., 2018).
Sentence Embeddings: CLEAR pretraining yields +2–8 accuracy points and +5–8 correlation points versus strong baselines, uplifting downstream NLU benchmarks (Wu et al., 2020).
VLN: CLEAR achieves +92% SR improvement (20.9 → 40.3) on RxR monolingual and shrinks unseen–seen performance gaps, with both cross-lingual and environment-agnostic visual representations contributing orthogonally (Li et al., 2022).
Radiology: CLEAR achieves Positive-F1=0.934 (GPT-4o), outperforming CheXpert and CheXbert, and exhibits high correlation between LLM-based similarity metrics and expert scores (Pearson 0.994). Micro-averaged accuracies for clinical attributes (e.g., recommendation: 0.887, location ROUGE-L: 0.686) underlie its strong alignment with radiologist judgment and actionable feedback (Jiang et al., 22 May 2025).
Unlearning: Balanced approaches (IDK, SCRUB, DPO) yield Retain ≈0.26–0.28, Forget ≈0.24–0.42 (vs. catastrophic Retain=0 for aggressive methods), with statistical indistinguishability (p_KS ≈0.6–0.9) relative to retrained gold (Dontsov et al., 2024).
Green AI: Attention block E/FLOP is 1.5–3× MLP across 15 transformer models; %Capture ≥90% and variance <9.5%. Significant fixed kernel launch overheads suggest disproportionate energy costs for memory- and softmax-bound submodules (Jain et al., 3 Oct 2025).

5. Implementation Protocols and Calibration

Each CLEAR metric framework is tightly coupled to explicit, often multi-stage pipelines:

RNA-seq: Adapter trimming, contaminant filtering, genome alignment, per-base coverage calculation, transcript-level normalization, moving-window histogram aggregation, double-beta mixture fitting, and strict sample-wise inclusion criteria (Walker et al., 2018).
Radiology: LLM-based unified extraction of attribute labels from candidate/ground-truth reports, tabulation across conditions, hybrid accuracy/similarity evaluation, and empirical validation with expert-annotated gold standards (Jiang et al., 22 May 2025).
Energy Measurement: Activation capture, loop-amplified modular execution, ms-resolved power readings, N×T repetition for variance reduction, FLOPs estimation via profiler, and consistency/completeness calibration (Jain et al., 3 Oct 2025).
VLN and Representation Learning: ML model training/evaluation on standardized splits, task-specific readout heads, aggregation of per-task scores, and decomposition by split/language (Wu et al., 2020, Li et al., 2022).

Across domains, thresholds and hyperparameters (e.g., expression bin sizes, bimodality cutoffs, window radii, attribute similarity metrics) are selected empirically or via expert consensus. Validation against strong baselines is universal.

6. Scientific and Practical Significance

CLEAR metrics systems are designed to surpass the expressive limitations of one-dimensional figures-of-merit:

Holistic Performance Surfaces: By integrating multi-axis tradeoffs—accuracy vs. interpretability, energy vs. latency, clinical relevance vs. string match—CLEAR reveals performance cliffs, failure modes, and points for targeted system and model improvement.
Actionable Diagnostics: In radiology and RNA-seq, fine-grained tabulation enables immediate identification of mode-specific errors, supports targeted retraining, QC flagging, and workflow optimization.
Comparability and Benchmarking: Standardized CLEAR metrics support cross-method, cross-system, and cross-institution benchmarking. In "Moore’s Law in CLEAR Light", the Capability-to-Latency-Energy-Amount-Resistance surface accurately postdicts technology evolution and informs projected performance/cost limits for semiconductor and photonic devices (Sun et al., 2016).
Engineering Guidance: In AI hardware, CLEAR’s component-level energy assessments inform architectural and algorithmic optimizations, supporting modular design for energy proportionality.

7. Generalization and Future Prospects

CLEAR has demonstrated adaptability and extensibility:

Attribute and Condition Expansion: Attribute-level metrics in radiology are natively extensible to additional clinical axes or domains, conditional on high-quality expert annotations (Jiang et al., 22 May 2025).
Scalability: RNA-seq CLEAR is compatible with high-throughput protocols under appropriate imputation, and transformer energy CLEAR is validated in models scaling up to 20B parameters and input contexts up to 128K tokens (Jain et al., 3 Oct 2025).
Cross-domain Influence: The CLEAR paradigm of domain-tailored, multi-axis metrics has been cited as a model for methodological design in diverse settings, from galactic spectral surveys (Simons et al., 2023) to VLN transfer learning (Li et al., 2022).

A plausible implication is that the CLEAR design ethos—multiplexing of orthogonally-informative metrics, empirical calibration, and transparency of failure—constitutes a generalizable blueprint for rigorous system evaluation in evolving computational and scientific domains.

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