ML Compass: Navigating ML Trade-Offs

• ML Compass is a framework that visualizes and formalizes the trade-offs among capability, cost, and compliance in machine learning deployments.
• It employs graphical representations and constrained optimization to enhance model selection, supporting transparent benchmarking beyond single-metric evaluations.
• The approach is applied across diverse domains such as continual learning, autonomous robotics, and decentralized systems to streamline real-world deployment decisions.

ML Compass refers to a family of visual, algorithmic, and optimization-driven frameworks for evaluation, deployment, and model selection within the broader scope of ML and autonomous systems. These approaches offer structured representations—most notably, graphical “compasses”—for navigating the multidimensional capability, resource, and compliance landscape inherent to modern ML, continual learning (CL), distributed inference, and specialized domain applications. ML Compass formalizes and visualizes trade-offs among performance, cost, and operational constraints, supporting transparent benchmarking and deployment-aware decision making across diverse contexts such as AI model selection, continual learning evaluation, e-commerce, autonomous robotics, and distributed workflow scheduling.

1. Foundational Principles and Motivation

ML Compass frameworks emerged in response to chronic gaps between benchmark-centric capability rankings and the practical requirements of real-world deployment. In continually evolving domains such as CL and vertical application areas (e.g., healthcare, multilingual e-commerce), prevailing single-metric leaderboards fail to capture the interplay among user utility, cost (compute/storage/latency), and context-specific compliance minima (Digalakis et al., 29 Dec 2025). This capability–deployment gap motivates a systems-level approach: model selection is re-cast as constrained optimization over a capability–cost frontier, subject to downstream utility, operational budgets, and regulatory stipulations.

In continual learning, for example, small differences in experimental design—data ordering, memory buffers, access to task labels—can fundamentally alter method behavior, rendering raw accuracy or forgetting scores insufficient for valid cross-method comparison (&&&1&&&).

2. Compass Structures: Visualization and Taxonomy

The core ML Compass idea is graphical, extending the CLEVA-Compass paradigm of (Mundt et al., 2021). CLEVA-Compass and its generalizations are composed of two concentric visualization layers:

• Inner star diagram: Each axis represents a critical facet of system set-up or evaluation, e.g., task-agnosticism, federated learning, uncertainty quantification, interpretability, adversarial robustness, fairness/bias, causality. Rings distinguish supervised and unsupervised approaches per axis.
• Outer radial bar chart: Bars encode the actual empirical measures reported (e.g., optimization steps, computation time, forgetting, forward/backward transfer, energy use, ROC-AUC, calibration error).

Each facet or metric is binary-coded according to actual use or reporting. This compact format immediately exposes which assumptions, capabilities, or evaluation protocols a given method addresses, and highlights missing elements required for meaningful comparison or deployment recommendation.

Compass Axis Examples

Inner Axis	Outer Metric	Description
Task Agnostic	Forgetting	Test whether method can operate without task oracle
Open-World Detection	Openness	Unknown/out-of-distribution sample handling
Federated Learning	Communication	Decentralized learning with inter-node rounds
Generative Modeling	Generated Data	Synthetic/replay sample budget
Uncertainty	Calibration Error	Quantified predictive uncertainty

The structure generalizes fluidly: an adversarial ML Compass may use axes for attack types (white/black-box) and outer bars for attack-specific robustness.

3. Theoretical Model Selection: Optimization Over Capability–Cost Frontiers

ML Compass formalizes model selection as constrained optimization. Let $x \in \mathbb{R}_+^I$ encode internal capability measures (e.g., fluency, factuality, safety), with $c$ the normalized deployment cost (compute, memory, API pricing). The objective is to maximize expected utility minus weighted cost, given explicit compliance and resource constraints:

$\max_{x,c\in[0,1]} U(x;z) - \lambda B_0(c;w)$

subject to

$$R_i(x;r) \le 0 \quad (i=1,\dots,I), \quad B_j(c;w)\le 0 \quad (j=1,\dots,J), \quad F_X(x)\le F_C(c)$$

where $U(x;z)$ is user/task-contextual utility, $\lambda$ the cost sensitivity parameter, $F_X$ and $F_C$ a technological CES frontier and resource envelope (Digalakis et al., 29 Dec 2025). The KKT stationary points induce a three-regime solution:

• Compliance regime: $x_i$ pinned to regulatory minima $R_i$.
• Saturation regime: $x_i=1$ (maximal feasible value).
• Interior regime: $x_i^* = (\frac{\beta_i}{\mu_0 a_i b})^{1/(b-1)}$ with $\mu_0$ as Lagrangian multiplier.

Budget, regulatory, and technological comparative statics precisely characterize how model selection recommendations shift under budget changes, regulatory tightening, and technical progress.

4. Implementation Pipeline: From Descriptors to Deployment-Aware Leaderboards

Operationalizing ML Compass involves:

1. Extraction of Internal Measures: Factor analysis and rotation (e.g., Promax) on raw model descriptors yield low-dim capability profiles, min-max scaled.
2. Frontier Estimation: Pareto peeling and CES frontier fitting on capability–cost pairs produce empirical non-dominated tiers.
3. Utility Estimation: Learning context-dependent utility functions from real or synthetic outcome data, using logistic or regression models, optionally with non-linear methods (LightGBM).
4. Optimization and Recommendation Generation: Solving the constrained maximization to recommend models, producing deployment-aware leaderboards aligned to user value, cost constraints, and compliance minima.

Deployment-aware leaderboards reorder models relative to single-metric capability rankings, revealing trade-offs and sometimes overturning established benchmarks (Digalakis et al., 29 Dec 2025).

5. Compass in Continual Learning: CLEVA-Compass Methodology

CLEVA-Compass addresses CL’s unique evaluation ambiguity by:

• Visualizing which experimental axes (task-agnostic, online, federated, etc.) are covered by each method (supervised/unsupervised marking per axis).
• Radially charting empirical protocol measures (e.g., forgetting, forward/backward transfer, communication overhead) actually reported in each paper.
• Formalizing key CL metrics:
  • Average accuracy: $a_T = \frac{1}{T} \sum_{t=1}^T a_{T,t}$
  • Forgetting: $$F_t = \frac{1}{t-1} \sum_{j=1}^{t-1} [\max_{i < t} a_{i,j} - a_{t,j}]$$
  • Forward transfer (FWT): $$FWT_t = \frac{1}{t-1} \sum_{j=2}^t [a_{j-1,j} - \bar{b}_j]$$
  • Backward transfer (BWT): $$BWT_t = \frac{1}{t-1} \sum_{j=1}^{t-1} [a_{t,j} - a_{j,j}]$$
  • Openness: $$O = 1 - \sqrt{\frac{2 N_{train}}{N_{test} + N_{target}}}$$

Case studies (OSAKA, FedWeIT, A-GEM, VCL, OCDVAE) illustrate how CLEVA-Compass reveals practical set-up priorities and missing empirical measures for each method (Mundt et al., 2021).

6. Compass Paradigms in Specialized ML Systems

Beyond benchmarking or abstract selection, ML Compass is instantiated in operational frameworks and model architectures:

• Decentralized Workflow Scheduler (Compass): A fully decentralized protocol for latency-sensitive distributed ML, combining global state monitoring, per-node HEFT-inspired scheduling, and GPU model caching, with empirical demonstration that Compass halves server requirements at a given slowdown factor compared to alternative schedulers (Yang et al., 2024).
• Domain-Specific LLM (Compass-v3): MoE Transformer (245B parameters, 71B active/token), curated multilingual e-commerce corpus (12T tokens), hardware-conscious parallelism (NVLink EP), and token-level optimal-transport DPO alignment yield state-of-the-art results and massive deployment (70% of LLM traffic) in industrial e-commerce (Maria, 11 Sep 2025).
• Contrastive Multimodal Pretraining (COMPASS): Structured multimodal graph regularization, factorized latent spaces (motion pattern and current state), and cross-modal contrastive objectives enable transfer and generalization across vehicle/drone navigation and visual odometry (Ma et al., 2022).

7. Limitations, Scope Creep, and Open Challenges

Current ML Compass frameworks are inherently descriptive—they produce visual maps or constrained profiles, not scalar “scores” or absolute rankings. The value lies in transparent exposition and auditability rather than in opaque aggregation. As ML paradigms evolve (e.g., causal continual learning, new regulatory domains), compass axes and metrics must be updated—requiring community-driven iteration. Compass-based reporting must be factual, excluding speculative extrapolations (e.g., unsupported claims of federated capability). Compass complements, but does not replace, model cards, dataset sheets, or broader reproducibility checklists; holistic deployment requires multi-layered documentation and ethical scrutiny (Mundt et al., 2021).

8. Significance and Outlook

ML Compass unifies descriptive, optimization-driven, and domain-specialized frameworks, enabling multi-axis transparency for researchers and practitioners in model evaluation, capability–cost navigation, continual learning, distributed systems, and specialized vertical applications. Such frameworks clarify critical deployment trade-offs and support reproducible, context-aligned decision processes across the rapidly evolving landscape of machine learning.