Soppia: Ordered Proportional Intelligent Assessment

• Soppia is a modular framework that integrates weighted, multidimensional criteria to deliver transparent and proportional evaluations in diverse settings.
• Its dual-axis assessment paradigm combines raw computational scores with context-sensitive judgments to support legal reasoning, AI benchmarking, and cognitive assessments.
• The system employs structured prompt engineering and probabilistic models for real-time, explainable, and adaptable performance analysis.

Soppia (System for Ordered Proportional and Pondered Intelligent Assessment) is a modular, computational framework designed for ordered, transparent, and proportional evaluation across domains such as legal reasoning, intelligent agent benchmarking, and cognitive skill assessment. Its architecture enables structured, weighted analysis of multidimensional criteria, supporting explainability, replicability, and adaptability in complex decision-making environments.

1. Theoretical Foundations and Motivation

Soppia arises in response to the challenge of evaluating complex entities—ranging from intelligent systems to legal damage claims—where multiple, heterogeneously weighted criteria must be integrated into a coherent, auditable outcome. The framework synthesizes principles from decision theory (mitigating “noise” in human judgment), Explainable AI (XAI), and structured prompt engineering to address subjectivity and increase consistency, particularly in settings where qualitative and quantitative factors interact nontrivially (Araujo, 24 Oct 2025). In the context of intelligent system evaluation, Soppia’s logic supports the development of “anytime universal intelligence tests,” yielding metrics robust to interruptions and applicable across agent types (Insa-Cabrera et al., 2011).

A dual-axis assessment paradigm further extends the theoretical foundation, distinguishing between raw computational power ("brute force") and adequately anticipatory, context-sensitive application of that power (Kubryak et al., 2022). This distinguishes Soppia from mere score aggregation, positioning it as a framework for holistic, proportional evaluation.

2. System Architecture and Methodological Design

Soppia is structured as a modular system, comprising the following elements (with examples from both intelligent system testing and legal reasoning):

• Criteria Definition Layer: All assessment begins with the identification of relevant criteria, each explicitly defined with contextually grounded legal, cognitive, or operational relevance. For example, in legal contexts, Article 223-G of the Brazilian CLT provides a canonical twelve-criteria scheme for non-pecuniary damages (Araujo, 24 Oct 2025); in cognitive assessment, discrete skills such as pattern recognition and algorithm design are similarly instantiated as nodes in a competence network (Adorni, 27 Mar 2025).
• Scoring and Weighting Layer: Each criterion is evaluated on a standardized ordinal scale (typically 1–5), and paired with a domain-informed weight, yielding a pondered (weighted) contribution to the aggregate score. Dual-logic handling enables both aggravating (direct logic) and mitigating (inverse logic) factors to be incorporated:

$S_{\text{total}} = \sum_{i=1}^n w_i \cdot s_i$

where $s_i$ is the score for criterion $i$ (using the appropriate logic), and $w_i$ is its assigned weight.

• Interaction and Evaluation Layer: In agent assessment, Soppia operationalizes environments as directed graphs of “cells” and “actions”, facilitating repeated cycles of observation, action, and reward assignment under both manually specified and randomly generated topologies (Insa-Cabrera et al., 2011). In legal reasoning, a structured prompt guides LLMs through each criterion, ensuring both input transparency and decision traceability (Araujo, 24 Oct 2025).
• Granular Adjustment and Classification: Post-aggregation, Soppia classifies the computed score into ordinal bands (e.g., Mild, Medium, Severe, Very Severe), and applies secondary refinements based on the score’s position within the band, thus ensuring proportionality and granularity in recommendations.

This architecture is inherently extensible, supporting adaptation to alternate sets of criteria, weighting schemes, and both individual and composite evaluations (e.g., via “social evaluation” in multi-agent contexts).

3. Proportional and Weighted Assessment Methodologies

Proportionality in Soppia is achieved through the combination of scaled scoring, dual-logics, and explicit weighting. For domains such as non-pecuniary legal damages, each criterion’s significance is reflected in its designated weight, with the final score used to anchor recommendations within context-dependent compensation ranges (Araujo, 24 Oct 2025). Similarly, in intelligent assessment, normalized reward averages enable the system to differentiate between random, observer, or learning agents, with performance variance directly interpretable as a function of agent “intelligence” (Insa-Cabrera et al., 2011).

In educational contexts, Soppia’s approach aligns with developmental competence models that tailor task complexity and weighting to age, ability, and context, supporting adaptivity across learner cohorts (Adorni, 27 Mar 2025). A probabilistic engine, typically instantiated as a Bayesian Network with noisy-OR gates, enables real-time, skill-specific inference—eschewing the limitations of aggregate, single-score evaluations.

Domain	Assessment Scales	Weighting Mechanism
Legal Reasoning	1–5, dual-logic	Domain-expert weights
Intelligent Agents	Normalized rewards (−1 to 1)	Balanced env./ agent variance
Education	Fine-grained skill probabilities	Bayesian Network (noisy gates)

4. Explainability, Auditing, and Transparency

A central feature of Soppia is its commitment to explainability and auditability at every layer of its operation. Each datum—from the rationale behind criterion scores to the weighting basis and final classification—is recorded and exposed for review. Legal Soppia instances provide full narrative justifications for each criterion, with audit trails enabling procedural challenges or appeals (Araujo, 24 Oct 2025). In intelligent agent evaluation, all actions, observations, and reward assignments are logged, supporting cross-agent and session-based comparisons (Insa-Cabrera et al., 2011).

Structured prompt engineering underpins system LLMs, ensuring that each output can be traced decisively to specific input criteria and rules. This design mitigates "noise" and supports the replicability of complex, discretionary assessments.

5. Adaptability and Domain Transferability

Soppia’s separation of criteria definition, scoring logic, and aggregate computation affords robust domain transfer. Legal professionals can instantiate the framework in alternate normative domains—such as consumer or environmental law—by redefining the criteria and adapting the weights, without altering the overall logic or transparency (Araujo, 24 Oct 2025).

In cognitive and educational applications, Soppia’s ordered, proportional, and pondered assessment scaffolds facilitate integration with competence models, dynamic task generation, and developmental calibration, supporting both unplugged (manual) and virtual (digital) assessment variants (Adorni, 27 Mar 2025). In artificial intelligence benchmarking, Soppia’s controlled, generative environment methodology is compatible with a wide range of agent types and problem classes (Insa-Cabrera et al., 2011).

6. Limitations and Future Directions

While Soppia provides a replicable, auditable methodology, several limitations are acknowledged. Current environment and object generation may rely on simple uniform distributions rather than truly universal (e.g., Kolmogorov complexity-based) distributions, limiting assessment depth (Insa-Cabrera et al., 2011). In legal applications, weighting and criterion definition remain subject to domain-specific judgment and legislative evolution, potentially requiring periodic expert recalibration (Araujo, 24 Oct 2025).

A plausible implication is the eventual extension to adaptively generated environments and tasks, observation limitations reflecting perceptual constraints, and more sophisticated models of social and contextual influence—particularly in dynamic or multi-agent settings. Probabilistic modules and competence-weighted assessments may further refine responsiveness to real-world, temporal, and context-driven complexities (Kubryak et al., 2022, Adorni, 27 Mar 2025). Open-source prompt and scoring infrastructure facilitate ongoing community validation and refinement.

7. Cross-Domain Significance and Impact

Soppia establishes a meta-framework for robust, proportional assessment in multi-criteria domains where both binary and nuanced distinctions are required. Through explicit, modular design and adherence to explainable, auditable methodology, Soppia is positioned as a reference architecture for advancing reliable, fair, and context-sensitive decision support systems across artificial intelligence, legal, and educational settings [(Araujo, 24 Oct 2025); (Insa-Cabrera et al., 2011); (Adorni, 27 Mar 2025)]. Integration of dual-axis intelligence models and competence-based scaffolds further positions Soppia as a bridge between computational reasoning and normative, context-embedded evaluation (Kubryak et al., 2022).