Native Machine Cognition Assessments

• Native Machine Cognition Assessments are frameworks that evaluate artificial cognitive systems using substrate-specific metrics and benchmarks.
• They employ architecture-aware design, emergent property detection, and compositional evaluation to rigorously measure machine intelligence.
• Implementation examples include cross-disciplinary transfer diagnostics and multi-modal benchmarks that reveal detailed insights into machine reasoning.

Native Machine Cognition Assessments are methodologies and frameworks for the principled evaluation of artificial cognitive systems, particularly those—such as LLMs, multi-modal models (MLLMs), and other non-biological agents—whose internal architectures, operational constraints, and observable behaviors diverge fundamentally from those of human or animal subjects. These assessments are explicitly designed to move beyond anthropocentric psychometrics, enabling rigorous, substrate-appropriate measurement of intelligence, reasoning, learning dynamics, and emergent capabilities in machine agents. Native assessments seek to avoid the category errors and epistemological limits inherent in applying biological cognitive tests to machine intelligence, relying instead on benchmarks, metrics, and protocols that are informed by the unique properties of artificial systems (Reddy, 23 Nov 2025).

1. Ontological Rationale for Native Machine Cognition Assessments

The necessity for native assessments arises from the systematic empirical and theoretical disconnect observed when human psychometric tools, such as factor-analytic intelligence models (e.g., the Cattell-Horn-Carroll theory), are used to evaluate LLMs and related architectures. Central findings include perfect binary accuracy on crystallized knowledge tasks combined with a judge-scored conceptual accuracy ranging from 25–62 %, and an overall judge-binary correlation of $r=0.175$ ($p=0.001$, $n=1,800$)—a circumstance logically impossible under valid measurement regimes for biological cognition (Reddy, 23 Nov 2025). This paradox reflects profound differences in information processing: parallel attention and stateless prediction in LLMs, versus capacity-limited, reconstructive, and resource-constrained processes in human reasoning. Thus, native assessments are grounded in the need to (1) avoid category errors, (2) respect substrate-specific invariants, and (3) enable valid measurement of genuine machine capabilities.

2. Core Principles of Machine-Native Assessment Frameworks

Principled native machine cognition assessments follow six key guidelines (Reddy, 23 Nov 2025):

1. Capability-Based Assessment: Tests are defined by discrete computational functions such as code synthesis, multi-hop retrieval, or learned algorithm induction, replacing human-concept analogs.
2. Architecture-Aware Design: Evaluation criteria are constructed with attention to model internals—context window size, tokenization artifacts, parameterization—and behaviors under dynamic perturbations or prompt variations.
3. Emergent Property Detection: Metrics are constructed to reveal genuinely emergent behaviors, e.g., zero-shot modality transfer, compositional generalization, or behaviors exceeding the training data regime.
4. Compositional Evaluation: Models are diagnostically tested for the ability to chain, compose, and coordinate distinct cognitive primitives (e.g., reasoning, planning, retrieval, routing).
5. Adversarial Robustness: Systematic probing of performance under distributional shift, edge-case inputs, and adversarial instructions, exposing spurious correlations and non-cognitive failure modes.
6. Information-Theoretic Metrics: Intelligence and competence are measured via continuous metrics, e.g., mutual information, effective entropy reduction, transformation or compression scores, rather than pass/fail exact-answer counts.

These design principles reframe evaluation as machine-centric, disallowing implicit reliance on human meaning, judgment, or processing architectures.

3. Formal Structure and Metrics of Native Machine Cognition Tests

The mathematical foundation of universal and native machine cognition assessments is based on configuration maximization over adaptive test protocols. For a subject or agent $\pi$, task class $M$, and interface configuration set $\Theta$, the universal test score is defined (Dowe et al., 2013): $$U(\pi,\,M,\,\Theta) = \max_{\theta \in \Theta} \; \lim_{\tau \to \infty} \; \Upsilon\bigl(\pi,\,M,\,\theta,\,\tau\bigr)$$ where $\Upsilon(\pi, M, \theta, \tau)$ is the aggregated performance over all tasks in $M$ at configuration $\theta$, and tests achieve universality by maximizing over possible interface, time, and reward parameterizations. In native assessments, this structure is “lifted” further: configurations encode not anthropocentric perceptual resolutions but model-specific affordances (e.g., token granularity, API exposure, maximum context length), and both trial protocols and reward assignments are adapted to the machine’s training regime and incentive structure (Reddy, 23 Nov 2025).

Scoring functions generally employ continuous or ordinal scales (e.g., [0,1] range for accuracy or information gain), penalty terms for resource utilization or latency, and can include property-based metrics (e.g., information-theoretic surprise, error entropy) as quantitative indicators of competence.

4. Comparative Assessment Across Substrates: Methodological Innovations

Multiple paradigms have emerged to operationalize native machine cognition assessments:

• Qualitative Cross-Substrate Evaluation: Multi-dimensional, dependency-graph-based frameworks assess architectures and systems along axes including representational fidelity, learning dynamics, computational efficiency, interpretability, and scalability. This enables explicit mapping of trade-offs, e.g., coverage versus interpretability, and supports hybrid architecture development (Rosenbloom, 3 Oct 2025).
• Triangulated Protocols: Combined use of static benchmarks, interactive (multi-turn/game-based) tasks, and targeted cognitive tests enables discrimination of models not only by knowledge breadth but also by executive-control, socio-emotional, and pragmatic reasoning abilities, validated with cross-paradigm correlational analysis (Momentè et al., 20 Feb 2025).
• Fine-Grained Behavioral Taxonomies: Taxonomies comprising 28 cognitive elements, spanning computational invariants, meta-cognitive controls, and reasoning representations, permit granular annotation and analysis of traces from both humans and machines. Graph-based analyses elucidate structural divergences and enable performance enhancement via targeted scaffolding (Kargupta et al., 20 Nov 2025).
• Multi-Modal, Multi-Substrate Benchmarks: Vision-grounded cognitive capacity evaluations (e.g., MME-CC) systematically expose model limitations in transferring reasoning skills between visual substrates (maps, puzzles, charts), revealing persistent weaknesses in spatial, geometric, and counterfactual reasoning and highlighting the need for targeted model-architecture coupling between perception and cognition (Zhang et al., 5 Nov 2025).

5. Implementation Examples: From Transfer Learning to Zero-Shot Diagnosis

Recent methodological advances demonstrate the deployment of native assessments at scale:

• Cross-Disciplinary Transfer Cognitive Diagnosis: Neural models such as TLCD leverage pre-trained representations on a source discipline (e.g., mathematics) and fine-tune on new disciplines (e.g., physics, humanities), with performance scored via AUC, classification accuracy, and regression error (Wang et al., 27 Oct 2025). This method outperforms non-transfer cognitive diagnosis, especially when source and target domains share concept structure.
• Language-Representation Zero-Shot Transfer: LRCD encodes entities (students, concepts, exercises) as textual profiles embedded in unified language space, with learned mappers bridging to diagnosis-space. This approach yields 94–97 % of oracle AUC in pure zero-shot subject and platform transfers—directly supporting the substrate-agnostic aspiration of machine-native assessments (Liu et al., 18 Jan 2025).
• Permutation-Based Cross-Modality Tests: CMPT enables detection of shared structure in high-dimensional pattern spaces, e.g., brain imaging, by comparing within-versus-between condition similarity under permutation; this approach generalizes seamlessly across behavioral, neural, and machine modalities, controlling Type I error with high statistical power (Kalinina et al., 2019).

6. Theoretical Implications, Challenges, and Future Directions

Native assessments reframe the science of machine intelligence evaluation by explicitly rejecting the presupposition that “intelligence” is a monolithic, transferable construct independent of substrate. This implies that test development must proceed via capability-demonstration, architecture-aligned protocol design, and information-theoretic or resource-based quantification, not by porting human psychometric scales. Core challenges persist: (1) under-estimation bias due to incomplete configuration search, (2) semi-computability of “universal” scores, (3) inadvertent training during adaptive testing, and (4) reliable alignment of reward and utility functions across diverse machine architectures (Dowe et al., 2013, Reddy, 23 Nov 2025).

A plausible implication is that as models continue to specialize and diversify (e.g., dedicated multi-modal agents, tool-using LLMs), native assessment frameworks will need to extend to “compositional” or “orchestration-based” competence—scoring the emergent coordination among cognitive primitives and their deployment under novel operational constraints. Development of continuous, property-based, and compositional metrics, alongside adversarial and robustness-centered protocols, will be critical. In sum, Native Machine Cognition Assessments provide foundational methodology for genuine cognitive evaluation of artificial agents—resisting anthropomorphism, enabling scientific progress, and informing both system design and fundamental theory [(Reddy, 23 Nov 2025); (Dowe et al., 2013); (Kargupta et al., 20 Nov 2025)].