Autonomy-of-Experts in AI Systems

• Autonomy-of-Experts is a paradigm that enables expert modules and human specialists to self-assess and engage in task processing, enhancing computational efficiency and epistemic oversight.
• It employs mathematical self-evaluation using L2 norms and top-K selection to determine which expert units execute full computations, reducing FLOPs compared to traditional MoE models.
• In human-AI contexts, AoE preserves skilled judgment and authentic value formation through adaptive socio-technical design patterns that counter deskilling and unnoticed bias.

Autonomy-of-Experts (AoE) is a paradigm in both machine learning architecture and AI-supported decision frameworks that empowers individual expert modules or human domain specialists to actively determine their own participation in task processing. This approach aims to enhance computational efficiency, interpretability, and epistemic agency. In model architectures, AoE refers to mechanisms allowing expert neural subnetworks to self-select based on internal activation signals, eliminating centralized routers. In human-AI contexts, AoE denotes the preservation of skilled judgment and authentic values amidst increasing reliance on Artificial Epistemic Authorities. Across implementations, AoE integrates mathematical self-evaluation, domain-specific reliability criteria, and socio-technical design to minimize deskilling and maintain critical oversight.

1. Mathematical Foundations and Architectural Principles

In model-based AoE, as formulated in "Autonomy-of-Experts Models" (Lv et al., 22 Jan 2025) and "Video-VoT-R1" (Li et al., 20 Mar 2025), each expert module $E_i$ receives an input token vector $\mathbf{x}$ and precomputes its own activation measure. Specifically, the core self-evaluation employs the $L^2$ norm of a low-dimensional activation constructed via low-rank weight factorizations:

$$\mathbf{a}^i = \mathrm{SiLU}(\mathbf{x}\mathbf{W}^i_{\mathrm{down}}\mathbf{W}^i_{\mathrm{up}}) \odot (\mathbf{x}\mathbf{W}^i_{p}), \qquad \|\mathbf{a}^i\|_2 \text{ as confidence score}$$

Experts are ranked by $\|\mathbf{a}^i\|_2$ and only the top-$K$ proceed with full forward computation; others abort, saving FLOPs. This expert self-selection operates without a router, contrasting with classic Mixture-of-Experts (MoE) models which decouple routing from expert competence (Lv et al., 22 Jan 2025).

The AoE selection pipeline is mathematically characterized by:

1. Concatenate all down-projection weights as $$\widehat{\mathbf{W}}_{\mathrm{down}}\in\mathbb{R}^{d_{\mathrm{model}}\times n\,d_{\mathrm{low}}}$$.
2. Compute caches for all experts: $$\mathbf{C} = \mathbf{x}\,\widehat{\mathbf{W}}_{\mathrm{down}}$$.
3. Calculate per-expert norms; select top-$K$ by sorting.
4. Softmax-normalize confidence scores for aggregation weighting.
5. Execute full expert pass for chosen modules.

Compared to traditional sparse MoE, this paradigm yields better specialization, balanced expert loads, and more decisive activations while maintaining comparable throughput (Lv et al., 22 Jan 2025, Li et al., 20 Mar 2025).

2. Training Procedures and Empirical Performance

AoE models are trained end-to-end using standard objectives (negative log-likelihood, contrastive/distillation loss) with optional load-balancing auxiliaries. For example, in Video-VoT-R1 (Li et al., 20 Mar 2025), the loss stack comprises cross-entropy and multiple contrastive terms. A hard top-$K$ sparsity constraint governs expert invocation, and auxiliary load metrics promote balanced expert utilization.

Empirical studies establish AoE’s computational and accuracy advantages. With $n=16$ experts, $K=4$, and $d_{\mathrm{low}}/d_{\mathrm{ffn}}\approx 0.125$, AoE achieves a $\sim$3–4$\times$ reduction in expert-block FLOPs and up to 97% of routers' throughput. Benchmark results for 4B-parameter models on eight NLP tasks improve from 48.06% (MoE) to 49.80% (AoE), and in multimodal video inference gain 3–6 points on NExT-QA and Causal-VidQA tasks (Lv et al., 22 Jan 2025, Li et al., 20 Mar 2025).

Model Type	Benchmark (AVG) (%)	Active Params (B)	Throughput (K tok/s)
Traditional MoE	48.06	1.18	51.42
AoE	49.80	1.18	49.42

This self-evaluating-then-partner-comparing approach robustly scales from sub-billion to multi-billion parameter models with minimal overhead.

3. Human AoE: Epistemic Agency, Reliability, and Value Formation

In AI decision-support settings, AoE is formalized as the preservation of expert agency—both in epistemic judgment and authentic value formation—when interacting with Artificial Epistemic Authorities (AEAs) (Lange, 23 Oct 2025, Buijsman et al., 30 Jun 2025). The total-evidence view posits that AEA outputs serve as contributory, not preemptive, reasons:

$$P(H \mid E_{\text{human}}, E_{\text{AI}}) = \frac{P(E_{\text{human}}, E_{\text{AI}} \mid H) P(H)}{P(E_{\text{human}}, E_{\text{AI}})}$$

Key criteria for justified deference include reliability thresholds ($R_{\text{AI}} > R_{\text{human}}$ with an epistemic buffer), absence of defeaters (domain mismatch, systematic bias, conflicting authority, novel evidence), and maintenance of explanatory traceability.

AoE’s human dimension further encompasses two principal axes (Buijsman et al., 30 Jun 2025):

• Skilled Competence: Ability to deploy domain-specific knowledge and metacognitive calibration, formalized as $C(t) = \alpha \cdot K(t) + \beta \cdot M(t)$.
• Authentic Value-Formation: Maintenance and conscious endorsement of evolving values, $AVF(t) = \gamma \cdot A(t) + \delta \cdot E(t)$, protecting experts from unexamined value drift under opaque AI influence.

4. Failure Modes and Critiques

The data identifies two major AoE failure modes in decision-support (Buijsman et al., 30 Jun 2025):

• Missing Failure Indicators: Opaque systems suppress metacognitive cues necessary for reflection, resulting in silent skill degradation.
• Unconscious Value Shifts: AI-induced biases persist and propagate, sometimes unnoticed by users, undermining authentic value formation over time.

A plausible implication is that robust AoE mechanisms are necessary to counter deskilling and epistemic entrenchment, especially in high-stakes domains. Without explicit failure signals and reflective practice scaffolds, experts may lose the ability both to identify critical evidence and articulate authentic domain values.

5. Socio-Technical Design Patterns and Practical Guidelines

For human-AI systems, AoE is operationalized through five socio-technical design patterns (Buijsman et al., 30 Jun 2025):

1. Role Specification: Assign complementary, not overlapping, tasks to human and AI agents.
2. Defeater Mechanisms: Implement warnings for outlier or context-missing cases, prompting expert reflection.
3. Training Regimens: Schedule periodic “AI-free” sessions to maintain and recalibrate expert skills.
4. Reflective Practice Support: Introduce positive frictions via prompts, assumption checklists, or journaling.
5. Adaptive Value-Sensitive Interfaces: Elicit user values and dynamically align AI content or recommendations.

System designers are advised to integrate failure-signal layers, skill-maintenance protocols, and reflection triggers into workflows, as well as to monitor domain-specific autonomy metrics (competence calibration and value survey shifts).

6. Applied Domains and Implementation Examples

AoE frameworks are deployed in diverse contexts:

• Medical: Dual-screening workflows maintain radiologist involvement alongside AI (Buijsman et al., 30 Jun 2025).
• Finance: Outlier-warning and independent analysis guard metric reliability (Lange, 23 Oct 2025).
• Legal: Structured veto–review processes support professional judgment (Lange, 23 Oct 2025).
• Education: Quarterly AI-off peer review preserves core evaluative skills (Buijsman et al., 30 Jun 2025).
• Computer Vision: Video-VoT-R1’s AoE modules reduce computational cost while increasing accuracy in multimodal inference (Li et al., 20 Mar 2025).
• Language Modeling: 700M–4B parameter LLMs demonstrate AoE-driven efficiency and accuracy over classic MoE architectures (Lv et al., 22 Jan 2025).

Workflows often include explicit checkpoints for expert override, periodic calibration, and adaptive value-capture protocols, safeguarding both skills and values.

7. Controversies, Limitations, and Future Directions

Some critiques highlight the possibility that AoE self-selection may overweight internal activation magnitude, risking the exclusion of experts capable of domain-general inference. This suggests further empirical work is needed on activation-norm calibration and expert diversity. In human-AI contexts, balance between efficiency and expert engagement remains challenging; total-evidence deference demands explainable models and periodic retraining, which may increase implementation costs.

Ongoing research targets refined autonomy metrics, improved positive friction designs, and adaptive alignment protocols—aiming for systems that optimize both computational efficiency and epistemic health.

---

Autonomy-of-Experts thus encompasses a suite of mechanisms—mathematical, procedural, and organizational—designed to preserve the active role and judgment of individual experts, either as self-evaluating neural modules or as skilled human agents, in the face of ever-more capable and opaque AI systems.