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Emotional Preferences as Goal-Priority Regulation

Shiqi Liu, Yihua Tan, Hu Fu, Guanyu Qi

arXiv:2608.27072v1cs.LGcs.AI

TL;DR

The paper asks whether higher-level goals can autonomously regulate competing lower-level priorities rather than relying on externally specified preferences. It combines a pretrained preference-conditioned MORL controller with an outer reinforcement-learning preference generator, and finds contextual, persistent priority regulation while showing that representation error bounds optimality and disappears under perfect representation.

  • Problem

    Existing MORL methods generally treat objective preferences as externally supplied, fixed, or externally generated, leaving autonomous priority regulation from a high-level goal open.

  • Method

    A pretrained MORL controller provides preference-conditioned behaviors, while an outer network learns a state-to-preference mapping optimized for a high-level objective.

  • Results

    The learned preferences show contextual switching and temporal persistence, outperform tested fixed-preference and handcrafted strategies, and have zero optimality gap when the optimal policy is representable by inner policies.

  • Takeaways & Limitations

    Preference regulation can emerge through high-level optimization as an operational mechanism for selecting among competing goal-directed behaviors.

  • Takeaways & Limitations

    The emotional preference model remains constrained to behaviors represented by the pretrained controller, and direct survival optimization achieves stronger raw survival performance.

Abstract

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A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional preferences autonomously generated by higher-level goals, rather than being externally prespecified. Under changing external environments and evolving internal states, emotions play an important functional role in regulating the relative priorities of competing goals. Inspired by the goal-directed theory of emotion, this paper studies how such preference regulation can be computationally realized through reinforcement learning. We first propose a conception of emergent emotional preference: a high-level goal autonomously induces state-dependent preferences over competing lower-level objectives. This conception is built upon a framework consisting of a multi-objective reinforcement learning inner controller and an outer preference generator. The inner controller provides a repertoire of preference-conditioned goal-directed behaviors, while the outer preference generator learns a mapping from the current state to objective preferences through reinforcement learning on a high-level goal. We operationalize emotional preference as a state-dependent regulation of relative goal priorities that emerges through optimization. Furthermore, we characterize the policy space induced by preference regulation and derive an upper bound on the optimality gap in terms of the representation error of the inner behavioral repertoire. We show that the gap vanishes when the optimal policy can be represented by the available preference-conditioned policies. Experiments in self-constructed multi-objective exploration environments show that the learned preference function exhibits contextual priority switching, graded trade-offs, and temporal persistence, and outperforms the evaluated fixed-preference and handcrafted-preference strategies.

1. Introduction

The paper asks whether higher-level goals can autonomously regulate competing lower-level priorities, rather than relying on externally specified preferences. It proposes and analyzes an outer preference-learning mechanism over a pretrained MORL controller, then evaluates its emergent preference dynamics.

  • Existing MORL methods generally learn how to act under supplied preferences, leaving when objectives should change priority unresolved.
  • Experiments show contextual priority switching, graded trade-offs, temporal persistence, and better performance than tested fixed-preference and handcrafted strategies.
  • The framework learns state-dependent objective preferences through outer reinforcement learning on a high-level goal, instead of externally specifying objective weights.
  • Emotional preference is operationalized as learned, state-dependent regulation of competing goal priorities, without claiming to model subjective affect or complete human emotion.
  • A frozen preference-conditioned MORL controller supplies lower-level behaviors, while the outer generator regulates their relative priorities according to state and the high-level objective.
  • Theoretical analysis bounds the constrained policy's optimality gap by inner-repertoire representation error and shows zero gap when the optimal policy is representable.

2. Related Work

The paper positions preference regulation as a computational interpretation of goal-directed emotion that complements MORL's preference-conditioned action execution. Its architecture differs from intrinsic-reward and hierarchical approaches by learning state-dependent priority regulation over competing objectives.

  • Goal-Directed Theories of Emotion and Computational Preference Regulation: Goal-directed theory describes emotion functionally through discrepancy detection and selection of strategies capable of reducing goal-relevant discrepancies.
  • Goal-Directed Theories of Emotion and Computational Preference Regulation: The framework maps lower-level reward vectors and state-dependent preferences into scalarized utilities that determine which objective-conditioned strategy receives priority.
  • Other Computational Models of Emotion and Decision-Making: Unlike intrinsic-reward models, emotional preference changes the relative priority structure of multiple objectives rather than adding a separate reward signal.
  • Other Computational Models of Emotion and Decision-Making: MORL determines how to execute a given preference, whereas the outer process learns when and what to prefer.
  • Other Computational Models of Emotion and Decision-Making: The outer network's preference vector provides an interpretable emphasis over objectives while the inner policy executes actions under that preference.

3. Method

The method separates skill acquisition from preference regulation: an inner MORL controller supplies preference-conditioned strategies, while an outer reinforcement-learning process learns state-dependent priorities for achieving a high-level goal.

  • The framework learns when and how to change priorities among competing lower-level objectives instead of fixing one global preference.The outer process regulates priorities in a state-dependent manner while pursuing a high-level objective.
  • The high-level goal supplies the long-term criterion, while the preference vector determines what lower-level objective to prioritize now.The inner controller realizes the selected strategy through action selection.
  • The paper defines emotional preference operationally as learned state-dependent regulation of competing goal priorities, not as a complete model of human affect.The interpretation is functional and computational rather than phenomenological.
  • The outer preference generator learns eθ: S → ∆m−1, producing wt = eθ(st) to determine relative priorities from the current state.Emergence means the mapping is acquired through optimization rather than specified by a hand-crafted state-to-preference rule.
  • A pretrained inner MORL controller maps states and preferences to goal-directed actions, providing a repertoire of behaviors with different objective trade-offs.The same controller can execute different strategies under different preference values and is frozen before outer learning.
  • Outer learning regulates existing goal-directed strategies without directly rewarding low-level objectives, whose behaviors matter through their contribution to the high-level goal.This decomposition preserves an interpretable separation between how to act and which strategy to prioritize.

4. Experiments

Experiments in synthetic multi-objective exploration environments show that the learned preference generator produces distinguishable, context-sensitive behaviors with graded trade-offs and persistence, outperforming tested fixed and handcrafted strategies.

  • Preference-conditioned objective trade-offs: The inner controller exhibits a clear achievement-energy trade-off, confirming that different preferences expose substantially different goal-directed behaviors.The reward statistics were computed over 200,000 evaluation episodes, and the observed points approximately cover the achievable reward region convexly.
  • Preference-conditioned objective trade-offs: Preference-conditioned policies differ systematically in survival time and resource use: achievement-oriented preferences collect more fruits, while energy-oriented preferences collect more batteries.Survival initially increases with energy preference but can fall when excessive energy priority leads to requesting help.
  • Distribution of learned preference states: More than half of observed states are closest to strong energy priority, while intermediate preference states also occur, demonstrating graded rather than purely binary regulation.The resulting distribution is interpreted as a state-dependent preference landscape rather than one globally fixed scalarization weight.
  • Distribution of learned preference states: For the pure achievement preference (1, 0)⊤, 93.3% of 2,760 observed states occur when no battery remains available for collection.This pattern is associated with situations where further energy-oriented behavior has limited practical value.
  • Preference transitions within an episode: Within episodes, preferences remain stable during useful strategies and shift around resource acquisition or impending energy depletion.A representative trajectory moves from energy priority to an intermediate preference before battery collection, then toward achievement priority, and later back toward energy.
  • Quantitative comparison of preference-regulation strategies: The Emotional Preference Model achieves 14.97 ± 4.18 survival steps, exceeding the best fixed preference at 12.88 ± 4.34 and the battery-based handcrafted policy at 14.53 ± 4.11.The comparison uses the basic environment and 200,000 episodes.
  • Quantitative comparison of preference-regulation strategies: The directly optimized Survival Model reaches 16.00 ± 4.08 survival steps, but the Emotional Preference Model collects more fruits while achieving nearly the same battery collection.The paper attributes the survival difference to the emotional model’s constraint to the pretrained controller’s represented behaviors.
  • Distribution of learned preference states: The learned preference function is not a copy of one handcrafted switching rule; it maps environmental context to broader, interpretable priority changes.Intermediate states and transitions around multiple task-relevant events support this distinction.

5. Theoretical Analysis

The theoretical analysis shows that preference regulation is constrained by the inner MORL repertoire, with performance determined by its representation capacity. It also formalizes when dynamic preference regulation can match unrestricted optimization and how it reorganizes existing behaviors.

  • Restricted policy space: The outer preference generator selects among behaviors represented by the inner policy family rather than creating arbitrary low-level behaviors.This makes preference regulation representation-limited.
  • Representation error: The representation error has components from finite preference coverage and approximation of the unrestricted optimal policy.The resulting optimality gap is tied to how well the inner repertoire represents the ideal policy at each state.
  • Optimality guarantees: Dynamic preference regulation does not inherently impose a performance penalty; a gap appears only when the optimal outer policy is not representable by the inner policy family.If the unrestricted optimum belongs to the representable policy space, the gap vanishes.
  • Optimality guarantees: The discount factor γout amplifies representation error, making long-horizon tasks more demanding of inner-repertoire completeness.The amplification factor is 1/(1−γout).
  • Optimality guarantees: A deterministic preference output can attain the mixed-policy optimum because the Bellman objective is linear in state-wise mixture coefficients.Thus, one-hot selection is sufficient under the stated finite discounted MDP setting.
  • Behavioral implications: The outer model inherits useful skills and composes context-dependent behavior by selectively activating different regions of the inner repertoire.Experiments connect this mechanism to systematic preference-conditioned behaviors and state-dependent selection, while survival reached 14.97 ± 4.18 steps versus 16.00 ± 4.08 for the direct Survival Model.

6. Limitations

The framework is limited by its functional scope, state representation, synthetic discrete environments, inner behavioral repertoire, survival objective, and current optimization method.

  • Scope of emotional modeling: The model captures only state-dependent regulation of competing goal priorities, not richer human emotional phenomena such as appraisal, physiology, social cognition, or subjective experience.The authors therefore frame emotional preference as a functional computational construct rather than a complete human-like emotional state.
  • Temporal representation: The preference generator uses only instantaneous state input, so observed temporal persistence does not establish history-dependent emotional dynamics.A latent affective state or recurrent memory would be needed to represent path dependence, cumulative appraisal, and delayed adaptation.
  • Experimental scope: Experiments remain confined to synthetic discrete grid worlds, leaving scalability to continuous control, partial observability, and real-world interaction open.The advanced environment adds stochastic danger and safety objectives but remains synthetic.
  • Dependence on the inner repertoire: The attainable outer-task performance depends on the representation capacity and coverage of the pretrained inner MORL policy family.An insufficiently expressive repertoire may prevent desirable high-level behaviors from being realized.
  • Empirical performance boundary: The Emotional Preference Model achieves lower survival performance than the directly optimized Survival Model, despite outperforming tested fixed-preference strategies.This comparison reflects the constrained policy space available through the preference-conditioned repertoire.
  • Outer-objective scope: The experiments use long-term survival as the sole outer objective, although richer goals could require conditioning on persistent values as well as environmental state.Examples of broader goals include human well-being, connection, and happiness.
  • Optimization considerations: A systematic comparison with alternative optimization methods for continuous preference spaces remains future work.The current procedure uses a DDPG-style actor-critic with a structured, potentially non-smooth mapping to discrete actions.

7. Discussion

The discussion frames emotional preference as learned behavioral priority regulation rather than a discrete emotion label, and interprets its benefits and limits through the learned repertoire.

  • Emotional preference as a computational role: Emotional preference denotes functional regulation of competing objective priorities, not an internal category such as fear or happiness.The framework changes goal organization and priority without requiring a dedicated named-emotion module.
  • Emotional preference as a computational role: The same decision machinery can support instrumental behavior and emotional preference regulation because the organization of goals changes rather than the primitive action mechanism.The distinction concerns priority structure, not necessarily a separate action-selection mechanism.
  • Endogenous versus handcrafted preferences: Handcrafted mappings can outperform some fixed preferences, but they require externally specified rules and prior knowledge about relevant environmental variables.The proposed approach instead optimizes the state-to-preference mapping directly against a high-level goal.
  • Contextual priority regulation: Learned preferences reflect combinations of state conditions rather than a direct encoding of a single physiological variable.Achievement preference is associated with states where no further battery can be collected, while other priorities arise under different conditions.
  • Preference regulation and behavioral reorganization: The outer controller reorganizes available goal-directed behaviors and can activate represented skills without relearning them from primitive actions.Its emergence is constrained because absent primitives cannot be produced automatically.
  • Theoretical interpretation of performance: The Emotional Preference Model obtains 14.97±4.18 survival steps versus 16.00 ± 4.08 for direct Survival Model optimization.The discussion attributes this difference to representation error from searching through a constrained preference-conditioned repertoire.
  • Theoretical interpretation of performance: If the inner policy family is sufficiently expressive, ϵrep →0 and the outer policy can approach the unrestricted optimum.This motivates expanding repertoire coverage and compositional richness.
  • Broader computational formulation: The framework positions emotional preference as a computational interface connecting multi-objective reinforcement learning, hierarchical reinforcement learning, computational emotion, and adaptive preference learning.Each preference dimension remains behaviorally functional and semantically interpretable because it corresponds to an explicit objective.

8. Conclusion

The paper concludes that outer reinforcement learning can generate context-dependent emotional preferences over a pretrained MORL repertoire. Experiments and theory support this mechanism while retaining clear representation and performance constraints.

  • Framework: The framework separates acquisition of goal-directed behaviors from regulation of their relative priorities through a pretrained MORL controller and outer preference generator.The outer generator learns eθ : S →∆m−1 from a high-level goal.
  • Operational interpretation: Emotional preference is defined operationally as learned regulation of competing lower-level priorities within a goal-directed cycle, without claiming complete human-like emotion.The interpretation is grounded in the goal-directed perspective on emotion.
  • Empirical findings: Learned preferences develop contextualized, temporally persistent shifts across energy, achievement, and safety objectives without explicitly specified preference rules.The model outperforms evaluated fixed-preference and handcrafted contextual strategies, but direct survival optimization remains stronger in raw survival performance.
  • Theory: The theoretical analysis bounds the outer controller through policy representation error and establishes a sufficient condition for zero optimality gap.Represented behaviors can be inherited and selectively activated through state-dependent regulation.
  • Conclusion: The results support viewing emotional preference as endogenous regulation of lower-level goal priorities rather than a predefined module or additional reward signal.This offers a computational bridge between multi-objective reinforcement learning and goal-directed theories of emotion.

Appendix C. Optimization Problem

The appendix establishes optimal-policy existence for finite discounted MDPs and for the mixed space formed by state-dependent selection among fixed base policies.

  • Optimal policy existence: Finite-state, finite-action discounted MDPs admit a deterministic stationary policy attaining the optimal value at every state.The proof uses the Bellman optimality equation, a maximizing action at each state, and uniqueness of the policy-evaluation solution.
  • Status of the appendix results: The appendix presents these results as standard discounted-MDP theory used to support subsequent derivations, not as a paper contribution.The existence theorem is included for self-containedness.
  • Mixed policy formulation: The mixed policy space consists of state-dependent simplex choices over k fixed stochastic base policies.Each simplex row acts as a high-level action in an equivalent MDP with continuous linear transitions and state-only reward.
  • Vertex optimality: Because the Bellman expression is linear over the simplex, an optimal choice at each state occurs at a vertex corresponding to one base policy.Thus, deterministic one-hot base-policy selection suffices for an optimum in the mixed space.
  • Mixed-space optimum: The constructed mixed policy attains the pointwise maximum over the mixed policy space under the finite discounted assumptions.The proof again relies on matching Bellman systems and invertibility when γout < 1.

Appendix D. Optimality Theorem

The appendix characterizes the optimality of policies formed by mixing base policies with state-dependent emotion weights, comparing this restricted class with all stationary policies.

  • Policy-space construction: The mixed policy space is generated from k base policies using state-wise emotion weights on the probability simplex.The full policy space contains all randomized stationary policies, while the mixed space is restricted to policies formed from the given bases.
  • Zero-gap condition: The optimality gap vanishes when an optimal full-space policy belongs to the mixed policy space.Under that condition, the restricted policy class contains a policy achieving the unrestricted optimal value.
  • Restricted optimality: The componentwise maximum value over the mixed policy space cannot exceed the maximum over the full stationary-policy space.This follows from the inclusion of the mixed policy space within the full policy space.

Appendix E. Theoretical Analysis of the Gap

The analysis derives an exact expression and an upper bound for the gap between unrestricted and preference-regulated policies, linking the gap to the inner repertoire’s representation error.

  • Norm bound: The resolvent bound ∥(I − γP)^−1∥∞ ≤ 1/(1 − γ) controls the discounted transition dynamics in the gap analysis.The bound applies to row-stochastic transition matrices with γ ∈ [0, 1).
  • Setup: The appendix defines the mixed policy value through state-dependent convex combinations of base policies and compares it with the unrestricted optimal value.The setting uses finite state and action spaces, a discounted objective, state-dependent rewards, and transition operators induced by policies.
  • Exact gap: The exact optimality-gap expression follows by comparing the resolvents of the unrestricted optimal policy and the best mixed policy.The derivation uses the difference between their transition operators and the corresponding value functions.
  • Representation-error bound: The optimality gap is upper-bounded by a factor proportional to γout/(1 − γout), the physical-transition norm, and the representation error.The representation-error theorem applies the bound to the projection of the unrestricted optimal policy into the mixed policy space.
  • Zero-gap condition: The gap is zero when the unrestricted optimal policy’s action distribution lies in the base-policy convex hull at every state.That condition makes the global representation error zero, which forces the infinity norm of the gap to be zero.

Appendix F. Policy Classes and Properties under the One-Hot Constraint

The appendix analyzes one-hot preference regulation, its relationship to convex-combination policy classes, and conditions under which restricted policy values match unrestricted optimal values.

  • One-hot policy class: Under the one-hot constraint, each state selects one base policy, so the overall policy uses that base policy’s action distribution at the state.The emotional matrix has one-hot rows, with exactly one selected base-policy index per state.
  • Policy-class nesting: The one-hot policy set is contained in the convex-combination policy space, which is contained in the full stationary-policy space.One-hot vectors are simplex extreme points, and convex combinations remain valid stationary action distributions.
  • Optimal-value equivalence: The optimal values under one-hot and convex-combination policies are equal in the discounted state-reward setting.The equivalent high-level action formulation attains its state-wise maximum at a simplex extreme point.
  • Restricted optimality: The convex-combination policy class matches the full policy optimum if and only if it contains an optimal full-space policy.This is the equality condition for the restricted-versus-unrestricted optimality comparison.
  • Zero-gap condition: A sufficient zero-gap condition is that each state’s optimal action distribution equals the distribution of some base policy at that state.The appendix cautions that this condition is not necessary: a one-hot policy with the same value function can also suffice.

Appendix G.1. Training Hyperparameters

The appendix specifies training duration, network outputs, and the pretraining and soft-update procedures used for the outer preference generator and inner controller.

  • Training duration: Training lasts 20,000 rounds in the basic environment and 160,000 rounds in the advanced environment.These are the reported training-round counts for the two environments.
  • Outer preference generator: The outer network generates a preference vector from the current state, constrained to the probability simplex.For the basic environment, the state dimension is 19 and the reward dimension is 2; for the advanced environment, they are 37 and 3.
  • Outer preference generator: The outer network is a multilayer fully connected network that outputs state-dependent objective preferences.The output components are nonnegative and sum to one in both environments.
  • Critic and inner controller: The outer critic evaluates long-term values of state-preference pairs, while the inner network outputs action-and-reward-dimension Q-values.The inner network uses an action space of size A = 6 and is pretrained before outer optimization.
  • Optimization: Outer optimization uses a DDPG-style soft-update mechanism for the outer network and critic target networks.The target networks are updated softly to improve training stability.

Appendix H.1. Outer Training Process Plots

Outer training increases survival and battery collection before stabilizing, while fruit collection decreases, reflecting changing behavioral priorities. The learned preferences produce interpretable trade-offs, contextual switching, and temporal persistence among achievement, energy, and safety.

  • Outer Training Process Plots: Survival duration and battery collection initially increase and then stabilize, while fruit collection decreases during outer training.Danger-zone exposure also increases and stabilizes, indicating reallocation among lower-level objectives rather than single-objective maximization.
  • Preference–Behavior Relationships: High-energy preferences favor battery collection, achievement weights above approximately 0.4 favor fruit collection, and safety-heavy preferences generally avoid danger zones.Battery collection can also emerge under high achievement preference because energy supports subsequent fruit collection.
  • Comparative Performance: The direct Survival Model achieves 16.26 ± 9.04 survival steps but only 0.03±0.25 fruit collections, illustrating the cost of optimizing outside the inner repertoire.The performance difference is consistent with the theoretical distinction between full physical-policy optimization and repertoire-constrained optimization.
  • Comparative Performance: The Emotional Preference Model achieves 14.81 ± 9.56 survival steps, 1.14±1.12 battery collections, and 0.76 ± 1.24 fruit collections.Its broader objective organization contrasts with pure survival maximization.
  • Preference Dynamics: The preference distribution includes energy-, safety-, and achievement-dominant states alongside intermediate regions, providing evidence for graded contextual regulation.The largest groups are the energy-dominant, safety-dominant, and achievement-dominant preferences.
  • Preference Dynamics: Preference trajectories persist across multiple transitions and change around goal-relevant environmental events, including shifts toward mixed energy–safety preferences.These patterns support context sensitivity and temporal persistence.

Appendix I.7. Comparative Analysis

The comparative analysis finds that emotional preference regulation improves over fixed and handcrafted strategies while organizing behavior across competing objectives. Its explicit preference representation supports auditing, but does not guarantee an interpretable causal mechanism or eliminate control risks.

  • Performance Comparison: 14.81 ± 9.56 survival steps let the Emotional Preference Model outperform evaluated fixed-preference policies and handcrafted contextual baselines.The best fixed-preference strategy reaches 14.24 ± 9.15 steps, and the battery-based handcrafted model reaches 14.39 ± 9.44.
  • Performance Comparison: 16.26 ± 9.04 survival steps make the direct Survival Model strongest on survival, because it is not constrained by the inner preference-conditioned repertoire.The Emotional Preference Model instead produces substantially more achievement-oriented behavior: 0.76 ± 1.24 versus 0.03 ± 0.25 fruit collections.
  • Multi-Objective Extension: The advanced environment extends the framework from two competing objectives to a richer three-objective setting while retaining interpretable contextual preference regulation.The three objectives are achievement, energy, and safety.
  • Interpretability and Auditing: The explicit preference vector links semantically meaningful objective priorities to behavioral changes, enabling structured behavioral auditing.Auditing can examine the generated preference, its reasonableness for the state, and the corresponding inner-policy behavior.
  • Interpretability and Auditing: The interpretable preference output does not guarantee explainability because preference generation remains a learned nonlinear process.An inspectable intermediate variable need not reveal an interpretable causal mechanism.

Appendix J.1.3. Contextual Adaptability

Context-dependent preference regulation can adapt priorities without manually specifying every switching rule, but autonomous priority changes introduce risks under distribution shift, misspecified objectives, and unsafe inner skills. The authors therefore frame monitoring, constraints, and human intervention as integral safeguards rather than assuming interpretability ensures safe deployment.

  • Contextual Adaptability: Different environmental situations induce different objective priorities, allowing adaptation to changing resource and risk conditions without a complete manually specified state-to-preference rule.The authors caution that emergent preferences should be evaluated and audited rather than automatically treated as desirable values.
  • Optimization Risks: Outer task optimization does not guarantee human-intended preference regulation when environmental dynamics or poorly explored repertoire regions support unintended strategies.Energy-oriented preferences can occur when no battery remains, exploiting existing state-action structure to extend survival.
  • Distribution Shift: Under distribution shift, weakly represented states may trigger unexpected and behaviorally consequential preference drift.The preference generator learns from encountered training states, so deployment beyond that distribution requires monitoring novelty and preference trajectories.
  • Repertoire Safety: Unsafe behaviors encoded in the inner repertoire may be reused when they improve the high-level objective, so safety must be addressed across skill acquisition and activation.Controlling the repertoire itself is therefore part of controlling the final system.
  • Objective Specification: Survival-only optimization does not guarantee appropriate trade-offs among safety, efficiency, task completion, or human welfare.The flexibility of preference generation does not remove the need to specify the high-level objective carefully.
  • Scope Boundaries: The current evidence is limited to computational preference regulation in controlled settings and does not establish safe, fair, private, or ethically suitable real-world deployment.The experiments use synthetic environments without human participants, personal data, or physical-world intervention.
  • Mitigation and Control: Safety floors can preserve adaptive regulation while preventing the outer optimizer from completely suppressing safety-critical objectives.The proposed extension constrains minimum safety priority under predefined conditions.
  • Responsible Interpretation: The framework’s autonomous priority changes improve contextual adaptability and expose an auditable intermediate variable, but require predictability and alignment checks under distribution shift.Ethical control is treated as part of the architecture rather than inferred solely from interpretable preference patterns.
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