Value Cores for Inner and Outer Alignment: Simulating Personality Formation via Iterated Policy Selection and Preference Learning with Self-World Modeling Active Inference Agents
Bibliographic record
Abstract
Humanity faces multiple existential risks in the coming decades due to technological advances in AI, and the possibility of unintended behaviors emerging from such systems. We believe that better outcomes may be possible by rigorously exploring frameworks for intelligent (goal-oriented) behavior inspired by computational neuroscience. Here, we explore how the Free Energy Principle and Active Inference (FEP-AI) framework may provide solutions for these challenges via affording the realization of control systems operating according to principles of hierarchical Bayesian modeling and prediction-error (i.e., surprisal) minimization. Such FEP-AI agents are equipped with hierarchically-organized world models capable of counterfactual planning, realized by the kinds of reciprocal message passing performed by mammalian nervous systems, so allowing for the flexible construction of representations of self-world dynamics with varying degrees of temporal depth. We will describe how such systems can not only infer the abstract causal structure of their environment, but also develop capacities for “theory of mind” and collaborative (human-aligned) decision making. Such architectures could help to sidestep potentially dangerous combinations of systems with high intelligence and human-incompatible values, since such mental processes are entangled (rather than orthogonal) in FEP-AI agents. We will further describe how (meta-)learned deep goal hierarchies may also well-describe biological systems, suggesting that potential risks from “mesa-optimisers” may actually represent one of the most promising approaches to AI safety: minimizing prediction-error relative to causal self-world models can be used to cultivate modes of policy selection and agent personalities that robustly optimize for achieving goals that are consistently aligned with both individual and shared values. Finally, we will describe how iterative policy selection and preference learning can result in "value cores" or self-reinforcing, relatively stable attracting states that agents will seek to return to through their goal-oriented imaginings and actions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".