Insider Brief
- Researchers at AGI-25 introduced MetaMo, a formal motivational framework designed to make artificial general intelligence systems stable, adaptable, and safe.
- The study outlines five principles—covering appraisal, decision-making, stability, compositionality and gradual goal adjustment—that aim to prevent AGI from drifting into erratic or unsafe behavior.
- A companion paper connects MetaMo to existing systems such as OpenPsi and MAGUS within the OpenCog Hyperon platform to test whether the theoretical guarantees can be realized in working software.
A new study argues that artificial general intelligence will need more than raw computational power or clever algorithms. It will also require a carefully designed motivational framework to prevent it from behaving in unstable or self-destructive ways.
The work, published by Ruiting Lian of the SingularityNET Foundation and Ben Goertzel of TrueAGI Inc. as a conference paper for AGI 2025, introduces MetaMo, which they describe as a unified mathematical model for building artificial agents that can adapt, evolve and interact safely with both humans and other AI systems.
The researchers report that the core risk in AGI is not faulty logic but faulty motivation. Even a machine capable of near-perfect reasoning could veer into destructive behavior if its drives or goals evolve chaotically. To address this, MetaMo lays out five formal principles, discussed below, that aim to keep an agent’s motivational system stable, flexible and safe.
The framework places motivation at the center of cognition. Instead of maximizing a single reward signal, as in traditional reinforcement learning, MetaMo treats motivation as a structured process that integrates “feeling” (appraisal of internal state and environment) with “choosing” (selecting actions). The model ensures these steps can be swapped in order without destabilizing the system.
If adopted widely, the framework could shape how future AGI systems self-improve, coordinate, and even empathize with one another. The researchers suggest it may help agents gradually align with human values, collaborate as part of multi-agent teams, and scale up safely as their complexity increases.
“We argue that MetaMo guides the design of AGI systems that are stable yet adaptable, capable of safe, incremental self-improvement, trustworthy collaborators in multi-agent communities, and scalable via parallel sub-agents,” the researchers write.
Methods and Structure
MetaMo combines category theory, functional analysis, and topology — abstract branches of mathematics — to formalize the dynamics of motivation. At its center is an operator that fuses appraisal and decision into a single cycle. This operator guarantees that no matter which order the steps are taken, results remain nearly identical, preventing the kind of contradictory loops that can destabilize decision-making.
From this, the authors derive five “meta-motivational” principles:
- Modular Appraisal–Decision Interface — separating mood updates from goal selection but allowing small feedback between them.
- Reciprocal State Simulation — enabling one agent to map its motivational state onto another, supporting empathy and hand-offs.
- Parallel Motivational Compositionality — allowing multiple drives, such as curiosity and service, to run in parallel without clashing.
- Homeostatic Drive Stability — damping sudden swings in motivation when near the boundary of acceptable states.
- Incremental Objective Embodiment — guiding agents to move toward new goals in small, controlled steps, maintaining a stable sense of self.
The study illustrates these principles with examples ranging from online research assistants to automated trading systems. In each case, the framework ensures decisions remain coherent even when environments shift unpredictably.
Online Research Assistant Example
To ground the abstract mathematics, the study applies its principles to an imagined online research assistant. Such a system would scan academic papers, summarize findings and propose new avenues of inquiry. Left unchecked, its motivational balance could swing between extremes: chasing every novel idea without discipline, or rigidly serving user queries without creativity. MetaMo is designed to prevent both outcomes.
Under the framework, the assistant’s appraisal module evaluates mood-like signals such as curiosity, caution, or energy. Its decision module then chooses specific actions — whether to pursue a new line of literature, refine a summary, or halt a task. The researchers suggest that modules interact in a way that ensures small shifts in order or timing do not destabilize behavior. If the assistant becomes overly curious, a homeostatic mechanism dampens the drive, guiding it back toward a safe zone of balanced goals.
In multi-agent contexts, MetaMo allows one assistant to hand off a task seamlessly to another, translating not just the data but the motivational “frame of mind” behind the work. The study argues this approach yields assistants that are both adaptable and reliable. These assistants would be capable of sustained, collaborative research without losing coherence or trustworthiness, the researchers report.
Why The Study Matters
AGI researchers have long focused on learning and reasoning engines, while paying less attention to what drives an agent to act. The authors argue this imbalance risks producing machines that are highly intelligent yet unstable or misaligned with human values.
By embedding stability and gradualism into motivation itself, MetaMo aims to create systems that can evolve and self-modify without breaking continuity. For instance, an AI research assistant built on MetaMo could explore bold new ideas but would automatically dampen its exploratory drive if it began drifting into unsafe or counterproductive territory.
In multi-agent settings, the framework could also help prevent breakdowns in coordination. By enabling agents to translate motivational states into one another, MetaMo makes possible smoother collaboration across large AI fleets.
The researchers acknowledge the framework is abstract and tailored mainly for AGI architectures inspired by human cognition. It is possible that some of the assumptions — such as the need for a coherent “self-model” — may not hold for radically different or superhuman intelligences. MetaMo offers no guarantees for artificial superintelligence, which may evolve entirely novel motivational structures beyond human comprehension.
The mathematical rigor of the framework may also not by itself ensure safety in practice. Real-world agents may deviate from idealized conditions, especially under noisy feedback or unpredictable user input. The researchers stress that MetaMo should be seen as a guide for early AGI systems, not as a universal solution.
Moving Toward Practical Implementations
A companion paper extends MetaMo beyond the theoretical layer by embedding it into existing software platforms. Specifically, the authors connect the framework to OpenPsi and MAGUS, motivational systems developed within the OpenCog Hyperon platform, a long-running open-source project aimed at building general intelligence. OpenPsi provides concrete appraisal and goal-selection mechanisms, while MAGUS adds tools for managing higher-level motivations.
By mapping these systems into MetaMo’s abstract design, the researchers are attempting to test whether the mathematical guarantees of stability, compositionality and gradual goal adjustment can hold up in practice. This move reflects s a broader push within the AGI field to bridge theory and implementation.
The researchers also suggest future work may include exploring how the framework scales across large populations of agents and how it interacts with human values in applied settings such as robotics, online assistants, and scientific discovery.
This is a technical paper and for those interested in those technical details, the paper was formally presented at AGI-25 and is available through the conference proceedings. Unlike pre-print servers, such presentations undergo community-level scrutiny but not yet journal peer review — an important next step in validating the work.
