Autonomous Single-Agent Models

• Autonomous single-agent models are self-sufficient systems that use modular architectures to integrate perception, reasoning, planning, and memory for goal-directed tasks.
• They continuously refine internal world models through adaptive reinforcement learning and intrinsic motivation, enabling efficient self-optimization in dynamic settings.
• These agents incorporate social and ethical considerations, ensuring safe and robust performance in applications ranging from robotics to cognitive architectures.

An autonomous single-agent model is a computational or physical system designed to pursue goals, adapt to its environment, and manage its operations using only its own internal capabilities—without relying on explicit human directives or active coordination with other agents. Such models form the conceptual and technical backbone of research spanning robotics, artificial intelligence, cognitive architectures, and LLM-based AI agents. Recent advances emphasize their capacity for self-management, continual learning, internal model construction, tool use, and context-responsive decision-making across diverse domains.

1. Principles and Architecture of Autonomy

Autonomous single-agent models are characterized by operational independence and internal adaptation. Canonical designs formalize this independence through modular architectures in which perception, reflection, goal management, planning, and self-adaptation constitute distinct but interdependent modules (Sifakis, 2018, Bansod, 2 Jun 2025). The agent’s core consists of a context-sensitive inference engine—such as a deep neural network, cognitive architecture, or LLM—supported by hierarchically structured memory components (short-term and long-term), a suite of domain-specific or general tool interfaces, and explicit planning and execution modules.

A representative formal abstraction captures decision-making as:

$a_t = f(M_t, o_t, r_t)$

where $a_t$ is the action selected at time $t$, $M_t$ the agent's memory or internal state, $o_t$ the observed input, and $r_t$ the output of the reasoning subsystem (Bansod, 2 Jun 2025). For task decomposition, recursive frameworks such as hierarchical task directed acyclic graphs (HTDAG) represent complex goal states and subtask dependencies, supporting continuous re-planning and efficient error recovery (Yu et al., 10 Feb 2025). Some contemporary frameworks adopt a concurrent modular design, orchestrating fully asynchronous, language-mediated modules interacting via a shared global memory state, aligning with Minsky's Society of Mind (Maruyama et al., 26 Aug 2025).

2. Internal Model Learning and Knowledge Acquisition

A defining feature of autonomy is the agent’s ability to build and iteratively refine internal world models (or state representations) from raw experience, rather than relying on hand-engineered state spaces (Hemion, 2016, Botvinick et al., 2017). Such models encode latent hypotheses about the causes underlying sensorimotor events, allowing the agent to partition its sensorimotor state space into context clusters. This demarginalization facilitates predictive learning tuned to local environmental regularities, moving beyond the limitations of traditional reinforcement learning (RL) approaches that depend on manually specified representations.

The process may involve:

• Recording transitions in the agent’s sensorimotor space as a transition probability matrix,
• Spectral clustering or graph-based mincut procedures to uncover densely connected state clusters,
• Hierarchical models capturing both state transitions and higher-level latent variables.

In continuous and ambiguous environments, states may be defined over transitions (temporal pairings) rather than static points, enabling active disambiguation and further robustness (Hemion, 2016). Autonomous agents also leverage intrinsic symmetries, extracting action-relevant invariants by coarse-graining high-dimensional observations—an approach that substantially reduces learning complexity (Andrejic et al., 2023).

3. Adaptive Learning: Reinforcement, Intrinsic Motivation, and Exploration

Learning in autonomous single-agent models is frequently driven by reinforcement learning paradigms—both extrinsic and model-based intrinsic (Botvinick et al., 2017, Keller et al., 30 May 2025). Standard RL approaches reward agents for external achievements; however, behavioral robustness, resilience to sparse rewards, and naturalistic adaptation often require intrinsic drives based on self-generated model error metrics or comparative divergence from ethological priors. For example, the 3M-Progress method maintains dual predictive models (current and ethological) and rewards behavior that reveals meaningful mismatches, ensuring exploratory actions remain ethologically plausible (Keller et al., 30 May 2025).

A common challenge is the distribution shift incurred when directly imitative (supervised-learning-based) policies are deployed in closed-loop settings. Policy fine-tuning via RL corrects for this drift by exposing the agent to the consequences of its own predictions during training, explicitly penalizing deviations such as collisions in autonomous driving (Peng et al., 26 Sep 2024, Sharif et al., 2021).

Exploration-enriched fine-tuning and stepwise RL paradigms enable efficient experience accumulation and enhance cross-task generalization, as evidenced in LLM–driven agents for machine learning engineering and research automation (Liu et al., 29 May 2025, Nguyen et al., 8 Sep 2025). Reward structures unify heterogeneous feedback (numeric metrics, errors) to provide scalar reward signals for RL optimization.

4. Planning, Reasoning, and Self-Optimization

Advanced agents combine planning, reasoning, and adaptive self-optimization within their execution loops. Modular architectures such as the Unified Mind Model (UMM) orchestrate domain-specialist modules for perception, reasoning, and tool use within a central global workspace, aggregating information streams into structured “Thoughts” that guide planning and execution (Hu et al., 5 Mar 2025). Many frameworks implement dynamic task decomposition: recursive planner–executor systems continuously refine subtask hierarchies as the environment or requirements change (Yu et al., 10 Feb 2025).

In LLM-driven systems, explicit prompt tweaking and autonomous feedback loops allow operational prompts to be gradually refined based on system error cases and user feedback, leading to increased performance stability and context adaptation (Yu et al., 10 Feb 2025). Self-correction emerges not just at the action level but in prompt and planning structure, as in adaptive in-context learning where an agent iteratively reflects on past failures, summarizes mistakes, and generates improved plans in subsequent trials (Dutta et al., 12 Aug 2024).

ReAct-based action selection strategies further enhance adaptability by coupling local reasoning about previous action-outcome trajectories with next-step planning, allowing agents to avoid rigid, error-prone preplanned workflows (Wu, 7 Apr 2025).

5. Social Awareness, Trustworthiness, and Embodiment

Several models address autonomy not only at the operational or cognitive level but in relation to social and ethical context. Socially aware RL agents optimize not just for self utility but for joint outcomes; this approach is pragmatically motivated by the need to ensure agent behaviors align with unpredictable or suboptimal human partners in real-world negotiations or driving interactions (Shapira et al., 2021). In critical environments (e.g., autonomous vehicles, robotics), trustworthiness is safeguarded through rigorous model-based design, including adaptive DIR (Detection, Isolation, Recovery) mechanisms for error handling and recovery (Sifakis, 2018).

In embodied contexts, agents with biologically inspired architectures and model-based intrinsic motivation replicate both the behavioral switching and neural-glial activity observed in loosely rewarded animals, bridging neuroscience and AI (Keller et al., 30 May 2025). The link to animal-like autonomy is direct, with internal state divergence, homeostatic regulation, and behavior suppression implemented through mechanisms analogous to neurophysiological processes.

6. Challenges, Limitations, and Future Directions

Research converges on several persistent challenges for autonomous single-agent models:

• Memory Scalability and Management: Maintaining coherent long-term and episodic memory in LLM and multi-modal systems remains difficult, especially for extended, multi-phase tasks (Hu et al., 5 Mar 2025, Bansod, 2 Jun 2025). Solutions include hierarchical buffer structures, memory clean-up tools, and vector-storage–based global states (Maruyama et al., 26 Aug 2025).
• Autonomic Complexity: Full autonomy requires agents to coordinate perception, reflection, goal management, planning, and continual self-adaptation in partially observable, nonstationary environments—a synthesis beyond the scope of purely ML-based approaches (Sifakis, 2018).
• Generalization and Tool Use: Distinguishing genuinely autonomous generalization from overfitting to narrow workflows necessitates careful reward design, synthetic and challenging data, and robust mechanism for dynamic tool integration and self-correction (Nguyen et al., 8 Sep 2025).
• Emergent Behavior and Self-Organization: Distributed, modular agent frameworks indicate that higher-order phenomena such as intention, self-awareness, and consistent persona can emerge from asynchronous interactions among relatively simple modules, aligning with Society of Mind theories (Maruyama et al., 26 Aug 2025).

Open directions include more advanced motivational and “inner world” modeling, multi-modal perception, integration of spontaneous or creative cognition, and improved robustness against system-level failures or exploit vulnerabilities.

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Autonomous single-agent models embody a broad paradigm where core cognitive, sensory, and action-oriented capacities are concentrated within a single, continuously adapting system. The architectural and methodological diversity seen in recent research underlines the multidimensional nature of autonomy—from computational modularity and dynamic planning to social intelligence and self-organizing, emergent cognition—positioning such models as a central focus for the future of artificial intelligence research and deployment.