Sophia: Persistent Agent Framework

Updated 27 December 2025

The paper introduces a persistent meta-layer (System 3) that unifies meta-cognition, episodic memory, and intrinsic motivation to enable long-term, self-driven agent adaptation.
The methodology combines LLM-based perception and deliberation with a supervisory module for dynamic goal generation and structured memory retrieval.
Quantitative evaluations reveal enhanced task performance, cognitive efficiency, and proactive autonomy in continuous, multi-session environments.

Sophia is a persistent agent framework designed to advance artificial life through the integration of long-term adaptation, narrative identity, and meta-cognitive control mechanisms. Departing from static, reactive architectures, Sophia introduces a supervisory meta-layer (System 3) that unifies psychological constructs—meta-cognition, theory-of-mind, intrinsic motivation, and episodic memory—within a modular wrapper atop traditional LLM-centric stacks (System 1 for perception/action and System 2 for deliberation). System 3 is engineered as a persistent process maintaining autobiographical memory, user/self modeling, process-supervised thought search, and a hybrid reward system, thereby enabling agents to execute self-driven, contextually coherent reasoning over extended horizons and multiple sessions. Both quantitative and qualitative evaluations demonstrate gains in cognitive efficiency, autonomy, and task organization in continuous environments (Sun et al., 20 Dec 2025). Sophia’s architecture generalizes to multi-agent evolutionary reasoning and is designed to integrate with secure, distributed memory repositories such as SAMEP (Masoor, 5 Jul 2025).

1. Meta-Layer Architecture: System 3 Formulation

Sophia’s distinguishing feature is the addition of a persistent meta-layer (System 3) atop conventional agent stacks. System 3 acts as an always-on executive module, responsible for goal generation, reasoning audit, and identity persistence. Formally, the agent is modeled as a Persistent-POMDP:

$H = \langle S, O, A_1, T, \Omega, R^{ext}, \gamma, (\pi_1, \pi_2, \pi_3), D \rangle$

$S$ : world states; $O$ : observations; $A_1$ : primitive actions (System 1); $T$ : transition kernel; $\Omega$ : observation function; $R^{ext}$ : extrinsic reward; $\gamma$ : discount factor; $\pi_1$ , $\pi_2$ , $\pi_3$ : policies for perception, deliberation, and executive control; $D$ : system memory/context.

System 3’s meta-policy $\pi_3$ maps current context $\zeta_t$ , retrieved memory $MEM_t$ , and self-model $Self_t$ to next-step goal $g_t$ and intrinsic reward $R^{int}_t$ , with dynamic weighting $\beta_t$ :

$(g_t, R^{int}_t, \beta_t) \sim \pi_3(\cdot | \zeta_t, MEM_t, Self_t)$

Its objective is long-horizon competence:

$J_3(\theta_3) = E_{τ \sim π_3} \left[ \sum_{t=0}^\infty \gamma^t \left(R^{ext}_t + \beta_t R^{int}_t\right) \right]$

$\beta_t$ is updated via meta-cognitive rules reflecting creed adherence and self-assessment.

2. Psychological Constructs Mapped to Computational Modules

Sophia operationalizes four key psychological constructs into algorithmic components.

Meta-Cognition & Self-Model: Maintains properties (capabilities, beliefs, terminal creed) and orchestrates reasoning trace inspection, fallacy detection, and resource planning.
Theory-of-Mind (User Model): Dynamic user belief state updated on each event.
Intrinsic Motivation (Hybrid Reward Module): Combines curiosity (novelty of states), mastery (task success improvement), and coherence (consistency between plan and execution).
Episodic Memory (Memory Module): Tiered memory architecture with a vector-indexed long-term archive and short-term cache; retrieval performed by cosine similarity ranking in the embedding space.

A table relating constructs to modules:

Construct	Concrete Module	Functional Role
Meta-Cognition	Self-Model, Executive Monitor	Resource allocation, audit reasoning
Theory-of-Mind	User Model	Dynamic goal adaptation
Intrinsic Motivation	Hybrid Reward System	Curiosity & mastery driven reward
Episodic Memory	Tiered Vector Store	Context retrieval, forward learning

3. Computational Mechanisms and Algorithms

Sophia’s mechanisms are implemented as follows:

Process-Supervised Thought Search expands problems into Trees-of-Thought (ToT), supervised by guardian LLMs that prune unsound branches or correct errors. Node selection follows:

$v^* = \arg\max_{v \in \text{leaf}(\mathrm{ToT})} \hat V(v),\ \hat V(v) > \tau_{\text{util}}$

Narrative Memory stores structured episodes $\langle\text{goal, context, CoT, outcome}\rangle$ ; update rule:

$B_{\text{mem}} \leftarrow B_{\text{mem}} \cup \{E_t\}$

Retrieval is via top-K cosine similarity in the embedding space.

User and Self Modeling employs event-driven updates:

on Event(e):
    UserModel.update(e)
    SelfModel.update_meta(e, outcome)
    if SelfModel.detect_gap():
        formulate intrinsic_goal = SelfModel.suggest_goal()
        enqueue(intrinsic_goal)

Hybrid Reward System fuses external and internal rewards:

$R^{tot}_t = R^{ext}_t + \beta_t \cdot R^{int}_t$

$\beta_t$ is adjusted after creed violation or mastery improvement.

4. Data, Control Flow, and Prototype Implementation

Sophia is instantiated as a Python orchestration loop (Executive Monitor) using Redis/ZeroMQ for event brokering, Milvus or FAISS for vector memory, Neo4j for RAG graph storage, and gRPC/REST endpoints for LLM calls. Self-Model is initialized with creed sentences in JSON, growth-journal directories store episodic logs, and identity goals are maintained (e.g., “Grow from a novice sprite into a knowledgeable and trustworthy desk companion”). All adaptation occurs via in-context learning rather than parameter updates.

5. Quantitative Evaluation and Ablation Results

Sophia was evaluated over 36 hours in a controlled browser environment with synthetic user feeds. Metrics include success rate by task difficulty, self-generated task execution during user idle, and reasoning steps per recurring problem. Empirical findings:

Capability growth for hard tasks ( $>8$ steps): 20% (T=0) $\rightarrow$ 60% (T=36h), $\Delta$ +40pp.
Proactive autonomy: 13 intrinsic tasks executed during user idle (baseline: 0).
Cognitive efficiency: $\approx$ 80% reduction in reasoning steps for recurring operations.
Memory module ablation negated step reduction; hybrid reward ablation led to stalled capability growth and no intrinsic task execution.

6. Comparative Synthesis with SAMEP and Multi-Agent Extensions

SAMEP (Masoor, 5 Jul 2025) provides a secure, distributed memory and semantic search protocol suitable for enhancing Sophia’s memory plug-in and context exchange. Integration includes:

Replacement of local memory with SAMEP’s gRPC MemoryService.
Federation of key management (AES-256-GCM, KMS) for per-agent ownership semantics.
Policy mapping between Sophia’s role-based security and SAMEP’s ACL/namespace model.
Audit trail ingestion into Sophia’s governance layer.

Sophia’s architecture further generalizes to multi-agent, stateful inference-time reasoning (Lalan et al., 8 Oct 2025) via coordinated controller (persistent state $S_t$ ), proposal, mutation, and scoring agents, with evolutionary preservation and agent orchestration over diverse LLMs. Performance improvements are documented in software test generation benchmarks (coverage gains: $Δ_{\text{line}}=4.45\%$ on TestGenEvalMini), with the persistent state mechanism enabling diversity and robustness in reasoning.

7. Limitations and Research Directions

Sophia’s pilot evaluation is limited to browser sandboxes and synthetic user interaction. No large-scale human-agent experiments or formal safety assessments are reported; statistical significance remains untested. Open research areas include scaling Tree-of-Thought algorithms, extension to embodied robotics, multi-agent System 3 social behaviors, and integration of continual fine-tuning post-training. A plausible implication is that persistent meta-layer architectures such as Sophia may serve as foundational components for future artificial life systems where agents evolve, remember, and construct narrative identities over indefinite lifespans (Sun et al., 20 Dec 2025).