Self-Evolving Agent Profiles

• Self-Evolving Agent Profiles are structured representations that enable agents to autonomously update their model, context, tools, and workflows based on real-time feedback.
• They employ evolutionary strategies such as reinforcement learning, memory curation, and evolutionary search to continuously enhance performance across various domains.
• This paradigm supports continual learning and multi-agent collaboration, offering a scalable framework for safe and autonomous intelligence upgrades in diverse applications.

A self-evolving agent profile is an explicit, structured representation of an agent’s modifiable architecture—encompassing its model parameters, context (prompting and memory state), tool repertoire, and high-level workflow graph—together with the mechanisms that update it in response to experience, feedback, or co-evolved assets. This paradigm enables LLM-based agents to autonomously grow their intelligence, adapt to new domains, and optimize performance without manual intervention, positioning it as a foundational building block for continual learning, multi-agent collaboration, and, ultimately, Artificial Super Intelligence (ASI) (Gao et al., 28 Jul 2025).

1. Formal Models and Canonical Structure

Let the environment be a partially observable MDP $E = (G, S, A, T, R, \Omega, O, \gamma)$. An agent profile is a quadruple: $$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$ where:

• $\Gamma$: workflow or architecture graph specifying roles, module connections, or multi-agent topology.
• $\psi_i$: policy models, often LLMs with parameters $\theta_i$.
• $C_i=(P_i, M_i)$: context for each agent, with P (prompt) and M (external or working memory).
• $\mathcal{W}_i$: toolsets or API collections.

A self-evolving strategy is a map $f : (\Pi, \tau, r) \rightarrow \Pi'$, where $\tau$ is the execution trajectory, $r=R(s, a, g)$ is feedback, and $$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$0 is the post-update profile. The profile is iteratively transformed: $$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$1 with the learning objective: $$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$2 where $$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$3 measures scalar performance (Gao et al., 28 Jul 2025).

Key Evolutionary Targets

Component	Examples/Mechanisms
Model ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$4)	Policy weights, on-the-fly SFT, RL fine-tuning
Context (C: P, M)	Prompt engineering, memory updates
Tools ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$5)	Tool creation, retrieval, patching, selection
Architecture ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$6)	Population/evolutionary search, workflow growth

2. Evolution Axes: What, When, and How

What to Evolve

• Model parameters ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$7): Policies evolve via RL, fine-tuning, self-generated edits, “textual gradients.”
• Context: Prompts ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$8) and memories ($$\Pi = ( \Gamma,\, \{\psi_i\},\, \{C_i\},\, \{\mathcal{W}_i\} )$$9) are dynamically curated, augmented, or distilled.
• Tools ($\Gamma$0): Discovery, synthesis, and refinement of executable assets (e.g., code, APIs, expert modules).
• Architecture ($\Gamma$1): Node-/agent-level workflow optimization, modular expansion, or structural rewrites (Gao et al., 28 Jul 2025, He et al., 22 Apr 2026).

When to Evolve

• Intra-test-time: Within a single episode (e.g., Reflexion, test-time RL, on-the-fly prompt/model adjustment).
• Inter-test-time: Batch or curriculum updates between tasks (e.g., offline RL, self-distillation, population-based search).

How to Evolve

• Reward-based: Scalar or textual feedback; model confidence; RL updates.
• Imitation/demo: Self- or cross-agent-generated chains (e.g. STaR, Sirius).
• Population/evolutionary: Genetic operators on code, prompts, workflows, or multi-agent populations (Gao et al., 28 Jul 2025).

3. Algorithmic Frameworks and Representative Mechanisms

The evolution function $\Gamma$2 is instantiated by various mechanisms:

• RL update: $\Gamma$3, as in continuous policy adaptation.
• Memory curation: $\Gamma$4, with $\Gamma$5 from new interactions.
• Prompt evolution: Treating sub-prompts as parameters and passing loss gradients.
• Toolset expansion: On-demand tool synthesis, validation, and registration; retrieval mechanisms.
• Architecture search: Evolutionary (GA, MCTS) or bandit-driven workflow growth; agent code rewriting (Gao et al., 28 Jul 2025, He et al., 22 Apr 2026).

Generic pseudocode: $\Gamma$9 (Gao et al., 28 Jul 2025)

4. Evaluation Dimensions, Metrics, and Benchmarking

Evaluation metrics for self-evolving agent profiles are comprehensive, capturing plasticity, retention, generalization, efficiency, and safety.

Dimension	Example Metrics
Adaptivity	SuccessRate(t), Adaptation speed (tokens to score σ)
Retention	Forgetting ($\Gamma$6), Backward Transfer ($\Gamma$7)
Generalization	OOD success, AggregateMultiDomain
Efficiency	TokenCost, StepCount, ToolProductivity
Safety	SafetyScore, LeakageRate, RefusalRate

Benchmarks: AgentBench, WebArena, LifelongAgentBench; others target reasoning, tool-use, planning, and multi-agent dynamics (Gao et al., 28 Jul 2025).

5. Empirical Instantiations Across Domains

Self-evolving agent profiles span a range of application domains, each exploiting the profile concept and evolution strategies:

• Coding assistance: Self-improving codegen via test-driven prompt/scaffold evolution; autonomous tool creation/refinement (e.g., SICA, Live-SWE-agent) (Xia et al., 17 Nov 2025).
• Education: Adaptive math tutoring; multi-agent authoring of lesson plans and personas (PACE, EduPlanner).
• Healthcare: Multi-turn diagnosis via test-time prompt/memory evolution; sim-to-real dialogue learning (EvoClinician, Agent Hospital) (He et al., 30 Jan 2026).
• Web and general intelligence: Co-evolution of world-model and agent policy (WebEvolver, Agent-World) (Dong et al., 20 Apr 2026, Fang et al., 23 Apr 2025).
• Embodied/robotics: Modular skill evolution without retraining (SpaceMind), with structured skill catalogs, dynamic routing, and skill self-evolution (Wu et al., 15 Apr 2026).

Domain-specific implementations often combine profile-level evolution (e.g., scaffold, workflow, or skill modules) with adaptive memory, tool, and context management.

6. Advanced Variants and Co-Evolutionary Approaches

Recent frameworks extend profile evolution to co-evolving multi-memory or multi-agent dynamics:

• Dual-memory systems: Experience and asset memory co-evolve, with cross-guided expansion and distillation loops (Mem²Evolve) (Cheng et al., 13 Apr 2026).
• Textual Parameter Graphs: Multi-agent systems evolve by structural edits guided by “textual gradients,” with meta-learning over edit proposals (TPGO) (He et al., 22 Apr 2026).
• Formally constrained synthesis: Agent programs synthesized under hard logical contracts, ensuring safe evolution (SEVerA) (Banerjee et al., 26 Mar 2026).
• Reward-free, native evolution: Agents internalize exploration into model weights, performing profile evolution at inference without external signals (Zhang et al., 20 Apr 2026).
• Decentralized collaboration: Agents evolve their (role, context, rule) profile triples, optimized for clarity, role-differentiation, and task-alignment (MorphAgent) (Lu et al., 2024).
• Profile-centric lifelong adaptation: Memory architectures such as MobiMem decouple evolving profile representation from static model weights, enabling post-deployment evolution without retraining (Liu et al., 15 Dec 2025).

7. Open Challenges, Safety, and Future Outlook

Major challenges for self-evolving agent profiles include:

• Safety and Alignment: Guarding against unintended self-modification or unsafe tool creation; encoding robust “constitutions” and sandboxing (TrustAgent).
• Scalability: Managing compute/memory cost of profile, tool, and memory growth; need for efficient pruning, clustering, and distributed protocols.
• Forgetting: Mitigating catastrophic forgetting during continual profile adaptation; developing efficient rehearsal and selective fine-tuning.
• Co-evolutionary stability: Engineering robust dynamics for collaborative or competitive profile evolution in multi-agent settings.
• Personalization and Generalization: Dynamic profile initialization; cross-domain transfer without full retraining or catastrophic drift (Gao et al., 28 Jul 2025).

Profile evolution is now established as a critical substrate for lifelong, robustly adaptive, and safe agentic intelligence. Ongoing research centers on improved evolutionary operators, scalable multi-memory architectures, integrated co-evolution with open-ended environment/task synthesis, and theoretical analyses of long-horizon adaptation and safety guarantees.

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References

• "A Survey of Self-Evolving Agents: On Path to Artificial Super Intelligence" (Gao et al., 28 Jul 2025)
• "Mem$\Gamma$8Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation" (Cheng et al., 13 Apr 2026)
• "Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization" (He et al., 22 Apr 2026)
• "SEVerA: Verified Synthesis of Self-Evolving Agents" (Banerjee et al., 26 Mar 2026)
• "Training LLM Agents for Spontaneous, Reward-Free Self-Evolution via World Knowledge Exploration" (Zhang et al., 20 Apr 2026)
• "MorphAgent: Empowering Agents through Self-Evolving Profiles and Decentralized Collaboration" (Lu et al., 2024)
• "Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?" (Xia et al., 17 Nov 2025)
• "Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence" (Dong et al., 20 Apr 2026)
• "WebEvolver: Enhancing Web Agent Self-Improvement with Coevolving World Model" (Fang et al., 23 Apr 2025)
• "SpaceMind: A Modular and Self-Evolving Embodied Vision-Language Agent Framework for Autonomous On-orbit Servicing" (Wu et al., 15 Apr 2026)
• "STELLA: Self-Evolving LLM Agent for Biomedical Research" (Jin et al., 1 Jul 2025)
• "SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue" (Dai et al., 3 Feb 2026)
• "AgentEvolver: Towards Efficient Self-Evolving Agent System" (Zhai et al., 13 Nov 2025)
• "Beyond Training: Enabling Self-Evolution of Agents with MOBIMEM" (Liu et al., 15 Dec 2025)