# Evolve Together, Fall into Place: How Social Scaffolding Shapes Self-Evolving Multi-Agent Systems

arXiv: https://arxiv.org/abs/2610.19799 · HTML: https://mlsanity.com/paper/2610.19799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 26 of its 47 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 26 of 26)

- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Meta-Harness: End-to-End Optimization of Model Harnesses](https://mlsanity.com/paper/2603.28052/) (2026)
- [SkillOpt: Executive Strategy for Self-Evolving Agent Skills](https://mlsanity.com/paper/2605.23904/) (2026)
- [MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory](https://mlsanity.com/paper/2601.03192/) (2026)
- [CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification](https://mlsanity.com/paper/2604.01687/) (2026)
- [EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms](https://mlsanity.com/paper/2406.14228/) (2024)
- [Multi-Agent Collaboration via Evolving Orchestration](https://mlsanity.com/paper/2505.19591/) (2025)
- [AutoHarness: improving LLM agents by automatically synthesizing a code harness](https://mlsanity.com/paper/2603.03329/) (2026)
- [Multi-Agent Evolve: LLM Self-Improve through Co-evolution](https://mlsanity.com/paper/2510.23595/) (2025)
- [Do as We Do, Not as You Think: the Conformity of Large Language Models](https://mlsanity.com/paper/2501.13381/) (2025)
- [MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks](https://mlsanity.com/paper/2601.14652/) (2026)
- [Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets](https://mlsanity.com/paper/2604.02460/) (2026)
- [Multi-Agent Teams Hold Experts Back](https://mlsanity.com/paper/2602.01011/) (2026)
- [Conformity and Social Impact on AI Agents](https://mlsanity.com/paper/2601.05384/) (2026)
- [EvolveMem:Self-Evolving Memory Architecture via AutoResearch for LLM Agents](https://mlsanity.com/paper/2605.13941/) (2026)
- [Synthesizing Multi-Agent Harnesses for Vulnerability Discovery](https://mlsanity.com/paper/2604.20801/) (2026)
- [LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering](https://mlsanity.com/paper/2509.09614/) (2025)
- [Conformity Dynamics in LLM Multi-Agent Systems: The Roles of Topology and Self-Social Weighting](https://mlsanity.com/paper/2601.05606/) (2026)
- [Belief in Authority: Impact of Authority in Multi-Agent Evaluation Framework](https://mlsanity.com/paper/2601.04790/) (2026)
- [Large Language Models Exhibit Normative Conformity](https://mlsanity.com/paper/2604.19301/) (2026)
- [EvoAgent: An Evolvable Agent Framework with Skill Learning and Multi-Agent Delegation](https://mlsanity.com/paper/2604.20133/) (2026)
- [Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense](https://mlsanity.com/paper/2606.05743/) (2026)
- [Three AI-agents walk into a bar . . . . `Lord of the Flies' tribalism emerges among smart AI-Agents](https://mlsanity.com/paper/2602.23093/) (2026)
- [ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations](https://mlsanity.com/paper/2605.27908/) (2026)
- [Unified Context Evolution for LLM Agents](https://mlsanity.com/paper/2606.02304/) (2026)

## Abstract (arXiv)

Recent work lets LLM agents evolve their own harness, up to the team itself. Yet in human groups, the same abilities achieve different outcomes under different social conditions. Borrowing the notion of scaffolding from developmental psychology, we investigate whether the social scaffolding of a self-evolving multi-agent system (MAS), namely its team composition and structure of interdependence (e.g., collaboration, conformity, or competition), shapes how the team evolves. We present AGORA (Agents Growing through Organization and Relational Arrangements), a framework that varies the two axes independently across three evolution mechanisms. Across web search, codebase understanding, and document QA, we find that (1) evolution alone rarely helps, while the right scaffolding surpasses a strong agent evolved alone by up to 25 points; (2) it does so by moving authority to the most capable member while the others step back; and (3) this member, having grown within the team, outperforms the same model alone. Designing a self-evolving MAS thus requires designing its social scaffolding, which shapes not only how its agents learn, but who gets to lead.