Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

arXiv:2609.32965 · cs.AI, cs.SE · Submitted 2026-09-26 · Read on arXiv

cs.AI, cs.SE

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/cooperbench/CooperBench

Terminology

• Benchmark — 53× in this paper · explained in 2 episodes
• Verification — 42× in this paper · explained in 2 episodes
• Ablation — 18× in this paper · explained in 1 episode
• Provenance — 15× in this paper · explained in 4 episodes
• Aggregation — 11× in this paper · explained in 1 episode
• Trace — 11× in this paper · explained in 2 episodes
• Abstraction — 9× in this paper · explained in 1 episode
• Grounding — 9× in this paper · explained in 3 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Structured Decision Layer — 7× in this paper
• Decode — 6× in this paper · explained in 2 episodes
• Actor — 5× in this paper · explained in 3 episodes
• Affordance — 5× in this paper · explained in 1 episode
• Core — 5× in this paper · explained in 2 episodes
• Multi-Agent System — 5× in this paper · explained in 6 episodes
• Verifier — 5× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Clustering — 4× in this paper · explained in 2 episodes
• Multi-agent Collaboration — 4× in this paper · explained in 2 episodes
• Progressive Disclosure — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Accountability — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Failure Taxonomy — 1× in this paper · explained in 2 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
• CooperBench: Why Coding Agents Cannot be Your Teammates Yet
• CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
• TheBotCompany: Self-Organizing Multi-agent Systems for Continuous Software Development
• ChatDev: Communicative Agents for Software Development
• Voyager: An Open-Ended Embodied Agent with Large Language Models
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
• ProgramBench: Can Language Models Rebuild Programs From Scratch?
• From Skills to Talent: Organising Heterogeneous Agents as a Real-World Company
• ExpeL: LLM Agents Are Experiential Learners
• MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
• VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection