# CARD: Towards Conditional Design of Multi-agent Topological Structures arXiv: https://arxiv.org/abs/2603.01089 ยท HTML: https://mlsanity.com/paper/2603.01089/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. - Builds on 27 of its 34 references that are in the map. - First posted on arXiv 1 Mar 2026. ## Placed in - LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/) ## Builds on (most cited first, 27 of 27) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020) - [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021) - [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023) - [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024) - [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023) - [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023) - [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://mlsanity.com/paper/2305.19118/) (2023) - [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023) - [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019) - [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024) - [CodeT: Code Generation with Generated Tests](https://mlsanity.com/paper/2207.10397/) (2022) - [Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View](https://mlsanity.com/paper/2310.02124/) (2023) - [Continual Learning for Large Language Models: A Survey](https://mlsanity.com/paper/2402.01364/) (2024) - [G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks](https://mlsanity.com/paper/2410.11782/) (2024) - [Mind's Eye: Grounded Language Model Reasoning through Simulation](https://mlsanity.com/paper/2210.05359/) (2022) - [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025) - [VersiCode: Towards Version-controllable Code Generation](https://mlsanity.com/paper/2406.07411/) (2024) - [Towards LifeSpan Cognitive Systems](https://mlsanity.com/paper/2409.13265/) (2024) - [Graph-Augmented Large Language Model Agents: Current Progress and Future Prospects](https://mlsanity.com/paper/2507.21407/) (2025) - [Large Knowledge Model: Perspectives and Challenges](https://mlsanity.com/paper/2312.02706/) (2023) - [QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://mlsanity.com/paper/2512.19134/) (2025) - [Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System](https://mlsanity.com/paper/2602.08335/) (2026) - [Environment-Aware Code Generation: How far are We?](https://mlsanity.com/paper/2601.12262/) (2026) - [Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents](https://mlsanity.com/paper/2309.05999/) (2023) ## Built on by (most cited first, 9 of 9) - [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026) - [SIGMA: Skill-Incidence Graphs for Compositional Multi-Agent Design](https://mlsanity.com/paper/2606.19758/) (2026) - [CoMem: Collective-Individual Memory Synergy for Evolutionary Multi-Agent Systems](https://mlsanity.com/paper/2609.15009/) (2026) - [Learning What to Skip: Counterfactual Credit Assignment for Efficient Multi-Agent LLM Workflows](https://mlsanity.com/paper/2609.30734/) (2026) - [Multi-Agent System Search via Active Substructure-aware Policy Optimization](https://mlsanity.com/paper/2609.32430/) (2026) - [Re-derivability Decides What a Staged Agent Pipeline Recovers After an Upstream Fault](https://mlsanity.com/paper/2609.32802/) (2026) - [Topological Coherence for Self-evolving Multi-agent Systems](https://mlsanity.com/paper/2609.37953/) (2026) - [Decoupled Multi-Agent Orchestration](https://mlsanity.com/paper/2610.07556/) (2026) - [Competence-Aware Orchestration for LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2610.13144/) (2026) ## Abstract (arXiv) Large language model (LLM)-based multi-agent systems have shown strong capabilities in tasks such as code generation and collaborative reasoning. However, the effectiveness and robustness of these systems critically depend on their communication topology, which is often fixed or statically learned, ignoring real-world dynamics such as model upgrades, API (or tool) changes, or knowledge source variability. To address this limitation, we propose CARD (Conditional Agentic Graph Designer), a conditional graph-generation framework that instantiates AMACP, a protocol for adaptive multi-agent communication. CARD explicitly incorporates dynamic environmental signals into graph construction, enabling topology adaptation at both training and runtime. Through a conditional variational graph encoder and environment-aware optimization, CARD produces communication structures that are both effective and resilient to shifts in model capability or resource availability. Empirical results on HumanEval, MATH, and MMLU demonstrate that CARD consistently outperforms static and prompt-based baselines, achieving higher accuracy and robustness across diverse conditions. The source code is available at: https://github.com/Warma10032/CARD.