D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems

TL;DR

D3MAS (Decompose, Deduce, and Distribute), a hierarchical coordination framework addressing redundancy through structural design rather than explicit optimization, is proposed, a hierarchical coordination framework addressing redundancy through structural design rather than explicit optimization.

Abstract

Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundancy. Agents duplicate efforts in retrieval and reasoning processes. This inefficiency stems from a deeper issue: current architectures lack mechanisms to ensure agents share minimal sufficient information at each operational stage. Empirical analysis reveals an average knowledge duplication rate of 47.3\% across agent communications. We propose D3MAS (Decompose, Deduce, and Distribute), a hierarchical coordination framework addressing redundancy through structural design rather than explicit optimization. The framework organizes collaboration across three coordinated layers. Task decomposition filters irrelevant sub-problems early. Collaborative reasoning captures complementary inference paths across agents. Distributed memory provides access to non-redundant knowledge. These layers coordinate through structured message passing in a unified heterogeneous graph. This cross-layer alignment ensures information remains aligned with actual task needs. Experiments on four challenging datasets show that D3MAS consistently improves reasoning accuracy by 8.7\% to 15.6\% and reduces knowledge redundancy by 46\% on average.

Figures (7)

• Figure 1: Redundancy Breakdown by Coordination Method. We measure three types of redundancy across coordination methods. Memory redundancy represents the percentage of knowledge items retrieved by multiple agents. Reasoning redundancy quantifies semantically similar inference steps with cosine similarity exceeding 0.85. Task redundancy captures the percentage of overlapping sub-task assignments among agents. Stacked bars display total redundancy rate on the left vertical axis. Separate bars indicate computational efficiency gains relative to MetaGPT baseline on the right vertical axis. D³MAS reduces redundancy by 46% on average through cross-layer coordination. Results are averaged over HotpotQA and MMLU datasets with 4 to 8 agents per query.
• Figure 2: D³MAS hierarchical framework structures multi-agent reasoning through Decompose (blue), Deduce (green), and Distribute (purple) layers. The Decompose layer builds task dependency graphs like p4→p5 to avoid sub-problem overlap in chain-based systems. The Deduce layer uses dependency edges to enable reasoning reuse. Agent B directly reads Agent A's conclusions through the pink arrow, avoiding redundant inference. The Distribute layer assigns knowledge nodes to specific agents. Agent A is responsible for m1 and m2, Agent B for m4 and m5, and m3 is shared between both. This design reduces knowledge redundancy by 46% through hierarchical message passing and significantly improves efficiency compared to chain-based and existing graph methods.
• Figure 3: Performance comparison between vanilla agents and D³MAS-enhanced agents across eight different LLMs on seven evaluation metrics (Judge, Reason, Decept, Self-Aware, Compre, Coord, Rational). The yellow line represents D³MAS-enhanced agents while the blue shaded area shows vanilla agent performance. D³MAS consistently improves performance across most metrics for all models.
• Figure 4: Hyperparameter sensitivity analysis on MMLU and HumanEval. Optimal configurations: k=5 for top-k retrieval, ($\theta$)=0.65 for similarity threshold, 6 agents for scalability, and d=512 for embedding dimension.
• Figure 5: Impact of message passing iterations on reasoning accuracy and knowledge redundancy. Optimal performance occurs at L=3-5, balancing information propagation with computational efficiency.