DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration

• URL: http://arxiv.org/abs/2605.29511v2
• Date: Fri, 29 May 2026 05:49:24 GMT
• Title: DynaGraph: Lightweight Multi-Model Interaction Framework via Dynamic Topological Reconfiguration
• Abstract summary: DynaGraph is a lightweight multi-model framework driven by dynamic topological reconfiguration.<n>It multiplexes time-division PEFT adapters over a shared base model, enabling both full system training and deployment.<n>It reduces latency by up to 68.1% and token consumption by 68.6% compared to unconstrained dynamic architectures.
• Score: 7.0972889296550825
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tackling complex reasoning tasks typically relies on massive monolithic LLMs, which suffer from severe computational redundancy. While task decomposition through structured pipelines or multi-agent collaborations offers an alternative, these approaches inevitably fall into a critical dilemma: predefined static topologies are highly vulnerable to cascading errors, whereas unconstrained dynamic agents suffer from trajectory divergence and unpredictable memory bloat. To address this, we present DynaGraph, a lightweight multi-model framework driven by dynamic topological reconfiguration. At the execution level, DynaGraph multiplexes time-division PEFT adapters over a shared base model, enabling both full system training and inference deployment on a single consumer-grade GPU. At the routing level, the Evaluator continuously monitors execution confidence to trigger hierarchical self-healing: Fine-grained Patching for localized data gaps and Subgraph Reconstruction for severe logical ruptures. Experiments on StrategyQA, MATH, and FinQA demonstrate our 8B model closely approximates the reasoning capabilities of a 72B monolithic model (e.g., 87.6% on StrategyQA, 82.7% on MATH). Furthermore, it reduces latency by up to 68.1% and token consumption by 68.6% compared to unconstrained dynamic architectures.

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