Multi-agent systems powered by large language models have achieved strong performance on complex tasks, yet naive collaboration topologies often cause high communication costs and redundant context. Existing methods usually use a fixed communication graph and manage collaboration structure and shared memory in separate modules. Our log analysis of several representative systems shows that this separation leads to multiple copies of the same key facts in dialogue, memory and model inputs. We address this issue with EvoHyper, a framework based on an evolving hypergraph topology for multi-agent collaboration. In EvoHyper, a single hypergraph represents agents and shared memory, and each hyperedge serves as a collaboration unit that binds a group of agents to that shared memory. During execution a controller edits the hypergraph through a small set of predefined evolution operations, so collaboration units can spawn, update and merge as tasks unfold. Experiments on four benchmarks covering mathematical reasoning and code generation show that EvoHyper is (I) high-performing, achieving 3.2% to 7.8% accuracy gains over state-of-the-art methods, (II) efficient, reducing token consumption by up to 23.5%, and (III) adaptive, adjusting topology complexity according to task requirements.

Parameter-Efficient Fine-Tuning (PEFT), especially Low-Rank Adaptation (LoRA), has emerged as a promising approach to fine-tuning large language models(LLMs) while reducing computational and memory overhead. However, LoRA assumes a uniform rank r for each incremental matrix, not accounting for the varying significance of weight matrices across different modules and layers. AdaLoRA leverages Singular Value Decomposition (SVD) to parameterize updates and employs pruning of singular values to introduce dynamic rank allocation, thereby enhancing adaptability. However, during the training process, it often encounters issues of slow convergence speed and high computational overhead. To address this issue, we propose HyperAdaLoRA, a novel framework that accelerates the convergence of AdaLoRA by leveraging a hypernetwork. Instead of directly optimizing the components of Singular Value Decomposition (P, 𝛬, Q), HyperAdaLoRA employs a hypernetwork based on attention mechanisms to dynamically generate these parameters. By pruning the outputs of the hypernetwork that generates the singular values, dynamic rank allocation is achieved. Comprehensive experiments on various datasets and models demonstrate that our method achieves faster convergence without sacrificing performance. Moreover, our method generalizes well to other LoRA-based approaches, highlighting its strong generalization capability.