Title: Adaptive Minds: Empowering Agents with LoRA-as-Tools

Original: arXiv:2510.15416v2 Announce Type: replace

Abstract:

This study explores a novel architecture where Low-Rank Adaptation (LoRA) adapters function as executable tools, allowing a foundational language model to dynamically choose and activate them as needed. We posit that if these adapters are fine-tuned to deliver significant domain-specific improvements and are accompanied by precise metadata, the base model can accurately direct inquiries to the most suitable expert. This process effectively consolidates the advantages of numerous specialized adapters into one cohesive system. We present "Adaptive Minds," a versatile framework designed to examine both single-step routing mechanisms and multi-step agentic reasoning. Within this paradigm, agents can sequentially call upon various adapters in conjunction with other utilities—such as external APIs, retrieval systems, or execution environments—analyzing their results across multiple iterations. This approach transforms adapters into modular competencies or memory blocks that can be combined during the reasoning process, rather than being applied in a fixed manner.

Our evaluations demonstrate that the routing component achieves a 98.3% accuracy rate when selecting from a library of 30 adapters. Furthermore, well-optimized specialists yield strict-scorer improvements ranging from +4.6 to +84.0 percentage points across nine distinct task families, all under a unified training regimen. The Adaptive Minds (AM) router successfully accumulates these benefits, performing within 5 percentage points of direct specialist application on every benchmark where queries exhibit domain-specific signals. These results indicate that the success of this method hinges on the quality and specialization of the individual adapters. Moreover, facilitating the flexible integration of numerous experts can substantially broaden the practical abilities of language model agents, advancing the field toward more generalized, tool-enhanced intelligence.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC