arXiv:2510.15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke. We hypothesize that, when adapters are trained to provide strong domain-specific gains and are exposed with clear metadata, a base model can reliably route queries to the appropriate expert, effectively aggregating the benefits of many specialized adapters within a single framework. We introduce Adaptive Minds, a general framework within which we study both single-step routing and multi-step agentic rea
The proliferation of specialized language models and the demand for more efficient, adaptable AI systems are driving advancements in dynamic model orchestration.
This framework offers a significant step towards more autonomous and versatile AI, potentially collapsing traditional workflow layers by enabling models to self-select and apply expert knowledge.
AI systems can now dynamically route tasks to specialized adapters, leading to more efficient, accurate, and scalable model applications without requiring constant retraining of large base models.
- · AI platform providers
- · Developers of specialized AI models
- · Industries with diverse AI application needs
- · Companies relying on monolithic, untargeted AI models
- · SaaS providers for highly specific, narrow AI tasks
Increased efficiency and capability of agentic AI systems through dynamic tool-use and expertise aggregation.
Acceleration in the development of sophisticated AI agents capable of handling complex, multi-domain tasks with greater autonomy.
Reconfiguration of software architectures towards highly modular, adaptive AI components, impacting enterprise AI adoption and deployment strategies.
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Read at arXiv cs.AI