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Adaptive Minds: Empowering Agents with LoRA-as-Tools

large-language-models › model-architecture

📄 Abstract

Abstract: We present Adaptive Minds, an agentic system that treats LoRA adapters as domain-specific tools. Instead of relying on a single fine-tuned model or rigid rule-based routing, our approach empowers the base LLM itself to act as a semantic router analyzing each query and dynamically selecting the most relevant LoRA tool. This enables the agent to seamlessly switch between different domain experts on demand. By combining the flexibility of multi-agent orchestration with the efficiency of parameter-efficient fine-tuning, Adaptive Minds delivers accurate, specialized responses while preserving conversational ability. The system is built with LangGraph for workflow management, supports both API and web interfaces, and is fully open source, providing a scalable and extensible foundation for domain-adaptive AI assistance.

Authors (2)

Pavan C Shekar

Ashwanth Krishnan

Submitted

October 17, 2025

arXiv Category

cs.AI

arXiv PDF

Key Contributions

Adaptive Minds presents a system that treats LoRA adapters as domain-specific tools, with the base LLM acting as a semantic router. This allows dynamic selection of the most relevant LoRA tool for each query, enabling specialized responses while maintaining conversational ability and offering efficient, parameter-efficient fine-tuning.

Business Value

Enables the creation of highly adaptable AI assistants that can specialize in various domains without requiring separate, large models for each. This leads to more versatile and cost-effective AI solutions.

Paper Metadata

Innovation Type

System/Framework

Deployment Feasibility

High. Leverages existing LLMs and LoRA techniques. LangGraph provides a robust workflow management system. Open-source nature aids adoption.

Limitations Addressed

Limitations of single fine-tuned models,Inflexibility of rule-based routing,Need for dynamic adaptation to different domains,Balancing specialization and general conversational ability

Performance Gains

Accurate, specialized responses,Preservation of conversational ability,Efficient parameter usage

Technical Tags

LoRA AdaptersDomain-specific ToolsLLM as RouterSemantic RoutingDynamic Tool SelectionParameter-Efficient Fine-Tuning (PEFT)Multi-agent OrchestrationLangGraphAPI/Web InterfacesDomain-Adaptive AI

Research Topics

Model SpecializationAgent SystemsLLM RoutingEfficient Fine-tuningModular AI

Methods & Architectures

LoRA-as-ToolsLLM-based Semantic RouterDynamic Tool SelectionLangGraph for workflow management Base LLMLoRA Adapters

Applications & Tasks

AI Assistants Specialized AI Services Customer Support Information Retrieval Reliance on single fine-tuned modelsRigid rule-based routingDifficulty in seamlessly switching between domain expertsBalancing specialization with conversational ability Dynamically selecting domain-specific tools (LoRAs)Providing specialized responses on demandSeamlessly integrating multiple AI capabilitiesBuilding adaptable AI agents

Related Fields

Machine LearningNatural Language ProcessingArtificial IntelligenceSoftware EngineeringModel Compression

Keywords

LoRALLMadapterstoolssemantic routerdynamic selectionPEFTagent systemLangGraphdomain adaptationmodular AI

Academic Context

#Model Specialization#Agent Systems#LLM Routing#Efficient Fine-tuning#Modular AI

Technology Stack

Frameworks & Libraries

LoRALangGraph

Commercial Potential

Potential Products

Modular AI Assistant PlatformsDomain-Specific AI Service ProvidersCustomizable Chatbot Frameworks

Target Industries

TechnologyCustomer ServiceSaaSE-commerce

Use Case Examples

An AI assistant that can switch between medical, legal, and technical domainsCustomer support bots that access specialized knowledge bases dynamicallyTools for rapid prototyping of domain-specific AI applications

Competitive Edge

Offers a more flexible and efficient approach to domain specialization compared to monolithic fine-tuned models or complex multi-agent systems, by using LoRAs as dynamically selectable tools.

Market Opportunity

Large, driven by the demand for adaptable and specialized AI solutions.

Revenue Models

SaaS for AI assistant platformslicensing of the frameworkconsulting services.

Resource Requirements

Compute Needs

Moderate for routing and inference; potentially high for training/managing multiple LoRA adapters.

Data Requirements

Domain-specific datasets for training LoRA adapters.

Deployment Constraints

Managing and versioning multiple LoRA adapters. Ensuring efficient routing and low latency.

Scalability

Scalability is good due to the modular nature of LoRA adapters and efficient routing. Adding new domains is straightforward.

Production Readiness

Maturity Level

Research/Development

Time to Market

1-2 years

Licensing

Open Source

Patent Potential

Moderate, for the specific implementation of LoRA-as-Tools and the LLM-based routing mechanism.

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