Adaptive Minds: Empowering Agents with LoRA-as-Tools

• URL: http://arxiv.org/abs/2510.15416v1
• Date: Fri, 17 Oct 2025 08:10:06 GMT
• Title: Adaptive Minds: Empowering Agents with LoRA-as-Tools
• Abstract summary: Adaptive Minds is an agentic system that treats LoRA adapters as domain-specific tools.<n>System is built with LangGraph for workflow management, supports both API and web interfaces, and is fully open source.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering [62.63376387138257]
  We propose a plug-and-play intervention framework that adaptively steers large language models (LLMs) reasoning in activation space.<n>RISER constructs a library of reusable reasoning vectors and employs a lightweight Router to dynamically compose them for each input.<n>The Router is optimized via reinforcement learning under task-level rewards, activating latent cognitive primitives in an emergent and compositional manner.
  arXiv  Detail & Related papers  (2026-01-14T08:04:33Z)
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering [51.07491603393163]
  tAgent is a framework that formulates multi-agent QA as a knowledge-graph-guided routing problem supervised by empirical performance signals.<n>By leveraging soft supervision and weighted aggregation of agent outputs, Agent learns principled collaboration schemes that capture the complementary strengths of diverse agents.
  arXiv  Detail & Related papers  (2025-10-06T23:20:49Z)
• Multi-Agent Tool-Integrated Policy Optimization [67.12841355267678]
  Large language models (LLMs) increasingly rely on multi-turn tool-integrated planning for knowledge-intensive and complex reasoning tasks.<n>Existing implementations typically rely on a single agent, but they suffer from limited context length and noisy tool responses.<n>No existing methods support effective reinforcement learning post-training of tool-integrated multi-agent frameworks.
  arXiv  Detail & Related papers  (2025-10-06T10:44:04Z)
• SEQR: Secure and Efficient QR-based LoRA Routing [53.52716967527183]
  Low-Rank Adaptation (LoRA) has become a standard technique for parameter-efficient fine-tuning of large language models.<n> Efficiently selecting the correct LoRA adapter for a given input remains a challenge.<n>We introduce SEQR, an unsupervised LoRA routing algorithm designed to maximize efficiency while providing strict routing guarantees.
  arXiv  Detail & Related papers  (2025-09-22T17:59:38Z)
• RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory [57.449129198822476]
  RCR is a role-aware context routing framework for multi-agent large language model (LLM) systems.<n>It dynamically selects semantically relevant memory subsets for each agent based on its role and task stage.<n>A lightweight scoring policy guides memory selection, and agent outputs are integrated into a shared memory store.
  arXiv  Detail & Related papers  (2025-08-06T21:59:34Z)
• Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning [57.36978335727009]
  Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs) In this paper, we propose a framework that adaptively retrieves and composes multiple LoRAs based on input prompts.
  arXiv  Detail & Related papers  (2024-06-24T05:24:41Z)
• Mixture-of-LoRAs: An Efficient Multitask Tuning for Large Language Models [7.966452497550907]
  We propose the Mixture-of-LoRAs (MoA) architecture for multi-task learning with large language models (LLMs) Multiple domain-specific LoRA modules can be aligned with the expert design principles observed in Mixture-of-Experts (MoE) Each LoRA model can be iteratively adapted to a new domain, allowing for quick domain-specific adaptation.
  arXiv  Detail & Related papers  (2024-03-06T03:33:48Z)
• A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning [34.85351481228439]
  We propose a unified coordinating framework to efficiently utilize existing ML-based agents. We show that it can effectively utilize different ML-based agents and find better configurations with 1.414.1X speedups on the workload execution time.
  arXiv  Detail & Related papers  (2023-03-10T05:27:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.