TagsAdaptation of Agentic AI (Dec 2025)
- Title: Adaptation of Agentic AI (Dec 2025)
- Link: http://arxiv.org/abs/2512.16301v1
- Date: December 2025
Abstract
Summary:
This survey paper introduces a unified framework for the adaptation of agentic AI systems, organizing the research landscape into four core paradigms based on the optimization target (agent vs. tool) and the supervision signal source (tool execution vs. agent output). It systematically compares agent adaptation (A1 & A2), which modifies the core model, against tool adaptation (T1 & T2), which optimizes external components like retrievers and subagents around a frozen model. The paper highlights the emerging “symbiotic inversion” of the T2 paradigm, where tools are trained to serve specific agents, offering superior modularity and data efficiency.
Key Topics:
- Agentic AI
- Agent Adaptation
- Tool Adaptation
- Reinforcement Learning with Verifiable Rewards (RLVR)
- Adaptation Paradigms (A1, A2, T1, T2)
- Symbiotic Adaptation
- Deep Research
- Tool Learning