Agentic AI Adaptation Survey
Free while signed in. Answers cite the passages they came from.
Researchers from UIUC, Stanford, Berkeley, and other institutions present the first comprehensive taxonomy of adaptation strategies for agentic AI systems. The survey organizes recent advances into a unified framework covering how agents and their tools can be modified to achieve higher task performance, improved reliability, and better generalization across diverse scenarios. - **Four adaptation paradigms:** The framework categorizes methods into A1 (tool execution signaled agent adaptation using verifiable outcomes like code sandbox results), A2 (agent output signaled adaptation from evaluations of final answers), T1 (agent-agnostic tool adaptation where tools train independently), and T2 (agent-supervised tool adaptation where tools adapt using frozen agent feedback). - **Key trade-offs identified:** Agent adaptation (A1/A2) requires substantial compute for training billion-parameter models but offers maximal flexibility. Tool adaptation (T1/T2) optimizes external components at lower cost but may be constrained by frozen agent capabilities. T1 tools generalize well across agents while A1 methods may overfit without regularization. - **RLVR emergence:** The survey traces the evolution from early SFT and DPO methods to reinforcement learning with verifiable rewards (RLVR), where models learn directly from online interaction with tools and environments - marking a shift from pre-collected trajectories to dynamic, context-aware adaptation. - **Domain applications:** Demonstrates how adaptation strategies apply across deep research, software development, computer use, and drug discovery - with state-of-the-art systems increasingly combining multiple paradigms in cascaded architectures.
Four adaptation paradigms: The framework categorizes methods into A1 (tool execution signaled agent adaptation using verifiable outcomes like code sandbox results), A2 (agent output signaled adaptation from evaluations of final answers), T1 (agent-agnostic tool adaptation where tools train independently), and T2 (agent-supervised tool adaptation where tools adapt using frozen agent feedback).
Key trade-offs identified: Agent adaptation (A1/A2) requires substantial compute for training billion-parameter models but offers maximal flexibility. Tool adaptation (T1/T2) optimizes external components at lower cost but may be constrained by frozen agent capabilities. T1 tools generalize well across agents, while A1 methods may overfit without regularization.
RLVR emergence: The survey traces the evolution from early SFT and DPO methods to reinforcement learning with verifiable rewards (RLVR), where models learn directly from online interaction with tools and environments - marking a shift from pre-collected trajectories to dynamic, context-aware adaptation.
Domain applications: Demonstrates how adaptation strategies apply across deep research, software development, computer use, and drug discovery - with state-of-the-art systems increasingly combining multiple paradigms in cascaded architectures.
Abstract
We demonstrate the feasibility of quantum computing for large-scale, realistic chemical systems through the development of a new interface using a quantum circuit simulator and CP2K, a highly efficient first-principles calculation software. Quantum chemistry calculations using quantum computers require Hamiltonians prepared on classical computers. Moreover, to compute forces beyond just single-point energy calculations, one- and two-electron integral derivatives and response equations are also to be computed on classical computers. Our developed interface allows for efficient evaluation of forces with the quantum-classical hybrid framework for large chemical systems. We performed geometry optimizations and first-principles molecular dynamics calculations on typical condensed-phase systems. These included liquid water, molecular adsorption on solid surfaces, and biological enzymes. In water benchmarks with periodic boundary conditions, we confirmed that the cost of preparing second-quantized Hamiltonians and evaluating forces scales almost linearly with the simulation box size. This research marks a step towards the practical application of quantum-classical hybrid calculations, expanding the scope of quantum computing to realistic and complex chemical phenomena.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack