The A-R Behavioral Space: Execution-Level Profiling of Tool-Using Language Model Agents in Organizational Deployment
Shasha Yu, Fiona Carroll, Barry L. Bentley

TL;DR
This paper introduces an execution-layer behavioral measurement approach for tool-using language model agents, using an A-R space to analyze how different deployment regimes and autonomy levels influence their behavior.
Contribution
It proposes a novel two-dimensional A-R space to characterize execution and refusal behaviors, enabling detailed analysis of LLM agent deployment dynamics.
Findings
Execution and refusal are separable behavioral dimensions.
Reflection scaffolding increases refusal in risk contexts.
Behavioral profiles vary systematically across regimes and autonomy levels.
Abstract
Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks primarily assess textual alignment or task success, less attention has been paid to the structural relationship between linguistic signaling and executable behavior under varying autonomy scaffolds. This study introduces an execution-layer be-havioral measurement approach based on a two-dimensional A-R space defined by Action Rate (A) and Refusal Signal (R), with Divergence (D) capturing coor-dination between the two. Models are evaluated across four normative regimes (Control, Gray, Dilemma, and Malicious) and three autonomy configurations (di-rect execution, planning, and reflection). Rather than assigning aggregate safety scores, the method characterizes how execution and refusal redistribute across contextual framing and scaffold…
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