AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

TL;DR

AD-Bench is a real-world, trajectory-aware benchmark designed to evaluate LLM agents' performance in complex advertising and marketing tasks involving multi-round interactions and domain-specific tools.

Contribution

The paper introduces AD-Bench, a novel benchmark based on real-world advertising tasks with expert-verified answers and multi-tool trajectories, filling a gap in practical evaluation.

Findings

Gemini-3-Pro achieves Pass@1 = 68.0% on AD-Bench

Performance drops to Pass@1 = 49.4% on the most difficult level

Trajectory coverage is 70.1%, highlighting capability gaps in complex scenarios

Abstract

While Large Language Model (LLM) agents have achieved remarkable progress in complex reasoning tasks, evaluating their performance in real-world environments has become a critical problem. Current benchmarks, however, are largely restricted to idealized simulations, failing to address the practical demands of specialized domains like advertising and marketing analytics. In these fields, tasks are inherently more complex, often requiring multi-round interaction with professional marketing tools. To address this gap, we propose AD-Bench, a benchmark designed based on real-world business requirements of advertising and marketing platforms. AD-Bench is constructed from real user marketing analysis requests, with domain experts providing verifiable reference answers and corresponding reference tool-call trajectories. The benchmark categorizes requests into three difficulty levels (L1-L3) to evaluate agents' capabilities under multi-round, multi-tool collaboration. Experiments show that on AD-Bench, Gemini-3-Pro achieves Pass@1 = 68.0% and Pass@3 = 83.0%, but performance drops significantly on L3 to Pass@1 = 49.4% and Pass@3 = 62.1%, with a trajectory coverage of 70.1%, indicating that even state-of-the-art models still exhibit substantial capability gaps in complex advertising and marketing analysis scenarios. AD-Bench provides a realistic benchmark for evaluating and improving advertising marketing agents, the leaderboard and code can be found at https://github.com/Emanual20/adbench-leaderboard.