$C^3$-Bench: The Things Real Disturbing LLM based Agent in Multi-Tasking

TL;DR

This paper introduces $C^3$-Bench, a comprehensive benchmark designed to evaluate large language model-based agents' robustness in multi-tasking environments, focusing on complex tool interactions, hidden information, and dynamic decision-making.

Contribution

The paper presents a new benchmark, $C^3$-Bench, with challenges and metrics to assess and analyze the robustness of LLM-based agents in complex, multi-tasking scenarios.

Findings

Agents struggle with tool dependency management

Long context handling is a significant challenge

Frequent policy switching indicates robustness issues

Abstract

Agents based on large language models leverage tools to modify environments, revolutionizing how AI interacts with the physical world. Unlike traditional NLP tasks that rely solely on historical dialogue for responses, these agents must consider more complex factors, such as inter-tool relationships, environmental feedback and previous decisions, when making choices. Current research typically evaluates agents via multi-turn dialogues. However, it overlooks the influence of these critical factors on agent behavior. To bridge this gap, we present an open-source and high-quality benchmark $C^3$-Bench. This benchmark integrates attack concepts and applies univariate analysis to pinpoint key elements affecting agent robustness. In concrete, we design three challenges: navigate complex tool relationships, handle critical hidden information and manage dynamic decision paths. Complementing these challenges, we introduce fine-grained metrics, innovative data collection algorithms and reproducible evaluation methods. Extensive experiments are conducted on 49 mainstream agents, encompassing general fast-thinking, slow-thinking and domain-specific models. We observe that agents have significant shortcomings in handling tool dependencies, long context information dependencies and frequent policy-type switching. In essence, $C^3$-Bench aims to expose model vulnerabilities through these challenges and drive research into the interpretability of agent performance. The benchmark is publicly available at https://github.com/TencentHunyuan/C3-Benchmark.