AgentCE-Bench: Agent Configurable Evaluation with Scalable Horizons and Controllable Difficulty under Lightweight Environments
AgentCE-Bench evaluates agents with scalable horizons and controllable difficulty using hidden slots H and decoy budget B.
Key Findings
Methodology
AgentCE-Bench is based on a unified grid-based planning task where agents fill hidden slots in a partially completed schedule, adhering to local and global constraints. It offers fine-grained control over task length and difficulty through hidden slots H and decoy budget B, with all tool calls resolved via static JSON files, eliminating setup overhead.
Key Results
- Experiments show that as hidden slots and decoy budgets increase, task scores significantly decrease, validating controllable difficulty.
- AgentCE-Bench demonstrated significant cross-model performance variation across 13 models of diverse sizes and families.
- In six domains, AgentCE-Bench provides interpretable and controllable evaluation of agent reasoning.
Significance
AgentCE-Bench addresses the high environment interaction overhead and imbalanced task difficulty distributions of existing benchmarks, offering a more reliable and reproducible method for agent evaluation through lightweight environment design and controllable difficulty settings.
Technical Contribution
AgentCE-Bench achieves controllable task length and difficulty in a lightweight environment, offering strong model discriminability and domain consistency, significantly reducing environment interaction overhead.
Novelty
This is the first framework to achieve controllable task length and difficulty through hidden slots and decoy budgets, addressing inefficiencies in existing evaluation methods.
Limitations
- In complex tasks, the setup of hidden slots and decoy budgets may lead to uncertainty in evaluation results.
- Model performance may be limited by domain-specific constraints.
Future Work
Future research could explore more complex task settings and broader application domains to further validate the applicability and extensibility of AgentCE-Bench.
AI Executive Summary
Current agent benchmarks face issues of high environment interaction overhead and imbalanced task difficulty distributions, making aggregate scores unreliable. To address these issues, researchers have introduced AgentCE-Bench, a framework based on a unified grid-based planning task. Agents must fill hidden slots in a partially completed schedule, adhering to local and global constraints. By controlling the number of hidden slots H and the decoy budget B, AgentCE-Bench achieves fine-grained control over task length and difficulty.
Experimental results show that AgentCE-Bench demonstrates significant cross-model performance variation across 13 models of diverse sizes and families, validating its interpretability and controllability in agent reasoning evaluation. Compared to existing benchmarks, AgentCE-Bench achieves lower environment interaction overhead in a lightweight environment, offering strong model discriminability and domain consistency.
Although AgentCE-Bench has made significant advances in agent evaluation, the setup of hidden slots and decoy budgets in complex tasks may lead to uncertainty in evaluation results. Future research could explore more complex task settings and broader application domains to further validate the applicability and extensibility of AgentCE-Bench.
Deep Analysis
Background
Agent evaluation has made significant advances in recent years, with benchmarks like WebArena and TAU2-Bench providing standardized environments to evaluate agent capabilities. However, these benchmarks often require complex environment setups, leading to high computational resource consumption, and the difficulty and length distributions of tasks are uneven, affecting the reliability of evaluations.
Core Problem
The core problem of existing agent benchmarks is high environment interaction overhead and imbalanced task difficulty distributions. This leads to unreliable aggregate scores, making it difficult to accurately reflect the true capabilities of models.
Innovation
AgentCE-Bench achieves controllable task length and difficulty through hidden slots and decoy budgets. Hidden slots H control the task length, while the decoy budget B controls the task difficulty. This design eliminates the overhead of environment setup and provides fast, reproducible evaluation.
Methodology
- �� Task Setup: Based on grid-based planning tasks, agents fill hidden slots.
- �� Difficulty Control: Set global misleading candidates through decoy budget B.
- �� Lightweight Environment: All tool calls are resolved via static JSON files.
Experiments
Experiments involved 13 models of diverse sizes and families across six domains. By controlling hidden slots and decoy budgets, the controllability of task length and difficulty in AgentCE-Bench was validated.
Results
Results show that as hidden slots and decoy budgets increase, task scores significantly decrease, validating controllable difficulty. AgentCE-Bench shows strong cross-model and cross-domain performance variation.
Applications
AgentCE-Bench can be used for training-time validation and model selection, suitable for scenarios requiring fast, reproducible evaluation.
Limitations & Outlook
In complex tasks, the setup of hidden slots and decoy budgets may lead to uncertainty in evaluation results. Future research should explore more complex task settings.
Plain Language Accessible to non-experts
Imagine playing a crossword puzzle where each blank has specific rules, like must be a certain letter or can't exceed a certain length. AgentCE-Bench is like this game; it tests your ability to fill the blanks by setting different numbers of blanks and rule complexity. By adjusting these parameters, we can see the performance differences among different players, helping us understand who has the advantage in this game.
ELI14 Explained like you're 14
Imagine you're at school in a crossword competition where each blank has specific rules, like must be a certain letter or can't exceed a certain length. AgentCE-Bench is like this competition; it tests your ability to fill the blanks by setting different numbers of blanks and rule complexity. By adjusting these parameters, we can see the performance differences among different contestants, helping us understand who has the advantage in this competition.
Glossary
AgentCE-Bench
A framework for agent evaluation that achieves controllable task length and difficulty through hidden slots and decoy budgets.
Used for evaluating agent reasoning capabilities.
Hidden Slot
Slots in a task that agents need to fill, with the number determining task length.
Used to control task length.
Decoy Budget
A budget determining the number of globally misleading candidates, used to control task difficulty.
Used to set task difficulty.
Lightweight Environment
An evaluation environment that requires no complex setup, with all tool calls resolved via static JSON files.
Used to reduce environment interaction overhead.
Model Discriminability
The ability of an evaluation framework to distinguish the capabilities of different models.
Used to validate the effectiveness of the framework.
Open Questions Unanswered questions from this research
- 1 How to maintain stability in evaluation results for more complex task settings?
- 2 What is the applicability of AgentCE-Bench across different domains?
Applications
Immediate Applications
Agent Training Validation
Quickly validate agent reasoning capabilities during training, reducing computational resource consumption.
Long-term Vision
Cross-Domain Agent Evaluation
Apply AgentCE-Bench in more domains to validate its applicability and extensibility under different task settings.
Abstract
Existing Agent benchmarks suffer from two critical limitations: high environment interaction overhead (up to 41\% of total evaluation time) and imbalanced task horizon and difficulty distributions that make aggregate scores unreliable. To address these issues, we propose AgentCE-Bench built around a unified grid-based planning task, where agents must fill hidden slots in a partially completed schedule subject to both local slot constraints and global constraints. Our benchmark offers fine-grained control through two orthogonal axes: \textbf{Scalable Horizons}, controlled by the number of hidden slots $H$, and \textbf{Controllable Difficulty}, governed by a decoy budget $B$ that determines the number of globally misleading decoy candidates. Crucially, all tool calls are resolved via static JSON files under a \textbf{Lightweight Environment} design, eliminating setup overhead and enabling fast, reproducible evaluation suitable for training-time validation. We first validate that $H$ and $B$ provide reliable control over task horizon and difficulty, and that AgentCE-Bench exhibits strong domain consistency and model discriminability. We then conduct comprehensive experiments across 13 models of diverse sizes and families over 6 domains, revealing significant cross-model performance variation and confirming that AgentCE-Bench provides interpretable and controllable evaluation of agent reasoning.