arXivLens breakdown of this paper ๐ https://arxivlens.com/PaperView/Details/agentlongbench-a-controllable-long-benchmark-for-long-contexts-agents-via-environment-rollouts-5957-1c8a02a2
\n- \n
- Executive Summary \n
- Detailed Breakdown \n
- Practical Applications \n
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arXiv explained breakdown of this paper ๐ https://arxivexplained.com/papers/agentlongbench-a-controllable-long-benchmark-for-long-contexts-agents-via-environment-rollouts
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Abstract
AgentLongBench evaluates large language models as autonomous agents through dynamic environment interactions, revealing challenges in handling high-information-density tool responses compared to memory fragmentation in long conversations.
The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static, relying on passive retrieval tasks that fail to simulate the complexities of agent-environment interaction, such as non-linear reasoning and iterative feedback. To address this, we introduce AgentLongBench, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles. This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios. Experiments with state-of-the-art models and memory systems (32K to 4M tokens) expose a critical weakness: while adept at static retrieval, agents struggle with the dynamic information synthesis essential for workflows. Our analysis indicates that this degradation is driven by the minimum number of tokens required to resolve a query. This factor explains why the high information density inherent in massive tool responses poses a significantly greater challenge than the memory fragmentation typical of long-turn dialogues.
Community
The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static, relying on passive retrieval tasks that fail to simulate the complexities of agent-environment interaction, such as non-linear reasoning and iterative feedback. To address this, we introduce \textbf{AgentLongBench}, which evaluates agents through simulated environment rollouts based on Lateral Thinking Puzzles. This framework generates rigorous interaction trajectories across knowledge-intensive and knowledge-free scenarios. Experiments with state-of-the-art models and memory systems (32K to 4M tokens) expose a critical weakness: while adept at static retrieval, agents struggle with the dynamic information synthesis essential for workflows. Our analysis indicates that this degradation is driven by the minimum number of tokens required to resolve a query. This factor explains why the high information density inherent in massive tool responses poses a significantly greater challenge than the memory fragmentation typical of long-turn dialogues. The code is available at https://github.com/euReKa025/AgentLongBench.
arXivLens breakdown of this paper ๐ https://arxivlens.com/PaperView/Details/agentlongbench-a-controllable-long-benchmark-for-long-contexts-agents-via-environment-rollouts-5957-1c8a02a2
- Executive Summary
- Detailed Breakdown
- Practical Applications
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
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- Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026)
- AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026)
- NL2Repo-Bench: Towards Long-Horizon Repository Generation Evaluation of Coding Agents (2025)
- IDRBench: Interactive Deep Research Benchmark (2026)
- ToolGym: an Open-world Tool-using Environment for Scalable Agent Testing and Data Curation (2026)
- Jenius Agent: Towards Experience-Driven Accuracy Optimization in Real-World Scenarios (2026)
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arXiv explained breakdown of this paper ๐ https://arxivexplained.com/papers/agentlongbench-a-controllable-long-benchmark-for-long-contexts-agents-via-environment-rollouts
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