AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications

Yujie Zhao, Boqin Yuan, Junbo Huang, Haocheng Yuan, Zhongming Yu, Haozhou Xu, Lanxiang Hu, Abhilash Shankarampeta, Zimeng Huang, Wentao Ni, Yuandong Tian, Jishen Zhao
Proceedings of the 43rd International Conference on Machine Learning, PMLR 306:162781-162809, 2026.

Abstract

Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long-horizon applications, where effective memory is critical for sustained performance. Yet existing memory benchmarks are largely dialogue-centric, while real agent memory consists of continuous agent-environment interaction trajectories composed of states, actions, observations, and tool outputs. To address this gap, we introduce AMA-Bench (Agent Memory with Any length), a benchmark for evaluating long-horizon memory in realistic agentic settings. AMA-Bench combines real-world agent trajectories from representative applications with expert-curated QA, as well as synthetic trajectories that scale to arbitrary horizons with rule-based QA. Our study shows that existing memory systems underperform because they fail to capture causal and objective information and rely heavily on lossy similarity-based retrieval. We further propose AMA-Agent, a memory system based on causality-graph construction and tool-augmented retrieval. AMA-Agent achieves 57.22% accuracy on AMA-Bench, outperforming the strongest baseline by 11.16%. Resources are available at: https://ama-bench.github.io/.

Cite this Paper

BibTeX
@InProceedings{pmlr-v306-zhao26bs, title = {{AMA}-Bench: Evaluating Long-Horizon Memory for Agentic Applications}, author = {Zhao, Yujie and Yuan, Boqin and Huang, Junbo and Yuan, Haocheng and Yu, Zhongming and Xu, Haozhou and Hu, Lanxiang and Shankarampeta, Abhilash and Huang, Zimeng and Ni, Wentao and Tian, Yuandong and Zhao, Jishen}, booktitle = {Proceedings of the 43rd International Conference on Machine Learning}, pages = {162781--162809}, year = {2026}, editor = {Zhang, Tong and Dudik, Miroslav and Jaggi, Martin and Agarwal, Alekh and Li, Sharon and Schuurmans, Dale and Zhu, Jerry and Berkenkamp, Felix and Dong, Hanze and Bietti, Alberto}, volume = {306}, series = {Proceedings of Machine Learning Research}, month = {06--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v306/main/assets/zhao26bs/zhao26bs.pdf}, url = {https://proceedings.mlr.press/v306/zhao26bs.html}, abstract = {Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long-horizon applications, where effective memory is critical for sustained performance. Yet existing memory benchmarks are largely dialogue-centric, while real agent memory consists of continuous agent-environment interaction trajectories composed of states, actions, observations, and tool outputs. To address this gap, we introduce AMA-Bench (Agent Memory with Any length), a benchmark for evaluating long-horizon memory in realistic agentic settings. AMA-Bench combines real-world agent trajectories from representative applications with expert-curated QA, as well as synthetic trajectories that scale to arbitrary horizons with rule-based QA. Our study shows that existing memory systems underperform because they fail to capture causal and objective information and rely heavily on lossy similarity-based retrieval. We further propose AMA-Agent, a memory system based on causality-graph construction and tool-augmented retrieval. AMA-Agent achieves 57.22% accuracy on AMA-Bench, outperforming the strongest baseline by 11.16%. Resources are available at: https://ama-bench.github.io/.} }
Endnote
%0 Conference Paper %T AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications %A Yujie Zhao %A Boqin Yuan %A Junbo Huang %A Haocheng Yuan %A Zhongming Yu %A Haozhou Xu %A Lanxiang Hu %A Abhilash Shankarampeta %A Zimeng Huang %A Wentao Ni %A Yuandong Tian %A Jishen Zhao %B Proceedings of the 43rd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2026 %E Tong Zhang %E Miroslav Dudik %E Martin Jaggi %E Alekh Agarwal %E Sharon Li %E Dale Schuurmans %E Jerry Zhu %E Felix Berkenkamp %E Hanze Dong %E Alberto Bietti %F pmlr-v306-zhao26bs %I PMLR %P 162781--162809 %U https://proceedings.mlr.press/v306/zhao26bs.html %V 306 %X Large Language Models (LLMs) are increasingly used as autonomous agents in complex, long-horizon applications, where effective memory is critical for sustained performance. Yet existing memory benchmarks are largely dialogue-centric, while real agent memory consists of continuous agent-environment interaction trajectories composed of states, actions, observations, and tool outputs. To address this gap, we introduce AMA-Bench (Agent Memory with Any length), a benchmark for evaluating long-horizon memory in realistic agentic settings. AMA-Bench combines real-world agent trajectories from representative applications with expert-curated QA, as well as synthetic trajectories that scale to arbitrary horizons with rule-based QA. Our study shows that existing memory systems underperform because they fail to capture causal and objective information and rely heavily on lossy similarity-based retrieval. We further propose AMA-Agent, a memory system based on causality-graph construction and tool-augmented retrieval. AMA-Agent achieves 57.22% accuracy on AMA-Bench, outperforming the strongest baseline by 11.16%. Resources are available at: https://ama-bench.github.io/.
APA
Zhao, Y., Yuan, B., Huang, J., Yuan, H., Yu, Z., Xu, H., Hu, L., Shankarampeta, A., Huang, Z., Ni, W., Tian, Y. & Zhao, J.. (2026). AMA-Bench: Evaluating Long-Horizon Memory for Agentic Applications. Proceedings of the 43rd International Conference on Machine Learning, in Proceedings of Machine Learning Research 306:162781-162809 Available from https://proceedings.mlr.press/v306/zhao26bs.html.

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