@inproceedings{cheng-etal-2026-tame,
title = "{TAME}: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking",
author = "Cheng, Yu and
Hu, Yongkang and
Zhou, Jiuan and
Zhang, Yushuo and
Chen, Yihang and
Zhou, Huichi and
Chen, Mingang and
Zhang, Zhizhong and
Shao, Kun and
Xie, Yuan and
Yin, Zhaoxia",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2100/",
pages = "47800--47818",
ISBN = "979-8-89176-459-0",
abstract = "Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable{---}a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we propose TAME, a trust-aware memory evolution framework in which a shared memory bank is jointly governed by an Executor and an Evaluator. The Executor retrieves and applies transferable experiences to support task solving, while the Evaluator assesses the contribution of each utilized experience to the outcome and produces trust-aware feedback to guide subsequent memory use. This executor{--}evaluator loop enables memory to be selectively reinforced, cautiously reused, and continuously expanded over time. Experiments show that TAME mitigates memory misevolution while achieving strong task performance. In particular, on the GPT-5.2 AIME benchmark, TAME improves accuracy by 14.6 percentage points over the strongest existing method and maintains competitive trustworthiness."
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<abstract>Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable—a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we propose TAME, a trust-aware memory evolution framework in which a shared memory bank is jointly governed by an Executor and an Evaluator. The Executor retrieves and applies transferable experiences to support task solving, while the Evaluator assesses the contribution of each utilized experience to the outcome and produces trust-aware feedback to guide subsequent memory use. This executor–evaluator loop enables memory to be selectively reinforced, cautiously reused, and continuously expanded over time. Experiments show that TAME mitigates memory misevolution while achieving strong task performance. In particular, on the GPT-5.2 AIME benchmark, TAME improves accuracy by 14.6 percentage points over the strongest existing method and maintains competitive trustworthiness.</abstract>
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%0 Conference Proceedings
%T TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking
%A Cheng, Yu
%A Hu, Yongkang
%A Zhou, Jiuan
%A Zhang, Yushuo
%A Chen, Yihang
%A Zhou, Huichi
%A Chen, Mingang
%A Zhang, Zhizhong
%A Shao, Kun
%A Xie, Yuan
%A Yin, Zhaoxia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F cheng-etal-2026-tame
%X Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable—a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we propose TAME, a trust-aware memory evolution framework in which a shared memory bank is jointly governed by an Executor and an Evaluator. The Executor retrieves and applies transferable experiences to support task solving, while the Evaluator assesses the contribution of each utilized experience to the outcome and produces trust-aware feedback to guide subsequent memory use. This executor–evaluator loop enables memory to be selectively reinforced, cautiously reused, and continuously expanded over time. Experiments show that TAME mitigates memory misevolution while achieving strong task performance. In particular, on the GPT-5.2 AIME benchmark, TAME improves accuracy by 14.6 percentage points over the strongest existing method and maintains competitive trustworthiness.
%U https://aclanthology.org/2026.emnlp-main.2100/
%P 47800-47818
Markdown (Informal)
[TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking](https://aclanthology.org/2026.emnlp-main.2100/) (Cheng et al., EMNLP 2026)
ACL
- Yu Cheng, Yongkang Hu, Jiuan Zhou, Yushuo Zhang, Yihang Chen, Huichi Zhou, Mingang Chen, Zhizhong Zhang, Kun Shao, Yuan Xie, and Zhaoxia Yin. 2026. TAME: A Trustworthy Test-Time Evolution of Agent Memory with Systematic Benchmarking. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 47800–47818, Budapest, Hungary. Association for Computational Linguistics.