MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. |
| Approach: | They propose a dataset and benchmark to evaluate the memory capability of LLM-based agents from multiple aspects including their effectiveness, efficiency, and capacity. |
| Outcome: | The proposed benchmark incorporates factual memory and reflective memory as different levels, and proposes participation and observation as various interactive scenarios. |
Similar Papers
A Survey on Evaluation of LLM-based Agents (2026.findings-acl)
Copied to clipboard
Asaf Yehudai, Lilach Eden, Alan Li, Guy Uziel, Yilun Zhao, Roy Bar-Haim, Arman Cohan, Michal Shmueli-Scheuer
| Challenge: | This paper provides the first comprehensive survey of evaluation methods for LLM-based agents . LLMs are static, having fixed knowledge, and confined to text-to-text interaction. |
| Approach: | They analyze the evaluation of LLM-based agents across five perspectives . they identify current trends and key gaps in evaluation methods . |
| Outcome: | The proposed evaluation frameworks and tools are based on five perspectives . the results highlight current trends and identify gaps in future research . |
Mem2ActBench: A Benchmark for Evaluating Long-Term Memory Utilization in Task-Oriented Autonomous Agents (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks focus on direct queries for a factual answer, but fail to evaluate the more crucial capability of actively applying memory to execute tasks. |
| Approach: | They propose a benchmark to evaluate whether agents can proactively leverage long-term memory to execute tool-based actions by selecting appropriate tools and grounding their parameters. |
| Outcome: | The proposed benchmarks show that 91.3% of tasks are memory-dependent . the benchmarks simulate persistent assistant usage, where users mention the same topic across long, interrupted interactions and expect previously established preferences and task states to be implicitly applied. |
How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026.acl-long)
Copied to clipboard
Zidi Xiong, Yuping Lin, Wenya Xie, Pengfei He, Zirui Liu, Jiliang Tang, Himabindu Lakkaraju, Zhen Xiang
| Challenge: | In practice, memory designs vary widely across agents due to their diverse objectives and functionalities. |
| Approach: | They conduct an empirical study on how memory management choices impact the LLM agents’ behavior, especially their long-term performance. |
| Outcome: | The proposed methods show that LLM agents display an experience-following property, which results in highly similar agent outputs. |
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms (2026.findings-acl)
Copied to clipboard
Jinghao Luo, Yuchen Tian, Chuxue Cao, Ziyang Luo, Hongzhan Lin, Kaixin Li, Chuyi Kong, Ruichao Yang, Jing Ma
| Challenge: | Large Language Models (LLMs)-based agents have fundamentally reshaped artificial intelligence . however, the inherent statelessness of LLMs hinders their ability to maintain logical consistency across complex, multi-step tasks . |
| Approach: | They propose a framework for LLM agent memory mechanisms that formalizes the development process into three stages: storage, reflection, and experience. |
| Outcome: | The proposed framework breaks the development process into three stages . it analyzes the need for long-range consistency, challenges in dynamic environments, and the ultimate goal of continual learning. |
An Evaluation Mechanism of LLM-based Agents on Manipulating APIs (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have remarkable capabilities across a variety of tasks, such as language, mathematics, coding, and etc. |
| Approach: | They propose to decompose tool use capability into seven aspects and form a thorough evaluation schema for generic agents. |
| Outcome: | The proposed agent acts like a super-APP and can manipulate API-based tools. |
Memp: Exploring Agent Procedural Memory (2026.findings-acl)
Copied to clipboard
Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters. |
| Approach: | They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions. |
| Outcome: | The proposed repository can be used to improve agents' performance on travelplanner and Alfworld. |
An Efficient Context-Dependent Memory Framework for LLM-Centric Agents (2025.naacl-industry)
Copied to clipboard
| Challenge: | a recent study has demonstrated that context-dependent memory encoding can help to retrieve key memory cues essential for problem-solving. |
| Approach: | They propose an efficient architecture miming human memory processes through multistage encoding, context-aware storage, and retrieval strategies for LLM-centric agents. |
| Outcome: | The proposed architecture surpasses state-of-the-art online LLM-centric approaches on two interactive decision-making benchmarks in the navigation and manipulation domain. |
Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)
Copied to clipboard
Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang
| Challenge: | Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities. |
| Approach: | They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion . |
| Outcome: | The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT . |
ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing memory benchmarks for LLMs evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavior without conscious retrieval. |
| Approach: | They propose a benchmark that evaluates implicit memory using three constructs from non-declarative memory. |
| Outcome: | The new benchmark reframes evaluation from "what agents recall" to "what they automatically enact" no model exceeds 66% overall, with top performers far below human baselines . |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
Copied to clipboard
Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |