基于大语言模型的智能体: 发展与未来展望
Integrity signals
Registry
Cites
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Suboptimal actions at individual steps can accumulate errors, ultimately leading to task failure.
“as suboptimal actions at individual steps can accumulate errors, ultimately leading to task failure [7].”
From Empowering Large Language Model Agent through Step-Level Self-Critique and Self-Training (2025) · cites this paper as [7]
Cited by
Sorted by citations
AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents
Wang, H., Poskitt, C. M., & Sun, J. (2026) · Proceedings of the 2026 IEEE/ACM 48th International Conference on Software Engineering · doi:10.1145/3744916.3764546 · Cited by 14 · see citing works
Fang, J., Peng, Y., Zhang, X., Wang, Y., Yi, X., Zhang, G., Xu, Y., Wu, B., Liu, S., Li, Z., et al. (2025) · arXiv · doi:10.48550/arxiv.2508.07407 · Cited by 6 · see citing works
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
Tan, H., Zhang, Z., Ma, C., Chen, X., Dai, Q., & Dong, Z. (2025) · Findings of the Association for Computational Linguistics: ACL 2025 · doi:10.18653/v1/2025.findings-acl.989 · Cited by 7 · see citing works
Empowering Large Language Model Agent through Step-Level Self-Critique and Self-Training
Zhai, Y., Liu, H., Zhang, Z., Lin, T., Xu, K., Yang, C., Feng, D., Ding, B., & Wang, H. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3729965 · Cited by 3 · see citing works
MAGMA: A Multi-Graph based Agentic Memory Architecture for AI Agents
Jiang, D., Li, Y., Li, G., & Li, B. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1709 · Cited by 2 · see citing works
Towards a Science of Scaling Agent Systems
Kim, Y., Gu, K., Park, C., Park, C., Schmidgall, S., Heydari, A. A., Yan, Y., Zhang, Z., Zhuang, Y., Liu, Y., et al. (2025) · arXiv · doi:10.48550/arxiv.2512.08296 · Cited by 1 · see citing works
BrowseComp-ZH: Benchmarking Web Browsing Ability of Large Language Models in Chinese
Zhou, P., Leon, B., Ying, X., Zhang, C., Shao, Y., Ye, Q., Chong, D., Jin, Z., Xie, C., Cao, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2504.19314
Accurate and Efficient Long-Term Memory for LLM Agents
Zhao, Z., Guo, X., Lv, L., Wang, M., Li, M., & Li, S. (2026) · arXiv · doi:10.48550/arxiv.2607.16211
Ning, X., Tieu, K., Fu, D., Wei, T., Li, Z., Bei, Y., Zou, J., Ai, M., Liu, Z., Li, T.-W., et al. (2026) · arXiv · doi:10.48550/arxiv.2605.18747
Shen, X., Liu, Y., Dai, Y., Wang, Y., Miao, R., Tan, Y., Pan, S., & Wang, X. (2025) · arXiv · doi:10.48550/arxiv.2505.23352 · Cited by 6 · see citing works
Memp: Exploring Agent Procedural Memory
Fang, R., Liang, Y., Wang, X., Wu, J., Qiao, S., Xie, P., Huang, F., Chen, H., & Zhang, N. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.866
EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory
Shen, Y., Pei, D., Guo, Y., Wang, J., Guo, Y., Zhang, Z., Jia, Q., Zhou, J., & Zhai, G. (2026) · arXiv · doi:10.48550/arxiv.2601.03543
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.06052
- Authors
- Zhiheng, X., Wenxiang, C., Xin, G., Wei, H., Yiwen, D., Boyang, H., Ming, Z., Junzhe, W., Senjie, J., Enyu, Z., Rui, Z., Xiaoran, F., Xiao, W., Limao, X., Yuhao, Z., Weiran, W., Changhao, J., Yicheng, Z., Xiangyang, L., Zhangyue, Y., Shihan, D., Rongxiang, W., Wenjuan, Q., Yongyan, Z., Xipeng, Q., Xuanjing, H., Qi, Z., & Tao, G.
- Venue
- SCIENTIA SINICA Informationis
- Publisher
- Science China Press., Co. Ltd.
- Published
- 2025-12-12
- Volume
- 56
- Issue
- 2
- Pages
- 485
- ISSN
- 1674-7267 (ISSN-L)
- OpenAlex
- W7114905576
- Cited by
- 1
- References
- 0
- Citations by year
- 2026: 1
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Scientia Sinica Informationis (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Keywords
- large language models, AI agents, agent development, future prospects
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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