The DOI matches its Crossref record.
Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
Open accessAbstract
Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, James Zou. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Many recent agentic-memory systems-CLIN (Majumder et al., 2023), Agent Workflow Memory (Wang et al., 2024), Dynamic Cheatsheet (Suzgun et al., 2026), and ACE (Zhang et al., 2025)-adopt update-after-every-interaction designs that treat each consolidation step as at worst neutral; our results contradict this assumption.”
From Useful Memories Become Faulty When Continuously Updated by LLMs (2026) · cites this paper as Suzgun et al., 2026
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Still being checked.
Research works printed but not linked: 4
- Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory https://aclanthology.org/2026.eacl-long.333/ (Suzgun et al., EACL 2026)
- Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory (Suzgun et al., EACL 2026)
- Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. 2026. Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7080–7106, Rabat, Morocco. Association for Computational Linguistics.
- ACL materials are Copyright © 1963–2026 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Cited by
Sorted by citations
Memory in the Age of AI Agents
Hu, Y., Liu, S., Yue, Y., Zhang, G., Liu, B., Zhu, F., Lin, J., Guo, H., Dou, S., Xi, Z., et al. (2025) · arXiv · doi:10.48550/arxiv.2512.13564 · Cited by 389
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Zhang, Q., Hu, C., Upasani, S., Ma, B., Hong, F., Kamanuru, V., Rainton, J., Wu, C., Ji, M., Li, H., et al. (2025) · arXiv · doi:10.48550/arxiv.2510.04618 · Cited by 370
Self-citationA 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 · Cited by 70
Self-citationVision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning
Yin, S., Ge, J., Wang, Z. Z., Wang, C., Li, X., Black, M. J., Darrell, T., Kanazawa, A., & Feng, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37464-6_30 · Cited by 52
MDTeamGPT: Mitigating Context Collapse and Enabling Self-Evolution in Medical Multi-Agent Reasoning
Chen, K., Li, X., Yang, T., Wang, H., Yang, G., Huo, J., & Gao, Y. (2026) · Findings of the Association for Computational Linguistics: ACL 2026 · doi:10.18653/v1/2026.findings-acl.1427 · Cited by 2
Metadata
- Authors
- Suzgun, M., Yuksekgonul, M., Bianchi, F., Jurafsky, D., & Zou, J.
- Venue
- Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2026
- Pages
- 7080-7106
- OpenAlex
- W7140107139
- Cited by
- 9
- References
- 0
- Citations by year
- 2026: 9
- FWCI
- 80.43
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the European Chapter of the Association for Computational Linguistics (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- adaptive memory, neural language models
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 ›
Are you an author, or do you see a problem with this page? Request a review
