---
title: "Agentic Context Engineering — paper by Zhang et al. (2025)"
description: "They propose ACE (Agentic Context Engineering), which treats the context as a… 66.7 accuracy. 70 references, cited by 36 papers here."
authors: ["Qizheng Zhang", "Changran Hu", "Shubhangi Upasani", "Boyuan Ma", "Fenglu Hong", "Vamsidhar Kamanuru", "Jay Rainton", "Chen Wu", "Mengmeng Ji", "Hanchen Li", "Urmish Thakker", "James Zou", "Kunle Olukotun"]
year: 2025
venue: "arXiv:2510.04618"
source: "https://arxiv.org/abs/2510.04618"
doi: "10.48550/arxiv.2510.04618"
arxiv: "2510.04618"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2025-agentic-context-engineering-evolving-contexts/"
---
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# Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

**Authors:** Qizheng Zhang, [Changran Hu](https://md.research.elamur.ai/free/en/authors/changran-hu/), Shubhangi Upasani, Boyuan Ma, [Fenglu Hong](https://md.research.elamur.ai/free/en/authors/fenglu-hong/), Vamsidhar Kamanuru, Jay Rainton, [Chen Wu](https://md.research.elamur.ai/free/en/authors/chen-wu/), Mengmeng Ji, Hanchen Li, [Urmish Thakker](https://md.research.elamur.ai/free/en/authors/urmish-thakker/), [James Zou](https://md.research.elamur.ai/free/en/authors/james-zou/) and Kunle Olukotun · **Year:** 2025 · **Venue:** arXiv:2510.04618 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2510.04618) · [PDF](https://arxiv.org/pdf/2510.04618) · [DOI 10.48550/arxiv.2510.04618](https://doi.org/10.48550/arxiv.2510.04618) · [arXiv 2510.04618](https://arxiv.org/abs/2510.04618) · [OpenAlex](https://openalex.org/W4414971614)

**Citations:** 3 citations (OpenAlex, 2026-10-14) · [36](https://md.research.elamur.ai/free/en/papers/zhang-2025-agentic-context-engineering-evolving-contexts/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLM systems such as agents and domain-specific reasoners often improve by changing their input context rather than their weights. The authors identify two weaknesses in existing context-adaptation methods. The first is a bias toward short, generic prompts that discard useful domain detail. The second is "context collapse," where repeated full rewrites by an LLM shrink an accumulated context and erase what it had learned. They propose ACE (Agentic Context Engineering), which treats the context as a growing playbook maintained by three roles. A Generator attempts tasks, a Reflector extracts lessons from successes and failures, and a Curator turns those lessons into small itemized delta entries. Simple non-LLM logic merges these entries, and a grow-and-refine step limits redundancy. ACE works offline (system prompt optimization) and online (agent memory). It can learn from execution feedback without labels. On agent and finance benchmarks it beats strong baselines while needing fewer rollouts and less adaptation latency. This suggests that detailed, evolving contexts are a cheap route to self-improving LLM systems.

## Key points
- Average gains over baselines are +10.6% on agent tasks and +8.6% on domain-specific (financial) benchmarks. Evaluation covers AppWorld, FiNER and Formula, with comparisons against ICL, GEPA and Dynamic Cheatsheet (DC).
- On the AppWorld leaderboard, ACE with open-source DeepSeek-V3.1 ties the top production agent IBM-CUGA (GPT-4.1) on the overall average. It outperforms IBM-CUGA on the harder test-challenge split.
- The paper gives a context collapse example from AppWorld with Dynamic Cheatsheet. At step 60, an 18,282-token context reached 66.7 accuracy. One step later it was rewritten down to 122 tokens and scored 57.1, below the 63.7 achieved with no adaptation.
- ACE has three core design elements:
  - a separate Reflector role;
  - incremental delta updates instead of monolithic rewrites;
  - a grow-and-refine mechanism.
- Code is released as ace-agent/ace (ace-agent.github.io). The appendix includes Generator, Reflector and Curator prompts for FiNER.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou and Kunle Olukotun. 2025. Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models. arXiv:2510.04618.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zhang2025agentic,
  title = {{Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models}},
  author = {Qizheng Zhang, Changran Hu, Shubhangi Upasani, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Jay Rainton, Chen Wu, Mengmeng Ji, Hanchen Li, Urmish Thakker, James Zou and Kunle Olukotun},
  year = {2025},
  doi = {10.48550/arxiv.2510.04618},
  eprint = {2510.04618},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2510.04618}
}
```

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## References

70 references, 25 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. General Data Protection Regulation (GDPR) Article 17: Right to Erasure. EU Regulation 2016/679, 2016. [find](https://scholar.google.com/scholar?q=General+Data+Protection+Regulation+%28GDPR%29+Article+17%3A+Right+to+Erasure%2E+EU+Regulation+2016%2F679%2C+2016%2E)
2. California Consumer Privacy Act, Civil Code §1798.105: Right to Delete. State of California Civil Code, 2018. [find](https://scholar.google.com/scholar?q=California+Consumer+Privacy+Act%2C+Civil+Code+%C2%A71798%2E105%3A+Right+to+Delete%2E+State+of+California+Civil+Code%2C+2018%2E)
3. Rishabh Agarwal, Avi Singh, Lei Zhang, Bernd Bohnet, Luis Rosias, Stephanie Chan, Biao Zhang, Ankesh Anand, Zaheer Abbas, Azade Nova, et al. [Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/). Advances in Neural Information Processing Systems (NeurIPS), 37:76930–76966, 2024.
4. Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, et al. GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning. arXiv preprint arXiv:2507.19457, 2025. [find](https://scholar.google.com/scholar?q=Lakshya+A+Agrawal%2C+Shangyin+Tan%2C+Dilara+Soylu%2C+Noah+Ziems%2C+Rishi+Khare%2C+Krista+Opsahl%2DOng%2C+Arnav+Singhvi%2C+Herumb+Shandilya%2C+Michael+J+Ryan%2C+Meng+Jiang%2C+et+al%2E+GEPA%3A+Reflective+Prompt+Evolution+Can+Out)
5. AppWorld. Leaderboard. [https://appworld.dev/leaderboard](https://appworld.dev/leaderboard). Accessed: 09/2025. [↗](https://appworld.dev/leaderboard)
6. Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/). In The Twelfth International Conference on Learning Representations (ICLR), 2024.
7. Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. Improving Language Models by Retrieving from Trillions of Tokens. In International Conference on Machine Learning (I… [find](https://scholar.google.com/scholar?q=Sebastian+Borgeaud%2C+Arthur+Mensch%2C+Jordan+Hoffmann%2C+Trevor+Cai%2C+Eliza+Rutherford%2C+Katie+Millican%2C+George+Bm+Van+Den+Driessche%2C+Jean%2DBaptiste+Lespiau%2C+Bogdan+Damoc%2C+Aidan+Clark%2C+et+al%2E+Improving+Langua)
8. Lucas Bourtoule, Varun Chandrasekaran, Christopher Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. Machine Unlearning. In IEEE Symposium on Security and Privacy (IEEE S&P), 2021. [find](https://scholar.google.com/scholar?q=Lucas+Bourtoule%2C+Varun+Chandrasekaran%2C+Christopher+Choquette%2DChoo%2C+Hengrui+Jia%2C+Adelin+Travers%2C+Baiwu+Zhang%2C+David+Lie%2C+and+Nicolas+Papernot%2E+Machine+Unlearning%2E+In+IEEE+Symposium+on+Security+and+Priv)
9. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. [Language Models are Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in Neural Information Processing Systems (NeurIPS), 33:1877– 1901, 2020.
10. Yinzhi Cao and Junfeng Yang. Towards Making Systems Forget with Machine Unlearning. In IEEE Symposium on Security and Privacy (IEEE S&P), 2015. [find](https://scholar.google.com/scholar?q=Yinzhi+Cao+and+Junfeng+Yang%2E+Towards+Making+Systems+Forget+with+Machine+Unlearning%2E+In+IEEE+Symposium+on+Security+and+Privacy+%28IEEE+S%26P%29%2C+2015%2E)
11. Tianxiang Chen, Zhentao Tan, Xiaofan Bo, Yue Wu, Tao Gong, Qi Chu, Jieping Ye, and Nenghai Yu. Flora: Effortless Context Construction to Arbitrary Length and Scale. arXiv preprint arXiv:2507.19786, 2025. — [arXiv:2507.19786](https://arxiv.org/abs/2507.19786) [find](https://scholar.google.com/scholar?q=Tianxiang+Chen%2C+Zhentao+Tan%2C+Xiaofan+Bo%2C+Yue+Wu%2C+Tao+Gong%2C+Qi+Chu%2C+Jieping+Ye%2C+and+Nenghai+Yu%2E+Flora%3A+Effortless+Context+Construction+to+Arbitrary+Length+and+Scale%2E+arXiv+preprint+arXiv%3A2507%2E19786%2C+20)
12. Yeounoh Chung, Gaurav T Kakkar, Yu Gan, Brenton Milne, and Fatma Özcan. Is Long Context All You Need? Leveraging LLM’s Extended Context for NL2SQL. Proceedings of the VLDB Endowment, 18(8):2735–2747, 2025. [find](https://scholar.google.com/scholar?q=Yeounoh+Chung%2C+Gaurav+T+Kakkar%2C+Yu+Gan%2C+Brenton+Milne%2C+and+Fatma+O%CC%88zcan%2E+Is+Long+Context+All+You+Need%3F+Leveraging+LLM%E2%80%99s+Extended+Context+for+NL2SQL%2E+Proceedings+of+the+VLDB+Endowment%2C+18%288%29%3A2735%E2%80%932747%2C)
13. DSPy. dspy.GEPA: Reflective Prompt Optimizer. [https://dspy.ai/api/optimizers/](https://dspy.ai/api/optimizers/) GEPA/overview/, a. Accessed: 09/2025. [↗](https://dspy.ai/api/optimizers/)
14. DSPy. dspy.MIPROv2: Multiprompt Instruction PRoposal Optimizer Version 2. [https://dspy.ai/api/optimizers/MIPROv2/](https://dspy.ai/api/optimizers/MIPROv2/), b. Accessed: 09/2025. [↗](https://dspy.ai/api/optimizers/MIPROv2/)
15. Arsene Fansi Tchango, Rishab Goel, Zhi Wen, Julien Martel, and Joumana Ghosn. DDXPlus: A New Dataset For Automatic Medical Diagnosis. Advances in neural information processing systems (NeurIPS), 2022. [find](https://scholar.google.com/scholar?q=Arsene+Fansi+Tchango%2C+Rishab+Goel%2C+Zhi+Wen%2C+Julien+Martel%2C+and+Joumana+Ghosn%2E+DDXPlus%3A+A+New+Dataset+For+Automatic+Medical+Diagnosis%2E+Advances+in+neural+information+processing+systems+%28NeurIPS%29%2C+2022%2E)
16. Shuzheng Gao, Chaozheng Wang, Cuiyun Gao, Xiaoqian Jiao, Chun Yong Chong, Shan Gao, and Michael Lyu. The Prompt Alchemist: Automated LLM-Tailored Prompt Optimization for Test Case Generation. arXiv preprint arXiv:2501.01329, 2025. — [arXiv:2501.01329](https://arxiv.org/abs/2501.01329) [find](https://scholar.google.com/scholar?q=Shuzheng+Gao%2C+Chaozheng+Wang%2C+Cuiyun+Gao%2C+Xiaoqian+Jiao%2C+Chun+Yong+Chong%2C+Shan+Gao%2C+and+Michael+Lyu%2E+The+Prompt+Alchemist%3A+Automated+LLM%2DTailored+Prompt+Optimization+for+Test+Case+Generation%2E+arXiv+pr)
17. In Gim, Guojun Chen, Seung-seob Lee, Nikhil Sarda, Anurag Khandelwal, and Lin Zhong. [Prompt Cache: Modular Attention Reuse for Low-Latency Inference](https://md.research.elamur.ai/free/en/papers/gim-2023-prompt-cache-modular-attention-reuse/). Proceedings of Machine Learning and Systems (MLSys), 6:325–338, 2024.
18. Neel Guha, Julian Nyarko, Daniel Ho, Christopher Ré, Adam Chilton, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel Rockmore, Diego Zambrano, et al. [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/). Advances in neural information processin…
19. Ishaan Gulrajani and David Lopez-Paz. In Search of Lost Domain Generalization. In International Conference on Learning Representations (ICLR), 2021. [find](https://scholar.google.com/scholar?q=Ishaan+Gulrajani+and+David+Lopez%2DPaz%2E+In+Search+of+Lost+Domain+Generalization%2E+In+International+Conference+on+Learning+Representations+%28ICLR%29%2C+2021%2E)
20. Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. [LoRA: Low-Rank Adaptation of Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/). In The Tenth International Conference on Learning Representations (ICLR), 2022.
21. Maxwell L Hutchinson, Erin Antono, Brenna M Gibbons, Sean Paradiso, Julia Ling, and Bryce Meredig. Overcoming Data Scarcity with Transfer Learning. arXiv preprint arXiv:1711.05099, 2017. — [arXiv:1711.05099](https://arxiv.org/abs/1711.05099) [find](https://scholar.google.com/scholar?q=Maxwell+L+Hutchinson%2C+Erin+Antono%2C+Brenna+M+Gibbons%2C+Sean+Paradiso%2C+Julia+Ling%2C+and+Bryce+Meredig%2E+Overcoming+Data+Scarcity+with+Transfer+Learning%2E+arXiv+preprint+arXiv%3A1711%2E05099%2C+2017%2E+%E2%80%94+%5BarXiv%3A1711)
22. Mingjian Jiang, Yangjun Ruan, Luis Lastras, Pavan Kapanipathi, and Tatsunori Hashimoto. Putting It All into Context: Simplifying Agents with LCLMs. arXiv preprint arXiv:2505.08120, 2025. — [arXiv:2505.08120](https://arxiv.org/abs/2505.08120) [find](https://scholar.google.com/scholar?q=Mingjian+Jiang%2C+Yangjun+Ruan%2C+Luis+Lastras%2C+Pavan+Kapanipathi%2C+and+Tatsunori+Hashimoto%2E+Putting+It+All+into+Context%3A+Simplifying+Agents+with+LCLMs%2E+arXiv+preprint+arXiv%3A2505%2E08120%2C+2025%2E+%E2%80%94+%5BarXiv%3A2505)
23. Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://md.research.elamur.ai/free/en/papers/khot-2022-decomposed-prompting-modular-approach/). In International Conference on Learning Representations (ICLR), 2023.
24. Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al. WILDS: A Benchmark of in-the-Wild Distribution Shifts. In International conference on machine learning (ICML), 2021. [find](https://scholar.google.com/scholar?q=Pang+Wei+Koh%2C+Shiori+Sagawa%2C+Henrik+Marklund%2C+Sang+Michael+Xie%2C+Marvin+Zhang%2C+Akshay+Balsubramani%2C+Weihua+Hu%2C+Michihiro+Yasunaga%2C+Richard+Lanas+Phillips%2C+Irena+Gao%2C+et+al%2E+WILDS%3A+A+Benchmark+of+in%2Dthe)
25. Ben Krause, Emmanuel Kahembwe, Iain Murray, and Steve Renals. Dynamic Evaluation of Transformer Language Models. arXiv preprint arXiv:1904.08378, 2019. — [arXiv:1904.08378](https://arxiv.org/abs/1904.08378) [find](https://scholar.google.com/scholar?q=Ben+Krause%2C+Emmanuel+Kahembwe%2C+Iain+Murray%2C+and+Steve+Renals%2E+Dynamic+Evaluation+of+Transformer+Language+Models%2E+arXiv+preprint+arXiv%3A1904%2E08378%2C+2019%2E+%E2%80%94+%5BarXiv%3A1904%2E08378%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1904%2E)
26. Wonbeom Lee, Jungi Lee, Junghwan Seo, and Jaewoong Sim. InfiniGen: Efficient Generative Inference of Large Language Models with Dynamic KV Cache Management. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI 24), pp. 155–172, 2024. [find](https://scholar.google.com/scholar?q=Wonbeom+Lee%2C+Jungi+Lee%2C+Junghwan+Seo%2C+and+Jaewoong+Sim%2E+InfiniGen%3A+Efficient+Generative+Inference+of+Large+Language+Models+with+Dynamic+KV+Cache+Management%2E+In+18th+USENIX+Symposium+on+Operating+Syste)
27. Brian Lester, Rami Al-Rfou, and Noah Constant. [The Power of Scale for Parameter-Efficient Prompt Tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/). In Proceedings of the 2021 conference on empirical methods in natural language processing (EMNLP), pp. 3045–3059, 2021.
28. Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/). Advances in neural information processing systems (NeurIPS), 2020.
29. Hanchen Li, Qiuyang Mang, Runyuan He, Qizheng Zhang, Huanzhi Mao, Xiaokun Chen, Hangrui Zhou, Alvin Cheung, Joseph Gonzalez, and Ion Stoica. Continuum: Efficient and Robust Multi-Turn LLM Agent Scheduling with KV Cache Time-to-Live. arXiv preprint arXiv:2511.02230, 2025. [find](https://scholar.google.com/scholar?q=Hanchen+Li%2C+Qiuyang+Mang%2C+Runyuan+He%2C+Qizheng+Zhang%2C+Huanzhi+Mao%2C+Xiaokun+Chen%2C+Hangrui+Zhou%2C+Alvin+Cheung%2C+Joseph+Gonzalez%2C+and+Ion+Stoica%2E+Continuum%3A+Efficient+and+Robust+Multi%2DTurn+LLM+Agent+Schedu)
30. Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, et al. [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://md.research.elamur.ai/free/en/papers/li-2023-llm-already-serve-database-interface/). Advances in Neural Information Processing Systems (NeurIPS), 2023.
31. Xiang Lisa Li and Percy Liang. Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 4582–4597, 20… [find](https://scholar.google.com/scholar?q=Xiang+Lisa+Li+and+Percy+Liang%2E+Prefix%2DTuning%3A+Optimizing+Continuous+Prompts+for+Generation%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+11th+Inte)
32. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, et al. DeepSeek-V3 Technical Report. arXiv preprint arXiv:2412.19437, 2024a. [find](https://scholar.google.com/scholar?q=Aixin+Liu%2C+Bei+Feng%2C+Bing+Xue%2C+Bingxuan+Wang%2C+Bochao+Wu%2C+Chengda+Lu%2C+Chenggang+Zhao%2C+Chengqi+Deng%2C+Chenyu+Zhang%2C+Chong+Ruan%2C+et+al%2E+DeepSeek%2DV3+Technical+Report%2E+arXiv+preprint+arXiv%3A2412%2E19437%2C+2024a)
33. Sijia Liu, Yuanshun Yao, Jinghan Jia, Stephen Casper, Nathalie Baracaldo, Peter Hase, Yuguang Yao, Chris Yuhao Liu, Xiaojun Xu, Hang Li, et al. Rethinking Machine Unlearning for Large Language Models. Nature Machine Intelligence, 7(2):181–194, 2025a. [find](https://scholar.google.com/scholar?q=Sijia+Liu%2C+Yuanshun+Yao%2C+Jinghan+Jia%2C+Stephen+Casper%2C+Nathalie+Baracaldo%2C+Peter+Hase%2C+Yuguang+Yao%2C+Chris+Yuhao+Liu%2C+Xiaojun+Xu%2C+Hang+Li%2C+et+al%2E+Rethinking+Machine+Unlearning+for+Large+Language+Models%2E)
34. Yuhan Liu, Hanchen Li, Yihua Cheng, Siddhant Ray, Yuyang Huang, Qizheng Zhang, Kuntai Du, Jiayi Yao, Shan Lu, Ganesh Ananthanarayanan, et al. CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving. In Proceedings of the ACM SIGCOMM 2024 Conference, pp. 38–56, 2024b. [find](https://scholar.google.com/scholar?q=Yuhan+Liu%2C+Hanchen+Li%2C+Yihua+Cheng%2C+Siddhant+Ray%2C+Yuyang+Huang%2C+Qizheng+Zhang%2C+Kuntai+Du%2C+Jiayi+Yao%2C+Shan+Lu%2C+Ganesh+Ananthanarayanan%2C+et+al%2E+CacheGen%3A+KV+Cache+Compression+and+Streaming+for+Fast+Larg)
35. Zhining Liu, Rana Ali Amjad, Ravinarayana Adkathimar, Tianxin Wei, and Hanghang Tong. Self-Elicit: Your Language Model Secretly Knows Where is the Relevant Evidence. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 9153–9173, 202… [find](https://scholar.google.com/scholar?q=Zhining+Liu%2C+Rana+Ali+Amjad%2C+Ravinarayana+Adkathimar%2C+Tianxin+Wei%2C+and+Hanghang+Tong%2E+Self%2DElicit%3A+Your+Language+Model+Secretly+Knows+Where+is+the+Relevant+Evidence%2E+In+Proceedings+of+the+63rd+Annual+)
36. Zirui Liu, Jiayi Yuan, Hongye Jin, Shaochen Zhong, Zhaozhuo Xu, Vladimir Braverman, Beidi Chen, and Xia Hu. [KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache](https://md.research.elamur.ai/free/en/papers/liu-2024-kivi-tuning-free-asymmetric-2bit/). In International Conference on Machine Learning (ICML), 2024c.
37. Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and Georgios Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volu… [find](https://scholar.google.com/scholar?q=Lefteris+Loukas%2C+Manos+Fergadiotis%2C+Ilias+Chalkidis%2C+Eirini+Spyropoulou%2C+Prodromos+Malakasiotis%2C+Ion+Androutsopoulos%2C+and+Georgios+Paliouras%2E+FiNER%3A+Financial+Numeric+Entity+Recognition+for+XBRL+Taggi)
38. Qiuyang Mang, Wenhao Chai, Zhifei Li, Huanzhi Mao, Shang Zhou, Alexander Du, Hanchen Li, Shu Liu, Edwin Chen, Yichuan Wang, et al. FrontierCS: Evolving Challenges for Evolving Intelligence. arXiv preprint arXiv:2512.15699, 2025. — [arXiv:2512.15699](https://arxiv.org/abs/2512.15699) [find](https://scholar.google.com/scholar?q=Qiuyang+Mang%2C+Wenhao+Chai%2C+Zhifei+Li%2C+Huanzhi+Mao%2C+Shang+Zhou%2C+Alexander+Du%2C+Hanchen+Li%2C+Shu+Liu%2C+Edwin+Chen%2C+Yichuan+Wang%2C+et+al%2E+FrontierCS%3A+Evolving+Challenges+for+Evolving+Intelligence%2E+arXiv+prep)
39. Yansheng Mao, Jiaqi Li, Fanxu Meng, Jing Xiong, Zilong Zheng, and Muhan Zhang. LIFT: Improving Long Context Understanding Through Long Input Fine-Tuning. arXiv preprint arXiv:2412.13626, 2024. — [arXiv:2412.13626](https://arxiv.org/abs/2412.13626) [find](https://scholar.google.com/scholar?q=Yansheng+Mao%2C+Jiaqi+Li%2C+Fanxu+Meng%2C+Jing+Xiong%2C+Zilong+Zheng%2C+and+Muhan+Zhang%2E+LIFT%3A+Improving+Long+Context+Understanding+Through+Long+Input+Fine%2DTuning%2E+arXiv+preprint+arXiv%3A2412%2E13626%2C+2024%2E+%E2%80%94+%5BarXi)
40. Sami Marreed, Alon Oved, Avi Yaeli, Segev Shlomov, Ido Levy, Offer Akrabi, Aviad Sela, Asaf Adi, and Nir Mashkif. Towards Enterprise-Ready Computer Using Generalist Agent. arXiv preprint arXiv:2503.01861, 2025. — [arXiv:2503.01861](https://arxiv.org/abs/2503.01861) [find](https://scholar.google.com/scholar?q=Sami+Marreed%2C+Alon+Oved%2C+Avi+Yaeli%2C+Segev+Shlomov%2C+Ido+Levy%2C+Offer+Akrabi%2C+Aviad+Sela%2C+Asaf+Adi%2C+and+Nir+Mashkif%2E+Towards+Enterprise%2DReady+Computer+Using+Generalist+Agent%2E+arXiv+preprint+arXiv%3A2503%2E01)
41. OpenAI. GPT-4o mini: Advancing Cost-Efficient Intelligence. [https://openai.com/](https://openai.com/) index/gpt-4o-mini-advancing-cost-efficient-intelligence/, 2024. [↗](https://openai.com/)
42. Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, David Broman, Christopher Potts, Matei Zaharia, and Omar Khattab. [Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://md.research.elamur.ai/free/en/papers/ong-2024-optimizing-instructions-demonstrations-multi-stage/). In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp…
43. Sinno Jialin Pan and Qiang Yang. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, 2010. [find](https://scholar.google.com/scholar?q=Sinno+Jialin+Pan+and+Qiang+Yang%2E+A+Survey+on+Transfer+Learning%2E+IEEE+Transactions+on+Knowledge+and+Data+Engineering%2C+22%2810%29%3A1345%E2%80%931359%2C+2010%2E)
44. Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. [Gorilla: Large Language Model Connected with Massive APIs](https://md.research.elamur.ai/free/en/papers/patil-2023-gorilla-large-language-model-connected/). Advances in Neural Information Processing Systems (NeurIPS), 2024.
45. Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. YaRN: Efficient Context Window Extension of Large Language Models. In The Twelfth International Conference on Learning Representations (ICLR), 2024. [find](https://scholar.google.com/scholar?q=Bowen+Peng%2C+Jeffrey+Quesnelle%2C+Honglu+Fan%2C+and+Enrico+Shippole%2E+YaRN%3A+Efficient+Context+Window+Extension+of+Large+Language+Models%2E+In+The+Twelfth+International+Conference+on+Learning+Representations+%28)
46. Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. [Reflexion: Language Agents with Verbal Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/). Advances in Neural Information Processing Systems (NeurIPS), 2023.
47. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://md.research.elamur.ai/free/en/papers/suzgun-2026-dynamic-cheatsheet-test-time-learning/). [https://github.com/](https://github.com/) suzgunmirac/dynamic-cheatsheet. Accessed: 09/2025. [↗](https://github.com/)
48. Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi, Dan Jurafsky, and James Zou. [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://md.research.elamur.ai/free/en/papers/suzgun-2026-dynamic-cheatsheet-test-time-learning/). arXiv preprint arXiv:2504.07952, 2025.
49. Harsh Trivedi, Tushar Khot, Mareike Hartmann, Ruskin Manku, Vinty Dong, Edward Li, Shashank Gupta, Ashish Sabharwal, and Niranjan Balasubramanian. [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://md.research.elamur.ai/free/en/papers/trivedi-2024-appworld-controllable-world-apps-people/). In Proceedings of the 62nd Annual Meeting of the Associatio…
50. Dannong Wang, Jaisal Patel, Daochen Zha, Steve Y Yang, and Xiao-Yang Liu. FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets. arXiv preprint arXiv:2505.19819, 2025a. [find](https://scholar.google.com/scholar?q=Dannong+Wang%2C+Jaisal+Patel%2C+Daochen+Zha%2C+Steve+Y+Yang%2C+and+Xiao%2DYang+Liu%2E+FinLoRA%3A+Benchmarking+LoRA+Methods+for+Fine%2DTuning+LLMs+on+Financial+Datasets%2E+arXiv+preprint+arXiv%3A2505%2E19819%2C+2025a%2E)
51. Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). In The Eleventh International Conference on Learning Representations (ICLR), 2023.
52. Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, and Graham Neubig. [Agent Workflow Memory](https://md.research.elamur.ai/free/en/papers/wang-2024-agent-workflow-memory/). In Forty-second International Conference on Machine Learning (ICML), 2025b.
53. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems (NeurIPS), 35:24824–24837, 2022.
54. Cheng-Kuang Wu, Zhi R Tam, Chieh-Yen Lin, Yun-Nung Chen, and Hung-yi Lee. StreamBench: Towards Benchmarking Continuous Improvement of Language Agents. Advances in Neural Information Processing Systems (NeurIPS), 2024. [find](https://scholar.google.com/scholar?q=Cheng%2DKuang+Wu%2C+Zhi+R+Tam%2C+Chieh%2DYen+Lin%2C+Yun%2DNung+Chen%2C+and+Hung%2Dyi+Lee%2E+StreamBench%3A+Towards+Benchmarking+Continuous+Improvement+of+Language+Agents%2E+Advances+in+Neural+Information+Processing+Systems)
55. Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. [A-Mem: Agentic Memory for LLM Agents](https://md.research.elamur.ai/free/en/papers/xu-2025-mem-agentic-memory-llm-agents/). Advances in neural information processing systems (NeurIPS), 2025.
56. John Yang, Carlos E Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press. [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://md.research.elamur.ai/free/en/papers/yang-2024-swe-agent-agent-computer-interfaces/). Advances in Neural Information Processing Systems (NeurIPS), 2024.
57. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/). In Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP), pp. 2369–…
58. Jiayi Yao, Hanchen Li, Yuhan Liu, Siddhant Ray, Yihua Cheng, Qizheng Zhang, Kuntai Du, Shan Lu, and Junchen Jiang. CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion . In Proceedings of the Twentieth European Conference on Computer Systems (EuroSys), pp. 94–109, 2025. [find](https://scholar.google.com/scholar?q=Jiayi+Yao%2C+Hanchen+Li%2C+Yuhan+Liu%2C+Siddhant+Ray%2C+Yihua+Cheng%2C+Qizheng+Zhang%2C+Kuntai+Du%2C+Shan+Lu%2C+and+Junchen+Jiang%2E+CacheBlend%3A+Fast+Large+Language+Model+Serving+for+RAG+with+Cached+Knowledge+Fusion+%2E+)
59. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. [ReAct: Synergizing Reasoning and Acting in Language Models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). In International Conference on Learning Representations (ICLR), 2023.
60. Jiacheng Ye, Chengzu Li, Lingpeng Kong, and Tao Yu. Generating Data for Symbolic Language with Large Language Models. In Proceedings of the 2023 conference on empirical methods in natural language processing (EMNLP), pp. 8418–8443, 2023. [find](https://scholar.google.com/scholar?q=Jiacheng+Ye%2C+Chengzu+Li%2C+Lingpeng+Kong%2C+and+Tao+Yu%2E+Generating+Data+for+Symbolic+Language+with+Large+Language+Models%2E+In+Proceedings+of+the+2023+conference+on+empirical+methods+in+natural+language+pro)
61. Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Pan Lu, Zhi Huang, Carlos Guestrin, and James Zou. Optimizing Generative AI by Backpropagating Language Model Feedback. Nature, 639(8055):609–616, 2025. [find](https://scholar.google.com/scholar?q=Mert+Yuksekgonul%2C+Federico+Bianchi%2C+Joseph+Boen%2C+Sheng+Liu%2C+Pan+Lu%2C+Zhi+Huang%2C+Carlos+Guestrin%2C+and+James+Zou%2E+Optimizing+Generative+AI+by+Backpropagating+Language+Model+Feedback%2E+Nature%2C+639%288055%29%3A60)
62. Matei Zaharia, Omar Khattab, Lingjiao Chen, Jared Quincy Davis, Heather Miller, Chris Potts, James Zou, Michael Carbin, Jonathan Frankle, Naveen Rao, and Ali Ghodsi. The Shift from Models to Compound AI Systems. [https://bair.berkeley.edu/blog/2024/02/](https://bair.berkeley.edu/blog/2024/02/) 18/compound-ai-systems/, 2024. [↗](https://bair.berkeley.edu/blog/2024/02/)
63. Genghan Zhang, Weixin Liang, Olivia Hsu, and Kunle Olukotun. Adaptive Self-Improvement LLM Agentic System for ML Library Development. In Forty-second International Conference on Machine Learning (ICML), 2025a. [find](https://scholar.google.com/scholar?q=Genghan+Zhang%2C+Weixin+Liang%2C+Olivia+Hsu%2C+and+Kunle+Olukotun%2E+Adaptive+Self%2DImprovement+LLM+Agentic+System+for+ML+Library+Development%2E+In+Forty%2Dsecond+International+Conference+on+Machine+Learning+%28ICML)
64. Genghan Zhang, Shaowei Zhu, Anjiang Wei, Zhenyu Song, Allen Nie, Zhen Jia, Nandita Vijaykumar, Yida Wang, and Kunle Olukotun. AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization. arXiv preprint arXiv:2511.15915, 2025b. — [arXiv:2511.15915](https://arxiv.org/abs/2511.15915) [find](https://scholar.google.com/scholar?q=Genghan+Zhang%2C+Shaowei+Zhu%2C+Anjiang+Wei%2C+Zhenyu+Song%2C+Allen+Nie%2C+Zhen+Jia%2C+Nandita+Vijaykumar%2C+Yida+Wang%2C+and+Kunle+Olukotun%2E+AccelOpt%3A+A+Self%2DImproving+LLM+Agentic+System+for+AI+Accelerator+Kernel+Op)
65. Qizheng Zhang, Ali Imran, Enkeleda Bardhi, Tushar Swamy, Nathan Zhang, Muhammad Shahbaz, and Kunle Olukotun. Caravan: Practical Online Learning of In-Network ML Models with Labeling Agents. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI), pp. 325–345, 2024. [find](https://scholar.google.com/scholar?q=Qizheng+Zhang%2C+Ali+Imran%2C+Enkeleda+Bardhi%2C+Tushar+Swamy%2C+Nathan+Zhang%2C+Muhammad+Shahbaz%2C+and+Kunle+Olukotun%2E+Caravan%3A+Practical+Online+Learning+of+In%2DNetwork+ML+Models+with+Labeling+Agents%2E+In+18th+US)
66. Qizheng Zhang, Michael Wornow, and Kunle Olukotun. Agentic Plan Caching: Test-Time Memory for Fast and Cost-Efficient LLM Agents. In The Thirty-ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025c. [find](https://scholar.google.com/scholar?q=Qizheng+Zhang%2C+Michael+Wornow%2C+and+Kunle+Olukotun%2E+Agentic+Plan+Caching%3A+Test%2DTime+Memory+for+Fast+and+Cost%2DEfficient+LLM+Agents%2E+In+The+Thirty%2Dninth+Annual+Conference+on+Neural+Information+Processing)
67. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/). Advances in neural information processing systems (NeurIPS), 36:46595–46623, 2023.
68. Huichi Zhou, Yihang Chen, Siyuan Guo, Xue Yan, Kin Hei Lee, Zihan Wang, Ka Yiu Lee, Guchun Zhang, Kun Shao, Linyi Yang, et al. AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs. arXiv preprint arXiv:2508.16153, 2025. — [arXiv:2508.16153](https://arxiv.org/abs/2508.16153) [find](https://scholar.google.com/scholar?q=Huichi+Zhou%2C+Yihang+Chen%2C+Siyuan+Guo%2C+Xue+Yan%2C+Kin+Hei+Lee%2C+Zihan+Wang%2C+Ka+Yiu+Lee%2C+Guchun+Zhang%2C+Kun+Shao%2C+Linyi+Yang%2C+et+al%2E+AgentFly%3A+Fine%2Dtuning+LLM+Agents+without+Fine%2Dtuning+LLMs%2E+arXiv+preprint)
69. Yujun Zhou, Yufei Han, Haomin Zhuang, Kehan Guo, Zhenwen Liang, Hongyan Bao, and Xiangliang Zhang. Defending Jailbreak Prompts via In-Context Adversarial Game. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 20084–20105, 2024. [find](https://scholar.google.com/scholar?q=Yujun+Zhou%2C+Yufei+Han%2C+Haomin+Zhuang%2C+Kehan+Guo%2C+Zhenwen+Liang%2C+Hongyan+Bao%2C+and+Xiangliang+Zhang%2E+Defending+Jailbreak+Prompts+via+In%2DContext+Adversarial+Game%2E+In+Proceedings+of+the+2024+Conference+on)
70. Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A Comprehensive Survey on Transfer Learning. arXiv:1911.02685, 2019. — [arXiv:1911.02685](https://arxiv.org/abs/1911.02685) [find](https://scholar.google.com/scholar?q=Fuzhen+Zhuang%2C+Zhiyuan+Qi%2C+Keyu+Duan%2C+Dongbo+Xi%2C+Yongchun+Zhu%2C+Hengshu+Zhu%2C+Hui+Xiong%2C+and+Qing+He%2E+A+Comprehensive+Survey+on+Transfer+Learning%2E+arXiv%3A1911%2E02685%2C+2019%2E+%E2%80%94+%5BarXiv%3A1911%2E02685%5D%28https%3A%2F%2Far)

*Updated 3 November 2026*