---
title: "Who cites Manish Kumar — 321 papers of our library"
description: "321 papers of the Elamur Research library cite the works of Manish Kumar, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/manish-kumar/cited-by/"
---
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# Who cites Manish Kumar

408 citations of their works from 321 papers of our library (of the 149,439 whose bibliographies we read) · 36 with a page here, the most cited first · [← Manish Kumar](https://md.research.elamur.ai/free/en/authors/manish-kumar/)

1. [**The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery arXiv:2408.06292**Chris Lu, Cong Lu, Robert Tjarko Lange, et al.*2024 · 372 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/)
2. **[Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 280 citations*
3. [**Perceiver: General Perception with Iterative Attention arXiv:2103.03206**Andrew Jaegle, Felix Gimeno, Andrew Brock, et al.*2021 · 269 citations*](https://md.research.elamur.ai/free/en/papers/jaegle-2021-perceiver-general-perception-iterative-attention/)
4. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? arXiv:2504.13837 · NeurIPS 2025 Oral; ICML 2025 AI4MATH workshop best paper**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 216 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
5. [**AlphaEvolve: A coding agent for scientific and algorithmic discovery arXiv:2506.13131**Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al.*2025 · 202 citations*](https://md.research.elamur.ai/free/en/papers/novikov-2025-alphaevolve-coding-agent-scientific-algorithmic/)
6. [**Agent Laboratory: Using LLM Agents as Research Assistants arXiv:2501.04227**Samuel Schmidgall, Yusheng Su, Ze Wang, et al.*2025 · 139 citations*](https://md.research.elamur.ai/free/en/papers/schmidgall-2025-agent-laboratory-using-llm/)
7. [**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models arXiv:2404.07738**Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/baek-2024-researchagent-iterative-research-idea/)
8. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
9. **[An AI system to help scientists write expert-level empirical software](https://md.research.elamur.ai/free/en/papers/aygun-2025-ai-system-help-scientists/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Eser Aygün, Anastasiya Belyaeva, Gheorghe Comanici, et al.*2025 · 27 citations*
10. **[Can Large Language Models Unlock Novel Scientific Research Ideas?](https://md.research.elamur.ai/free/en/papers/kumar-2025-can-large-language-models-unlock/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sandeep Kumar, Tirthankar Ghosal, Vinayak Goyal, et al.*2025 · 27 citations*
11. **[AI4Research: A Survey of Artificial Intelligence for Scientific Research](https://md.research.elamur.ai/free/en/papers/chen-2025-ai4research-survey-artificial-intelligence/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Mingda Yang, Libo Qin, et al.*2025 · 21 citations*
12. **[Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language Models](https://md.research.elamur.ai/free/en/papers/jin-2024-zero-shot-chain-thought-reasoning/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Feihu Jin, Yifan Liu, Ying Tan*2024 · 18 citations*
13. **[Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS](https://md.research.elamur.ai/free/en/papers/xu-2026-self-correcting-rag-enhancing-faithfulness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shijia Xu, Zhou Wu, Xiaolong Jia, et al.*2026 · 15 citations*
14. [**Data-driven Discovery with Large Generative Models arXiv:2402.13610**Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, et al.*2024 · 15 citations*](https://md.research.elamur.ai/free/en/papers/majumder-2024-data-driven-discovery-large-generative/)
15. **[UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-uora-uniform-orthogonal-reinitialization-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueyan Zhang, Jinman Zhao, Zhifei Yang, et al.*2025 · 14 citations*
16. **[FinanceReasoning: Benchmarking Financial Numerical Reasoning More Credible, Comprehensive and Challenging](https://md.research.elamur.ai/free/en/papers/tang-2025-financereasoning-benchmarking-financial-numerical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zichen Tang, Haihong E, Ziyan Ma, et al.*2025 · 13 citations*
17. **[Literature Meets Data: A Synergistic Approach to Hypothesis Generation](https://md.research.elamur.ai/free/en/papers/liu-2025-literature-meets-data-synergistic-approach/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li, et al.*2025 · 12 citations*
18. [**InternLM-Law: An Open Source Chinese Legal Large Language Model arXiv:2406.14887**Zhiwei Fei, Songyang Zhang, Xiaoyu Shen, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/fei-2024-internlm-law-open-source/)
19. **[Reinforcement Learning for Self-Improving Agent with Skill Library](https://md.research.elamur.ai/free/en/papers/wang-2026-reinforcement-learning-self-improving-agent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiongxiao Wang, Qiaojing Yan, Yawei Wang, et al.*2026 · 8 citations*
20. **[Assessing the Creativity of LLMs in Proposing Novel Solutions to Mathematical Problems](https://md.research.elamur.ai/free/en/papers/ye-2024-assessing-creativity-llms-proposing-novel/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Junyi Ye, Jingyi Gu, Xinyun Zhao, et al.*2024 · 8 citations*
21. [**Cutting Through the Noise: Boosting LLM Performance on Math Word Problems**Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/)
22. **[CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges](https://md.research.elamur.ai/free/en/papers/wang-2026-creativebench-benchmarking-enhancing-machine-creativity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Lam Nguyen, Zhengyang Zhao, et al.*2026 · 6 citations*
23. [**AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery arXiv:2604.05550**Yu Li, Chenyang Shao, Xinyang Liu, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/li-2026-autosota-end-end-automated-research/)
24. **[Can We Predict Before Executing Machine Learning Agents?](https://md.research.elamur.ai/free/en/papers/zheng-2026-can-we-predict-before-executing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingsheng Zheng, Jintian Zhang, Yujie Luo, et al.*2026 · 2 citations*
25. **[Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future](https://md.research.elamur.ai/free/en/papers/wu-2026-can-ai-be-good-peer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sihong Wu, Owen Jiang, Yilun Zhao, et al.*2026 · 1 citation*
26. **[What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary Search](https://md.research.elamur.ai/free/en/papers/zhang-2026-what-makes-llm-good-optimizer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinhao Zhang, Xi Chen, François Portet, et al.*2026 · 1 citation*
27. **[AdaptEvolve: Improving Efficiency of Evolutionary AI Agents through Adaptive Model Selection](https://md.research.elamur.ai/free/en/papers/ray-2026-adaptevolve-improving-efficiency-evolutionary-ai/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Pretam Ray, Pratik Prabhanjan Brahma, Zicheng Liu, et al.*2026*
28. **[AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment](https://md.research.elamur.ai/free/en/papers/wang-2026-alphacontext-evolutionary-tree-based-psychometric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixuan Wang, Yue Huang, Hong Qian, et al.*2026*
29. [**Auditing Discovery Claims: A Two-Sided Criterion for Agentic Science, with the Negative Side Decidable arXiv:2608.00981**Wenhui Chen, Jianlin Chen, Ziyao Lin, et al.*2026*](https://md.research.elamur.ai/free/en/papers/chen-2026-auditing-discovery-claims-two/)
30. **[Crossroads of Optimization under Uncertainty: How to Choose the Optimal Model](https://md.research.elamur.ai/free/en/papers/she-2026-crossroads-optimization-under-uncertainty-how/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengxi She, Zhiqiang Chen, Xingyu Lu, et al.*2026*
31. **[Evolutionary Search for Automated Design of Uncertainty Quantification Methods](https://md.research.elamur.ai/free/en/papers/seleznyov-2026-evolutionary-search-automated-design-uncertainty/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mikhail Seleznyov, Daniil Korbut, Viktor Moskvoretskii, et al.*2026*
32. **[Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable Format](https://md.research.elamur.ai/free/en/papers/wang-2026-format-adapter-improving-reasoning-capability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dingzirui Wang, Xuanliang Zhang, Rongyu Cao, et al.*2026*
33. **[Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution](https://md.research.elamur.ai/free/en/papers/liu-2026-learn-relax-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Beidan Liu, Zhengqiu Zhu, Chen Gao, et al.*2026*
34. **[Open Problems Solved by LLMs? A Survey of Verifiable Mathematical Discovery](https://md.research.elamur.ai/free/en/papers/tzachristas-2026-open-problems-solved-llms-survey/) [Big Picture v2: Crafting a Research Narrative](https://md.research.elamur.ai/free/en/venues/bigpicture/)**Ioannis Tzachristas, Georgios Tzachristas, Aifen Sui*2026*
35. **[Output-Space Search: Targeting LLM Generations in a Frozen Encoder-Defined Output Space](https://md.research.elamur.ai/free/en/papers/materzok-2026-output-space-search-targeting-llm/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Tobias Materzok*2026*
36. **[ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning](https://md.research.elamur.ai/free/en/papers/smirnov-2026-thinkbooster-unified-framework-seamless-test/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Vladislav Smirnov, Quang-Chieu Nguyen, Sergey Senichev, et al.*2026*

285 more citing papers of our library have no page here yet.

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