CS Graduate Student

Publications

32 total publications  —  ICML (×2)  •  ICLR  •  ACL (×4)  •  EMNLP (×2, Main)  •  CVPR  •  AAAI (Oral)  •  7 Q1 journal papers  •  5 under review

Accepted Papers (2025–)

  1. Gaurav Srivastava, Aafiya Hussain, Chi Wang, Yingyan (Celine) Lin, and Xuan Wang. “effGen: Enabling Small Language Models as Capable Autonomous Agents.” In Proc. Forty-third International Conference on Machine Learning (ICML’26), pages TBD, 2026 (acceptance rate: 26.6%). [arxiv] [effgen.org] [docs]

  2. Gaurav Srivastava, Aafiya Hussain, Zhenyu Bi, Swastik Roy, Priya Pitre, Meng Lu, Morteza Ziyadi, and Xuan Wang. “BeyondBench: Benchmark-Free Evaluation of Reasoning in Language Models.” In Proc. The Fourteenth International Conference on Learning Representations (ICLR’26), pages TBD, April 23-27, 2026, Rio de Janeiro, Brazil (acceptance rate: 28%). [openreview] [arxiv] [leaderboard]

  3. Gaurav Srivastava, Shuxiang Cao, and Xuan Wang. “ThinkSLM: Towards Reasoning in Small Language Models.” In Proc. 2025 Conf. of Empirical Methods in Natural Language Processing (EMNLP’25 Main), pages TBD, November 5-9, 2025, Suzhou, China (acceptance rate: 22.16%). [arxiv] [anthology] [leaderboard]

  4. Gaurav Srivastava, Zhenyu Bi, Meng Lu, and Xuan Wang. “DEBATE, TRAIN, EVOLVE: Self‑Evolution of Language Model Reasoning.” In Proc. 2025 Conf. of Empirical Methods in Natural Language Processing (EMNLP’25 Main), pages TBD, November 5-9, 2025, Suzhou, China (acceptance rate: 22.16%). [arxiv] [anthology] [project website]

  5. Gaurav Srivastava, Aafiya Hussain, Sriram Srinivasan, and Xuan Wang. “Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models.” In Proc. of the 64th Annual Meeting of the Association for Computational Linguistics (ACL’26 Findings), pages TBD, July 2-7, 2026, San Diego, CA (acceptance rate: 18%). [arxiv] [leaderboard]

  6. Zhenyu Bi*, Gaurav Srivastava*, Yang Li, Swastik Roy, Meng Lu, Morteza Ziyadi, and Xuan Wang. “JudgeBoard: Benchmarking and Enhancing Small Language Models for Reasoning Evaluation.” In Proc. The 40th Annual AAAI Conference on Artificial Intelligence (AAAI’26 Oral), pages TBD, January 20-27, 2026, Singapore (acceptance rate: 17.6%). [arxiv]

  7. Aafiya Hussain, Gaurav Srivastava, Alvi Ishmam, Zaber Hakim, and Chris Thomas. “SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models.” In Proc. of the 64th Annual Meeting of the Association for Computational Linguistics (ACL’26 Main), pages TBD, July 2-7, 2026, San Diego, CA (acceptance rate: 19%).

  8. Priya Pitre, Gaurav Srivastava, Lu Zhang, Le Wang, Naren Ramakrishnan, and Xuan Wang. “A Diagnostic Study of Multi-Agent LLMs for Real-World Debates.” In Proc. Forty-third International Conference on Machine Learning (ICML’26), pages TBD, 2026 (acceptance rate: 26.6%).

  9. Meng Lu, Ran Xu, Yi Fang, Wenxuan Zhang, Yue Yu, Gaurav Srivastava, Yuchen Zhuang, Mohamed Elhoseiny, Charles Fleming, Carl Yang, Zhengzhong Tu, Yang Xie, Guanghua Xiao, Hanrui Wang, Di Jin, Wenqi Shi, and Xuan Wang. “Scaling Agentic Reinforcement Learning for Tool-Integrated Reasoning in VLMs.” In Proc. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR’26), pages TBD, June 3-7, 2026, Denver CO (acceptance rate: 25.42%). [arxiv]

  10. Christopher Latimer, Nicolò Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, and Naren Ramakrishnan. “Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects.” In Proc. of the 64th Annual Meeting of the Association for Computational Linguistics (ACL’26 System Demonstrations), pages TBD, July 2-7, 2026, San Diego, CA (acceptance rate: 37%). [openreview] [pypi] [github] [website] [vectorize.io]

  11. Priya Pitre, Gaurav Srivastava, Lu Zhang, Le Wang, Naren Ramakrishnan, and Xuan Wang. “Beyond Consensus: Evaluating Multi-Agent LLM Debates through a Deliberative Democracy Framework.” In Proc. of the Generation, Evaluation & Metrics Workshop (GEM @ ACL’26), July 2026, San Diego, CA. [openreview]

(*Joint first authors)

Preprints

  1. Chris Latimer, Nicolo Boschi, Andrew Neeser, Chris Bartholomew, Gaurav Srivastava, Xuan Wang, and Naren Ramakrishnan. “Hindsight is 20/20: Building Agent Memory that Retains, Recalls, and Reflects.” arXiv preprint arXiv:2512.12818 (2025). [arxiv]

  2. Andrew Neeser, Gaurav Srivastava, Kaylen Latimer, Aadyant Khatri, Xuan Wang, Christopher Latimer, and Naren Ramakrishnan. “QuOTE: Question-Oriented Text Embeddings.” (Under review in KDD 2026) arXiv preprint (soon).

  3. Meng Lu, Yuchen Zhuang, Wenqi Shi, Gaurav Srivastava, Charles Fleming, and Xuan Wang. “MAF-IE: Multi-Agent Finetuning for Zero-shot Information Extraction.” arXiv preprint (soon).

  4. Priya Pitre, Gaurav Srivastava, Lu Zhang, Le Wang, Naren Ramakrishnan, and Xuan Wang. “SIMAGENT: Towards Multi-Agent LLM for Real-World Stakeholder Debate Simulations without Ground Truth.” arXiv preprint (soon).

  5. Gaurav Srivastava*, Meng Lu*, and Xuan Wang. “Towards Small (Vision-)Language Models as the Future of Real-World Agents.” (Under review in KDD 2026) arXiv preprint (soon).

Before 2025

Journal Papers

  1. Gaurav Srivastava and Nitesh Pradhan. “Handling imbalanced class in melanoma: Kemeny–Young rule based optimal rank aggregation and Self-Adaptive Differential Evolution Optimization.” Engineering Applications of Artificial Intelligence, vol. 125, p. 106738, 2023. (impact factor: 8.0) [link] [pdf]

  2. Gaurav Srivastava, Aninditaa Chauhan, and Nitesh Pradhan. “Cjt-deo: Condorcet’s jury theorem and differential evolution optimization based ensemble of deep neural networks for pulmonary and colorectal cancer classification.” Applied Soft Computing, vol. 132, p. 109872, 2023. (impact factor: 8.3) [link] [pdf]

  3. Gaurav Srivastava, Nitesh Pradhan, and Yashwin Saini. “Ensemble of deep neural networks based on condorcet’s jury theorem for screening covid-19 and pneumonia from radiograph images.” Computers in Biology and Medicine, vol. 149, p. 105979, 2022. (impact factor: 7.7) [link] [pdf]

  4. Gaurav Srivastava, Aninditaa Chauhan, Nitigya Kargeti, Nitesh Pradhan, and Vijaypal Singh Dhaka. “ApneaNet: A hybrid 1DCNN-LSTM architecture for detection of Obstructive Sleep Apnea using digitized ECG signals.” Biomedical Signal Processing and Control, vol. 84, p. 104754, 2023. (impact factor: 5.1) [link] [pdf]

  5. Gaurav Srivastava, Aninditaa Chauhan, Mahesh Jangid, and Sandeep Chaurasia. “Covixnet: A novel and efficient deep learning model for detection of covid-19 using chest x-ray images.” Biomedical Signal Processing and Control, vol. 78, p. 103848, 2022. (impact factor: 5.1) [link] [pdf]

  6. Amitesh Kumar Dwivedi, Gaurav Srivastava, Sakshi Tripathi, and Nitesh Pradhan. “eFuseNet: A deep ensemble fusion network for efficient detection of Arrhythmia and Myocardial Infarction using ECG signals.” Multimedia Tools and Applications, pages 1-32, 2024. (impact factor: 3.0) [link] [pdf]

  7. Nitesh Pradhan, Gaurav Srivastava, and Geetika Kaushik. “Vit-Ensemble: Probabilistic voting based ensemble of Vision Transformers for tuberculosis detection using radiographs.” Computational Biology and Chemistry, 2024. [link] [pdf]

Conference Proceedings

  1. Gaurav Srivastava and Mahesh Jangid. “Multi-view sparse laplacian eigenmaps for nonlinear spectral feature selection.” 2023 International Conference on System Science and Engineering (ICSSE), IEEE, 2023, pp. 548–553. [link] [pdf]

  2. Amitesh Kumar Dwivedi, Gaurav Srivastava, and Nitesh Pradhan. “Nff: A novel nested feature fusion method for efficient and early detection of colorectal carcinoma.” Proceedings of Fourth International Conference on Computer and Communication Technologies, Springer, 2023, pp. 297–309. [link] [pdf]

  3. Ayush Singh, Gaurav Srivastava, and Nitesh Pradhan. “Pneumothorax segmentation using feature pyramid network and mobilenet encoder through radiography images.” International Conference on Soft Computing and Signal Processing, Springer, 2023, pp. 107–117. [link] [pdf]

  4. Rugved Sanjay Chavan, Gaurav Srivastava, and Nitesh Pradhan. “Advance plant health monitoring and forecasting system using edge-fog-cloud computing and lstm networks.” Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications: ICAIAA 2022, Springer, 2023, pp. 335–344. [link] [pdf]

  5. Nitesh Pradhan, Saransh Gupta, and Gaurav Srivastava. “Image colorization: A convolutional network approach.” Proceedings of International Conference on Data Science and Applications: ICDSA 2022, Volume 2, Springer, 2023, pp. 533–544. [link] [pdf]

  6. Gaurav Srivastava, Aninditaa Chauhan, Mahesh Jangid, and Ashish Jain. “An analysis of deep learning models to diagnose covid-19 using radiography images.” 2022 International Conference for Advancement in Technology (ICONAT), IEEE, 2022, pp. 1–7. [link] [pdf]

Book Chapters

  1. Gaurav Srivastava, Devika Sapra, Akruti Sinha, Mahin Anup, and Deepak Sinwar. “Artificial intelligence and iot-assisted sustainable manufacturing for industry 4.0.” Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials, CRC Press, 2024, pp. 15–34. [link] [pdf]

  2. Akruti Sinha, Gaurav Srivastava, Devika Sapra, and Chhavi Deshlahra. “Fog computing for agriculture applications and its issues.” Bio-Inspired Optimization in Fog and Edge Computing Environments, Auerbach Publications, 2023, pp. 117–138. [link] [pdf]

  3. Akruti Sinha, Deepak Sapra, Gaurav Srivastava, Mahin Anup, and Deepak Sinwar. “AI-assisted big data analytics for smart healthcare systems.” Intelligent Internet of Things for Smart Healthcare Systems, CRC Press, 2023, p. 81. [link] [pdf]