Hi, I am Junlin, a fourth-year Computer Science PhD student at Duke University, advised by Prof. Bhuwan Dhingra, and previously also by Prof. Sam Wiseman (2022–2023). Before Duke, I worked closely with Sameer Singh on machine learning interpretability and NLP. I have also been a research intern at Google DeepMind, Together AI and AWS.
My main research goal is recursive self-improvement (RSI): AI that automates AI research. Toward that, I work on:
- Multi-agent scaling
- Continual learning in the real world that creates economic value
- The science of post-training
- Long-horizon evaluation that is robust, fast, and hard to reward-hack
News
Publications
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PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
arXiv 2026
@article{fox2026prolong, title = {PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning}, author = {Alexis Fox and Junlin Wang and Paul Rosu and Bhuwan Dhingra}, year = {2026}, journal = {arXiv preprint arXiv: 2607.20064} } -
DSGym: A Holistic Framework for Evaluating and Training Data Science Agents
arXiv 2026
@article{nie2026dsgym, title = {DSGym: A Holistic Framework for Evaluating and Training Data Science Agents}, author = {Fan Nie and Junlin Wang and Harper Hua and Federico Bianchi and Yongchan Kwon and Zhenting Qi and Owen Queen and Shang Zhu and James Zou}, year = {2026}, journal = {arXiv preprint arXiv: 2601.16344} } -
When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework
ICLR 2026
@inproceedings{xu2026when, title = {When Does Divide and Conquer Work for Long Context {LLM}? A Noise Decomposition Framework}, author = {Zach Xu and Shang Zhu and Jue WANG and Junlin Wang and Ben Athiwaratkun and Chi Wang and James Zou and Ce Zhang}, booktitle = {The Fourteenth International Conference on Learning Representations}, year = {2026}, url = {https://openreview.net/forum?id=ddQFUuHDDt} } -
Improving Model Alignment Through Collective Intelligence of Open-Source LLMs
ICML 2025
@inproceedings{wang2025improving, title = {Improving Model Alignment Through Collective Intelligence of Open-Source Models}, author = {Junlin Wang and Roy Xie and Shang Zhu and Jue WANG and Ben Athiwaratkun and Bhuwan Dhingra and Shuaiwen Leon Song and Ce Zhang and James Zou}, booktitle = {Forty-second International Conference on Machine Learning}, year = {2025}, url = {https://openreview.net/forum?id=K4N9UvsuNB} } -
Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods
arXiv 2025
@article{wang2025thinkdeep, title = {Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods}, author = {Junlin Wang and Shang Zhu and Jon Saad-Falcon and Ben Athiwaratkun and Qingyang Wu and Jue Wang and Shuaiwen Leon Song and Ce Zhang and Bhuwan Dhingra and James Zou}, year = {2025}, journal = {arXiv preprint arXiv: 2504.14047} } -
How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning
arXiv 2025
@article{cai2025backtracking, title = {How Much Backtracking is Enough? Exploring the Interplay of SFT and RL in Enhancing LLM Reasoning}, author = {Hongyi Cai and Junlin Wang and Xiaoyin Chen and Bhuwan Dhingra}, year = {2025}, journal = {arXiv preprint arXiv: 2505.24273} } -
Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals
NeurIPS 2025
@article{xie2025when, title = {Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals}, author = {Xie, Roy and Wang, Junlin and Rosu, Paul and Deng, Chunyuan and Sun, Bolun and Lin, Zihao and Dhingra, Bhuwan}, journal = {Advances in Neural Information Processing Systems}, year = {2025} } -
Mixture-of-Agents Enhances Large Language Model Capabilities
ICLR 2025
@inproceedings{wang2025mixtureofagents, title = {Mixture-of-Agents Enhances Large Language Model Capabilities}, author = {Junlin Wang and Jue Wang and Ben Athiwaratkun and Ce Zhang and James Zou}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2025}, url = {https://arxiv.org/abs/2406.04692}, } -
Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies
EMNLP 2024
@article{wang2024reasoning, title = {Reasoning in Token Economies: Budget-Aware Evaluation of LLM Reasoning Strategies}, author = {Junlin Wang and Siddhartha Jain and Dejiao Zhang and Baishakhi Ray and Varun Kumar and Ben Athiwaratkun}, year = {2024}, journal = {arXiv preprint arXiv: 2406.06461} } -
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods
EMNLP 2024
@article{xie2024recall, title = {ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods}, author = {Roy Xie and Junlin Wang and Ruomin Huang and Minxing Zhang and Rong Ge and Jian Pei and Neil Zhenqiang Gong and Bhuwan Dhingra}, year = {2024}, journal = {arXiv preprint arXiv: 2406.15968} } -
Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications
ACL 2024 Findings
@article{wang2024raccoon, title = {Raccoon: Prompt Extraction Benchmark of LLM-Integrated Applications}, author = {Junlin Wang and Tianyi Yang and Roy Xie and Bhuwan Dhingra}, year = {2024}, journal = {arXiv preprint arXiv: 2406.06737} } -
LLM-Resistant Math Word Problem Generation via Adversarial Attacks
EMNLP 2024 Findings
@inproceedings{Xie2024adversarial, title = {Adversarial Math Word Problem Generation}, author = {Roy Xie and Chengxuan Huang and Junlin Wang and Bhuwan Dhingra}, year = {2024}, url = {https://openreview.net/forum?id=bJz5uGzEe6&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3Daclweb.org%2FACL%2FARR%2F2024%2FJune%2FAuthors%23your-submissions)} } -
NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge
NAACL 2024 Findings
@misc{howard2023neurocomparatives, title={NeuroComparatives: Neuro-Symbolic Distillation of Comparative Knowledge}, author={Phillip Howard and Junlin Wang and Vasudev Lal and Gadi Singer and Yejin Choi and Swabha Swayamdipta}, year={2023}, eprint={2305.04978}, archivePrefix={arXiv}, primaryClass={cs.CL} } -
Maestro: A Gamified Platform for Teaching AI Robustness
EAAI 2023
@inproceedings{DBLP:conf/aaai/GeletaXLW00M23, author = {Margarita Geleta and Jiacen Xu and Manikanta Loya and Junlin Wang and Sameer Singh and Zhou Li and Sergio Gago Masagu{'{e}}}, editor = {Brian Williams and Yiling Chen and Jennifer Neville}, title = {Maestro: {A} Gamified Platform for Teaching {AI} Robustness}, booktitle = {Thirty-Seventh {AAAI} Conference on Artificial Intelligence, {AAAI} 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, {IAAI} 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2023, Washington, DC, USA, February 7-14, 2023}, pages = {15816-15824}, publisher = {{AAAI} Press}, year = {2023}, url = {https://doi.org/10.1609/aaai.v37i13.26878}, doi = {10.1609/AAAI.V37I13.26878} } -
Gradient-based Analysis of NLP Models is Manipulable
EMNLP 2020 Findings
@inproceedings{wang2020gradientbased, Author = {Junlin Wang, Jens Tuyls, Eric Wallace and Sameer Singh}, Booktitle = {Empirical Methods in Natural Language Processing Findings}, Year = {2020}, Title = {Gradient-based Analysis of NLP Models is Manipulable} } -
AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
Demo at EMNLP 2019Best Demo Award
@inproceedings{Wallace2019AllenNLP, Author = {Eric Wallace and Jens Tuyls and Junlin Wang and Sanjay Subramanian and Matt Gardner and Sameer Singh}, Booktitle = {Empirical Methods in Natural Language Processing}, Year = {2019}, Title = {AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models} }