Carnegie Mellon University
AI Measurement Science & Engineering (AIMSEC)

CMU-NIST Cooperative Research Center

2026 — 14 publications

Sorted by date, newest first

Aditi RaghunathanJune 2026
Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

Zhong, Z., Segal, I., Bercovich, I., Saxena, S., Zhang, K., et al., & Raghunathan, A. · arXiv:2606.08960

↗ arXiv
Lujo BauerJune 2026
Security and Privacy Prompts in the Wild: What Users Ask LLMs and How LLMs Respond

Kim, H., Wu, X., Akgul, O., Bauer, L., & Christin, N. · arXiv:2606.18062

↗ arXiv
Fernando DiazJune 2026
Characterizing Cultural Localization in AI-Generated Stories

Bhatt, S., Vijay, S., Milbauer, J., & Diaz, F. · arXiv:2606.14626

↗ arXiv
Anand RaoHong ShenMay 2026
Towards Apples to Apples for AI Evaluations: From Real-World Use Cases to Evaluation Scenarios

Choong, Y. Y., Greene, K., Qian, A., Marasli, M., Yang, Z., Rao, A., & Shen, H. · arXiv:2605.07986

↗ arXiv
Zico KolterMay 2026
Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks

Kim, E., Gu, C., Tiwari, V., & Kolter, J. Z. · arXiv:2605.11209

↗ arXiv
Hong ShenMay 2026
Actualizing Ethical Principles for Curating Large-Scale Training Datasets in the Era of Massive AI Models

Cazacu, S., Qian, A., Zhao, D., Pine, K., Walker, S., & Shen, H. · ACM FAccT CRAFT, 2026

↗ FAccT
Hong ShenApril 2026
What People See (and Miss) About Generative AI Risks: Perceptions of Failures, Risks, and Who Should Address Them

Li, M., Bickersteth, W., Tang, N., Kapoor, P., Win, K., Zhong, P., & Shen, H. · arXiv:2604.22654

↗ arXiv
Hong ShenApril 2026
Human Expertise for AI Red-Teaming and Scalable Evaluation

Qian, A., Chandhiramowuli, S., Dabbish, L., & Shen, H. · CHI EA '26, Barcelona, Spain

↗ CHI
Ramayya KrishnanRema PadmanMarch 2026
The Model Says Walk: How Surface Heuristics Override Implicit Constraints in LLM Reasoning

Li, Y., Zhang, L., Jiang, T., Krishnan, R., & Padman, R. · arXiv:2603.29025

↗ arXiv
Yubo LiRamayya KrishnanRema PadmanMarch 2026
When Documents Disagree: Measuring Institutional Variation in Transplant Guidance with Retrieval-Augmented Language Models

Li, Y., Krishnan, R., & Padman, R. · arXiv:2603.21460

↗ arXiv
Rema PadmanRamayya KrishnanMarch 2026
Confidence as Control: A Survey of Confidence Utilization in Large Language Models

Zhou, Y., Shen, X., Miao, Y., & Krishnan, R. · 2026

↗ Link
Ramayya KrishnanRema PadmanFebruary 2026
Consistency of Large Reasoning Models Under Multi-Turn Attacks

Li, Y., Krishnan, R., & Padman, R. · arXiv:2602.13093

↗ arXiv
Ramayya KrishnanRema PadmanFebruary 2026
Closing Reasoning Gaps in Clinical Agents with Differential Reasoning Learning

Liu, J., Jiang, Y., Krishnan, R., Padman, R., Zhang, Y., & Bian, J. · arXiv:2602.09945

↗ arXiv
Eli Ben-MichaelFebruary 2026
Omitted Variable Bias in Language Models Under Distribution Shift

Lin, V., Morency, L. P., & Ben-Michael, E. · arXiv:2602.16784

↗ arXiv

2025 — 6 publications

Ramayya Krishnan29 Dec 2025
ML Compass: Navigating Capability, Cost, and Compliance Trade-offs in AI Model Deployment

Digalakis Jr, V., Krishnan, R., Fernandez, G. M., & Orfanoudaki, A. · arXiv:2512.23487

↗ arXiv
Hoda HeidariOctober 2025
The AI Power Disparity Index: Toward a Compound Measure of AI Actors' Power to Shape the AI Ecosystem

Kim, R. M., Kuehnert, B., Lazar, S., Singh, R., & Heidari, H. · AIES 2025

↗ AIES
Hoda HeidariOctober 2025
Moral Change or Noise? On Problems of Aligning AI With Temporally Unstable Human Feedback

Keswani, V., Cousins, C., Nguyen, B., & Heidari, H. · arXiv:2511.10032

↗ arXiv
Ramayya KrishnanRema PadmanOctober 2025
Time-To-Inconsistency: A Survival Analysis of Large Language Model Robustness to Adversarial Attacks

Krishnan, R., & Padman, R. · arXiv:2510.02712

↗ arXiv
Ramayya KrishnanJune 2025
From Posting to Prediction: Building Validated Workforce Analytics

Charuvilparambil Titus, N. G., Krishnan, R., & Telang, R. · SSRN:4906323

↗ SSRN
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