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20232026
most citedQuantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

7 citations · 10 across the 8 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking

Anjali Kantharuban, Jonas Mueller

Recent benchmarks rely on user simulators to evaluate AI agents in multi-turn interaction. While existing simulation techniques demonstrate surface fidelity to human style and beha…

cs.AI2026

StudentBench: AI and human tutoring yield equivalent GRE learning gains

Curtis Northcutt, Inaara Hasmani, Kevin Feng +3

Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advancing model capabilities. We in…

cs.AI2026

Efficient Test-Time Adaptation through Human-AI Interaction

Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao +22

AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely meet the personal bar profes…

cs.AI2026

VIALS: A Benchmark for Visual Interpretation of Artifacts in the Life Sciences

Elaine Lau, Thanuka Udumulla, Lee Izhaki-Tavor +3

In professional life sciences workflows, scientists routinely interpret visual artifacts (gel blots, microscopy images, plasmid maps, flow cytometry plots, molecular structures, ..…

cs.LG2026

CAREBench: A Child-Safety Risk Benchmark for Language Models

Kaavya Krishna-Kumar, Elaine Lau, Vaughn Robinson +6

How can we evaluate whether frontier AI systems recognize child-safety risks before they escalate into explicit harm? Existing child safety evaluations focus on child sexual abuse…

cs.AI2026

BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows

Elaine Lau, Markus Dücker, Ronak Chaudhary +24

Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profe…

cs.CL2026

Real-Time Trustworthiness Scoring for LLM Structured Outputs and Data Extraction

Hui Wen Goh, Jonas Mueller

Structured Outputs from current LLMs exhibit sporadic errors, hindering enterprise AI deployment. We present CONSTRUCT, a real-time uncertainty estimator that scores the trustworth…