5 papers
QEDBENCH: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs
Santiago Gonzalez, Alireza Amiri Bavandpour, Peter Ye +48
As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that st…
Reasoning with Sampling: Cutting at Decision Points
Felix Zhou, Anay Mehrotra, Quanquan C. Liu
Frontier reasoning models are produced by posttraining base language models with reinforcement learning. Recent work has challenged this by showing that sampling from a sharpened v…
Differentially Private Matchings
Michael Dinitz, George Z. Li, Quanquan C. Liu +1
Computing matchings in graphs is a foundational algorithmic task. Despite extensive interest in differentially private (DP) graph analysis, work on privately computing matching sol…
Pointwise Lipschitz Continuous Graph Algorithms
Quanquan C. Liu, Grigoris Velegkas, Yuichi Yoshida +1
In many real-world applications, it is undesirable to drastically change the problem solution after a small perturbation in the input, as unstable outputs can lead to costly transa…
Sublinear Space Graph Algorithms in the Continual Release Model
Alessandro Epasto, Quanquan C. Liu, Tamalika Mukherjee +1
The graph continual release model of differential privacy seeks to produce differentially private solutions to graph problems under a stream of edge updates where new private solut…