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Kevin Ren

4 accepted papers

2026

QEDBench: Quantifying the Alignment Gap in Automated Evaluation of University-Level Mathematical Proofs

Santiago Gonzalez, Alireza Amiribavandpour, Peter Ye, Edward Zhang, Ruslans Aleksejevs, Todor Antić +45
ICML 2026poster

As Large Language Models (LLMs) saturate elementary benchmarks, the research frontier has shifted from generation to the reliability of automated evaluation. We demonstrate that standard "LLM-as-a-Judge" protocols suffer from a systematic evaluation Alignment Gap when applied to upper-undergraduate …

Cited by 0Source ↗Scholar ↗
2025

Predicting Language Models’ Success at Zero-Shot Probabilistic Prediction

Kevin Ren, Santiago Cortes-Gomez, Carlos Miguel Pati{\~n}o, Ananya Joshi, Ruiqi Lyu, Jingjing Tang +4
EMNLP 2025

Recent work has investigated the capabilities of large language models (LLMs) as zero-shot models for generating individual-level characteristics (e.g., to serve as risk models or augment survey datasets). However, when should a user have confidence that an LLM will provide high-quality predictions

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2025

Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large-Scale Imitation Learning for MAPF

Rishi Veerapaneni, Arthur Jakobsson, Kevin Ren, Samuel Kim, Jiaoyang Li, Maxim Likhachev
ICRA 2025

Multi-Agent Path Finding (MAPF) is the problem of effectively finding efficient collision-free paths for a group of agents in a shared workspace. The MAPF community has largely focused on developing high-performance heuristic search methods. Recently, several works have applied various machine learn

Cited by 7Source ↗Scholar ↗