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Nicolas Baldwin

4 accepted papers

2026

Interactive Evaluation of Large Language Models for Multi-Requirement Software Engineering Tasks

AAAI 2026technical

Standard single-turn, static benchmarks fall short in evaluating the nuanced capabilities of Large Language Models (LLMs) on complex tasks such as software engineering. In this work, we propose a novel interactive evaluation framework that assesses LLMs on multi-requirement programming tasks through

Cited by 0SourcePDFScholar
2026

RL for Reasoning by Adaptively Revealing Rationales

ICLR 2026poster

Learning in the combinatorially large output space of sequence generation problems is challenging as providing expert demonstrations scales poorly with sequence length, and RL struggles with sparse rewards. Between dense demonstrations in supervised training and no demonstrations in reinforcement l…

Cited by 0SourceScholar
2025

AI Research Agents for Machine Learning: Search, Exploration, and Generalization in MLE-bench

NeurIPS 2025spotlight

AI research agents are demonstrating great potential to accelerate scientific progress by automating the design, implementation, and training of machine learning models. We focus on methods for improving agents' performance on MLE-bench, a challenging benchmark where agents compete in Kaggle competi…

Cited by 0SourcecodeScholar
2025

GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning

EMNLP 2025

Large Language Models (LLMs) achieve strong performance across diverse tasks, but their effectiveness often depends on the quality of the provided context. Retrieval-Augmented Generation (RAG) enriches prompts with external information, but its reliance on static databases constrains adaptability an