New York Machine Learning Research Guild

Speaker

Zayne Sprague

Affiliation

PhD Researcher, Courant Institute of Mathematical Sciences, New York University

Research

Reasoning in language models · with Greg Durrett (TAUR Lab)

Partner

CUNY Tech Prep

Venue

Amazon JFK27 (Hank) · 12 W 39th St, New York, NY 10018

Zayne Sprague is an NLP PhD student at NYU's Courant Institute, advised by Greg Durrett in the TAUR Lab. Zayne's research asks how to evaluate reasoning in language models and how to instill it. MuSR (ICLR 2024 spotlight) introduced a benchmark of multistep soft-reasoning problems, and “To CoT or not to CoT?” (ICLR 2025) showed that chain-of-thought helps mainly on math and symbolic tasks. The latest work, SkillFactory (ICLR 2026), uses self-distillation to teach models reusable cognitive behaviors, alongside contributions to OpenThoughts (ICLR 2026, oral), a set of open data recipes for reasoning models. Before the PhD, Zayne earned a BA and MSc in computer science at UT Austin and spent seven years in industry, most recently as a senior software engineer at CoPilot. This summer, Zayne started an internship at Google.

From Chain of Thought to Agent Swarms: A Brief Story on Reasoning in LLMs

Giving language models more time to think has produced substantial gains, but in targeted domains, and the benefits appear to remain uneven across tasks. This talk explores how models can use additional computation at inference time effectively, reviewing the progression from prompt based methods to multi-agent systems. We will begin with chain-of-thought prompting, asking a model to think before giving a final answer, presenting our work showing that chain-of-thought prompting has its strongest benefits concentrated in mathematics and symbolic reasoning. These domains also share a practical advantage: answers can often be checked automatically, providing feedback that can be used to train models through reinforcement learning. This leads us to SkillFactory, where we use a model's own successful and unsuccessful attempts to construct training examples that demonstrate checking answers and retrying. Combining supervised finetuning on these examples with reinforcement learning helps models develop these behaviors and generalize to harder problems. Finally, we will discuss recent work on agent swarms, exploring how reasoning can scale in parallel and what role verification can play in making that additional computation useful.

Speaker

Arthur Jacot

Affiliation

Assistant Professor, Courant Institute of Mathematical Sciences, New York University

Research

Mathematical theory of deep learning

Partner

Intuit

Venue

Intuit NYC · 51 Astor Pl, New York, NY 10003

Arthur Jacot is an Assistant Professor at NYU's Courant Institute of Mathematical Sciences. Arthur builds mathematical theory for how deep neural networks learn, and is best known for introducing the Neural Tangent Kernel during a PhD at EPFL with Clément Hongler. Recent work explains how DNNs act as a computational Occam's razor, finding simple, low-dimensional representations when trained with weight decay. This includes a proof that wide networks trained with weight decay exhibit neural collapse (ICLR 2025, oral) and an account of how DNNs break the curse of dimensionality through compositionality and symmetry learning (ICLR 2025). The newest paper frames deep learning as a convex paradigm of computation that minimizes circuit size with ResNets. Arthur's honors include the 2025 AMR Prize in Mathematics of Artificial Intelligence and the 2023 EPFL PhD Thesis Prize.

Speaker

Rohun Agrawal

Affiliation

PhD Student, Department of Computer Science, Columbia University

Research

World-model planning and large-scale memory retrieval

Speaker

Leon Li

Affiliation

PhD Student, Courant Institute of Mathematical Sciences, New York University

Research

Self-improving AI, continual learning and post-training

Partner

Intuit

Venue

Intuit NYC · 51 Astor Pl, New York, NY 10003

Rohun Agrawal is a computer science PhD student at Columbia University, co-advised by Micah Goldblum and Pavel Izmailov, and an NSF Graduate Research Fellow. Rohun's research pushes the temporal horizons of machine learning models: extending the future horizon through planning with world models, and the past horizon through efficient large-scale memory retrieval. Recent work closes the train–test gap in world models for gradient-based planning, and earlier work on visual agentic AI for spatial reasoning with a dynamic API appeared at CVPR 2025. Rohun earned a BS in applied and computational mathematics at Caltech, working with Georgia Gkioxari on visual reasoning and in Katie Bouman's lab on imaging inverse problems, and has interned at Apple on large video model training and at NASA's Jet Propulsion Laboratory.

Leon Li is a second-year computer science PhD student at NYU, advised by Pavel Izmailov and Micah Goldblum, and a researcher at Modal. Leon studies how to build AI systems that learn, improve themselves, and operate safely in the real world, with interests spanning automated AI research, continual learning, post-training science, and the alignment of superintelligence. Recent work includes “End-to-End Context Compression at Scale” (2026), PersonalLLM (ICLR 2025) for tailoring LLMs to individual preferences, and “LLM Generated Persona is a Promise with a Catch” (NeurIPS 2025). Before the PhD, Leon earned a BS and MS in computer science at Columbia, graduating magna cum laude.