From the 1 of 7 linked papers with an AI index.
7 papers
Can AI agents conduct open-ended AI research? Early evidence from two case studies
Peter Kirgis, Sayash Kapoor, Andrew Schwartz +21
The paper evaluates whether current AI agents can independently conduct open‑ended AI research by having them attempt to solve the central questions of two unpublished NeurIPS subm…
Life After Benchmark Saturation: A Case Study of CORE-Bench
Nitya Nadgir, Sayash Kapoor, Kangheng Liu +11
When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy and misses the opportunity t…
Learning with Simulators: No Regret in a Computationally Bounded World
Sasha Voitovych, Abhishek Shetty, Noah Golowich +1
Understanding the minimal assumptions necessary for generalization is the fundamental question in learning theory. Unfortunately, most results rely heavily on independence (or some…
Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
Ishaq Aden-Ali, Noah Golowich, Allen Liu +3
Training modern large language models (LLMs) has become a veritable smorgasbord of algorithms and datasets designed to elicit particular behaviors, making it critical to develop te…
Provably Learning from Modern Language Models via Low Logit Rank
Noah Golowich, Allen Liu, Abhishek Shetty
While modern language models and their inner workings are incredibly complex, recent work (Golowich, Liu & Shetty; 2025) has proposed a simple and potentially tractable abstraction…
Sequences of Logits Reveal the Low Rank Structure of Language Models
Noah Golowich, Allen Liu, Abhishek Shetty
A major problem in the study of large language models is to understand their inherent low-dimensional structure. We introduce an approach to study the low-dimensional structure of…