Research in machine unlearning (MU) has gained strong momentum: MU is now widely regarded as a critical capability for building safe and fair AI. In parallel, r...

We study online learning in the adversarial injection model introduced by [Goel et al. 2017], where a stream of labeled examples is predominantly drawn i.i.d. f...

The dependence on expert annotation has long constituted the primary rate-limiting step in the application of artificial intelligence to biomedicine. While supe...

Error-bounded lossy compression has been regarded as a promising way to address the ever-increasing amount of scientific data in today's high-performance comput...

BabyLM aims to dissolve the boundaries between cognitive modeling and language modeling. We call for both workshop papers and for researchers to join the 4th Ba...

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by conditioning generation on retrieved external documents, but the effect of retriev...

Edge-based representations are fundamental cues for visual understanding, a principle rooted in early vision research and still central today. We extend this pr...

Large Language Models (LLMs) play a critical role in how humans access information. While their core use relies on comprehending written requests, our understan...

In this study, the output of large language models (LLM) is considered an information source generating an unlimited sequence of symbols drawn from a finite alp...

Modern code intelligence agents operate in contexts exceeding 1 million tokens--far beyond the scale where humans manually locate relevant files. Yet agents con...

Large language models are being deployed in complex socio-technical systems, which exposes limits in current alignment practice. We take the position that the d...

Large language models (LLMs) offer substantial promise for automating clinical text summarization, yet maintaining factual consistency remains challenging due t...