4 papers
PRO-LONG: Programmatic Memory Enables Long-Horizon Reasoning
Alexis Fox, Junlin Wang, Paul Rosu +1
Long-horizon tasks require sustained perception, reasoning, and exploration, and are a persistent challenge for large language model (LLM) agents. This gap is reflected in their li…
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1144
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals
Roy Xie, Junlin Wang, Paul Rosu +4
Large language models (LLMs) process entire input contexts indiscriminately, which is inefficient when the information required to answer a query is localized within the context. W…
In-Context Semi-Supervised Learning
Jiashuo Fan, Paul Rosu, Aaron T. Wang +3
There has been significant recent interest in understanding the capacity of Transformers for in-context learning (ICL), yet most theory focuses on supervised settings with explicit…