Models, methods, and the machinery of intelligence.
arXiv
Verdict: NotableStreaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available.
An emerging hypothesis about reinforcement learning (RL) post-training of large language models (LLMs) is that it merely sharpens existing behaviors of a base model, improving single-shot accuracy at the cost of…
Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task.
We introduce Context Language Models (CLMs), language models that natively manage their own context.
Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging.
Discrete diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional reasoning and global constraint satisfaction.
Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks.
Working agents need to read diverse files, coordinate tools, and produce deliverables.
Answering questions about long videos often requires connecting events involving the same objects across hours or days.