Papers

Filtered to contextual generation · clear filter

Browse by term

continual learning 86reinforcement learning 48benchmarking 13large language models 12benchmarks 11vision-language models 10language models 9robotics 7world models 7natural language processing 6recursive self-improvement 6generative models 5on-policy distillation 5video generation 5attention mechanisms 4coding agents 4LLMs 4multi-agent systems 4multimodal learning 4multimodal models 4self-distillation 4self-supervised learning 4transformers 4vision-language-action models 4world modeling 4agent-based systems 3agentic models 3agentic search 3agents 3autonomous systems 3

Matching papers

Language Models Can Control Their Own Attention

49 upvotes · 2 SEP 2026 · Namgyu Ho, Huzama Ahmad, Woosung Koh et al.

This paper introduces a new attention mechanism, Declarative Attention (DA), that allows language models to more efficiently control their attention by specifying where they need to focus during generation, rather than scanning the entire context. Practitioners might care because this could lead to significant reductions in computation and memory usage, making large language models more practical for real-world applications.

language modelsattention mechanismssparse attentioncontextual generationmodel efficiency