Papers

Filtered to attention mechanisms · clear filter

Browse by term

continual learning 91reinforcement learning 43vision-language models 18benchmarking 12benchmarks 11robotics 11large language models 10language models 9world models 9video generation 6generative models 5multimodal models 5natural language processing 5recursive self-improvement 5video world models 5agents 4dialogue systems 4diffusion models 4foundation models 4multimodal large language models 4on-policy distillation 4reasoning 4self-distillation 4self-supervised learning 4conversational AI 3evaluation benchmarks 3human-computer interaction 3LLMs 3multi-agent systems 3policy optimization 3

Matching papers

Metis: Memory Foundation Model

256 upvotes · 29 JUL 2026 · Zeyu Zhang, Ziliang Guo, Yihang Sun et al.

This paper introduces a new type of AI model called memory foundation models, which allows the model to learn and retain information internally, rather than relying on external memory modules. This could be useful for practitioners who want to build more efficient and flexible AI agents.

memory foundation modelsmultimodal modelsnative memorymodel compressionattention mechanismscontinual learning

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

166 upvotes · 3 SEP 2026 · Heng Wang, Jielin Qiu, Wenting Zhao et al.

This paper proposes a new method for managing the memory of large language models, called Random Attention, which improves their performance on tasks that require extended reasoning by reducing memory usage. Practitioners caring about efficient model deployment might find this approach useful.

language modelsKV cacheattention mechanismsmemory efficiencyreasoning tasks