Papers

Filtered to multimodal Mixture-of-Experts (MoE) · clear filter

Browse by term

continual learning 82reinforcement learning 50large language models 12benchmarking 11benchmarks 11language models 11vision-language models 10robotics 7natural language processing 6world models 6generative models 5recursive self-improvement 5attention mechanisms 4diffusion Transformers 4multi-agent systems 4multimodal learning 4multimodal models 4on-policy distillation 4self-distillation 4self-supervised learning 4transformers 4video generation 4vision-language-action models 4agent-based systems 3agentic models 3agentic search 3autonomous systems 3coding agents 3diffusion models 3image generation 3

Matching papers

DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression

53 upvotes · 17 SEP 2026 · DeepSeek-AI, Anyi Xu, B. Li et al.

This paper introduces DeepSeek-V4.1-Flash, a more efficient model that reduces the computational cost of long-horizon agents by optimizing its prefill process and cache compression. Practitioners can benefit from this model's improved performance and reduced storage needs for agentic workloads.

multimodal Mixture-of-Experts (MoE)Causal Encoder-Decoder (CED)Compressed Sparse Attention 2 (CSA2)FP4 cachingpersistent KV cacheagentic workloads