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DeepSeek-v4.1 Flash: Pushing the Limits of KV Cache Compression

DeepSeek-V4.1 Flash is a 552B-parameter multimodal MoE model with 1M-token context achieving 4x KV cache compression for long-horizon agent workloads.

A detailed analysis of the DeepSeek-V4.1 Flash technical report describes a 552B-parameter multimodal mixture-of-experts model supporting contexts up to 1 million tokens. Its Causal Encoder-Decoder (CED) architecture activates 8B parameters during prefill and 16B during decode, and reportedly delivers about 420 tokens/s. Joint optimization of architecture (CSA2 cross-layer compression), FP4 KV cache precision, and deployment strategy cuts runtime KV cache to roughly 1/4 and persistent KV cache to about 1/8 of DeepSeek-V4-Flash at the same sequence length, targeting storage and bandwidth bottlenecks in long-horizon agent serving. The author notes all DeepSeek-V4 Pro models were taken offline following the release.

Hacker News · AI01:39 UTC · 9h agoModel release HN 41↑ · 3 comments

MiST: Mid-Training LLMs for Cybersecurity

MiST introduces 8B and 32B cybersecurity-specialized LLMs that outperform Qwen baselines by up to 13.1 points on public security benchmarks.

MiST (Mid-trained Security Transformer) applies mid-training as an intermediate adaptation stage, converting an expert-vetted seed corpus into high-quality synthetic domain data rather than continual pretraining on raw text. The 8B and 32B checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute points over Qwen baselines (+27.0% and +15.8% relative). Ablations show gains arise in mid-training and supervised fine-tuning, and MiST provides stronger initialization for downstream fine-tuning and reinforcement learning.

arXiv cs.CRSep 16, 11:33 UTC · 23h agoModel release