【Spicy Couple Gangbang】 Episode 1 – Two More Fans Join the Group (20:20mins) 🌶️👬👭👬 During dinner, two of Little Chili’s fans suddenly messaged her 📱
We jokingly said, “Why not invite them over?” She casually asked, and they immediately agreed, saying they were coming right away! 😳
We had just had so much fun, and everyone wanted another round of even more passionate action 🔥 Not long after, they arrived at the hotel 🚪
They turned out to be two young Chinese businessmen. Only after chatting did we learn this was their first time trying group sex — one had a bit of experience, the other was a complete beginner and so nervous it was adorable 🫣 They were both so young that I felt a little out of place and a bit awkward inside…
After all, we barely knew each other and were about to get completely naked and play together, plus I seemed much older than them 😅
But I still adjusted my mindset and threw myself into it 💪 Thankfully the King’s Game 👑 used step-by-step escalating commands to quickly connect everyone. The awkwardness disappeared fast, and we were fully into the play in no time! 🔥 —————————————— 【群交超嫩辣情侣】第一集 多两位粉丝参与群交 （20:20分钟） 🌶️👬👭👬 晚餐时，小辣椒的两位粉丝突然联系她📱
我们开玩笑说要不要邀请他们过来玩，她随口一问，他们竟然马上答应，说立刻就来！😳
刚刚才玩得太开心，大家也都想再来一轮更激情的碰撞🔥 没多久他们就到了酒店🚪
原来是两位来自中国的年轻商人。聊天才知道这是他们第一次尝试群交——一位有点经验，另一位完全零经验，紧张得超级可爱🫣 他们都好年轻，搞得我有点格格不入，心里其实有点不自在……
毕竟几乎没怎么接触就要直接赤裸裸地一起玩，而且自己好像比他们大很多😅
但我还是尽量调整好状态，好好投入进去💪 幸好有国王游戏👑，用一步步升级的指令很快把大家连在一起，尴尬一下就消失，一下子就玩开了！🔥 @SEXYOY0214

DeepSeek V4.1 Gives Prefill and Decode Different Compute Paths Prompt tokens mostly traverse 20 layers; generated tokens traverse all 40. This Causal Encoder-Decoder design nearly halves long-input prefill while preserving autoregressive generation. Zhihu contributor 潜龙勿用, Changxin Ke(柯昌鑫), a graduate researcher at ICT, CAS, explains how its architecture and post-training were designed together. 1️⃣ A causal encoder, not T5 The 40-layer backbone is split into a 20-layer causal encoder and a 20-layer decoder. Both remain causal. The encoder processes the prompt and supplies the decoder’s global KV. Most prompt tokens avoid the decoder stack, while generated tokens still run through all 40 layers. That means 8B active parameters per prefill token versus 16B per decode token. 2️⃣ Most layers share global memory CSA2 uses three modes: Full creates global KV and an index; Reindex shares the KV but selects new positions; Reuse shares both. Only four layers create independent global KV, four reindex it, and 30 reuse both KV and the latest index. Each layer retains its own query and local SWA state. The first Full layer builds up to 16,384 candidates. Later layers search this pool for their Top-512. From 4K to 1M context, decode FLOPs per token rise only about 25%. 3️⃣ Serving approximations enter training Exact reconstruction of decoder SWA states would replay 2,560 prompt tokens. V4.1 replays only the final 128 encoder outputs and trains the model to tolerate the approximation. With FP4 global KV, cache falls to 890 bytes per token, persistent cache to roughly one eighth of V4-Flash, and prefill compute close to half. Bounded replay and constrained retrieval are not last-minute serving tricks. The model experiences them during post-training. 4️⃣ Post-training is an evolving Agent system Each RL task combines a problem, environment, and verifier. New trajectories can reveal shortcuts, broken environments, or verifier errors and send the task back for repair. V4.1 trains across multiple harnesses, merges checkpoints between RL runs, and finishes with on-policy distillation from more than 40 teachers. Raising reasoning effort from 25 to 100 increases output length about 2.5×, while average Pass@1 across eight benchmarks rises from 67.1% to 76.3%. ✅ The real design choice V4.1 aligns model structure, cache policy, retrieval limits, training environments, and inference around long-running Agents. The model learns under the same constraints the deployed system will actually impose. 🔗 Full analysis: #DeepSeek# #DeepSeekV41# #LLMArchitecture# #AIAgents# #LongContext# #AIInfra#