AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
We are late to this but better than never. Have been busy finalizing the second AIE NYC, which is happening in one month. Get your tix before prices go up - we will announce speakers from Bridgewater, Ramp, Coatue, Mastercard, Vanguard, Coinbase, Blackrock, Fidelity, Point72, Capital One, JPMC, Wells Fargo, Bloomberg, A24 (yes the movie studio) Labs, Two Sigma, Apollo Global, and more next week! The way DeepSeek pursues their research agenda is nothing short of fascinating. In between major DeepSeek versions, from v2 to v3 to v4, they have released intermediate papers with a hyperfocused architectural improvement and basically a 100% hit rate, from Math (esp GRPO), Coder, and R1, not to mention more recent work on Manifold Constrained Hyperconnections and Compressed Sparse Attention. After the enormous attention in 1H2025 from the R1 paper, DeepSeek started laying low, and for about the past year, was happy to let peers like GLM and Kimi take the lead on Open Models. It looked dicey for a little bit, but true whalebros never wavered, and now DeepSeek are sending a weirdly mixed message by doing a completely new architecture, retiring V4 Pro and going all in on this new model, and yet only titling it v4.1 Flash, it seems to be a test of whether or not you know how to read through the basic headlines to understand true advances. Yes, v4.1 Flash is technically behind other open models in some benchmarks. But that’s because we don’t yet have benchmarks that concisely capture what v4.1, and the broader research agenda of DeepSeek, is aiming for - the most creative and efficient use of context we have ever seen openly explained. If you are the sort to only read model versions and benchmark headlines, you are exactly the type of superficial person that DeepSeek is looking to fool. The best way to understand DeepSeek’s enormous advance here is to look at Sebastian’s meme: Same model name, but hardly a 0.1 bump by anyone’s standards, and they even threw in vision without making you wait for a separate model. For a better visualization you can look at all the model innovations stacked up over time from the OG encoder-decoder architecture from Attention is All You Need: …architecture.petergostev.chatgpt.site ","username":"petergostev","name":"Peter Gostev","profile_image_url":"https://pbs.substack.com/profile_images/1934694573797670912/1gnGJwlr_normal.jpg","date":"2026-09-10T20:43:13.000Z","photos":[{"img_url":"https://substackcdn.com/image/fetch/$s_!-BQ4!,w_1028,c_limit,f_auto,q_auto:best,fl_progressive:steep/l_play_button_usfui2,w_88,e_colorize:0/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fss-rehosttw-video-preview-13_2098148819175104520.jpg","link_url":"https://t.co/wJzLLmRJms"}],"quoted_tweet":{},"reply_count":54,"retweet_count":334,"like_count":2762,"impression_count":218344,"expanded_url":null,"video_url":"https://video.twimg.com/amplify_video/2098148819175104520/vid/avc1/1316x720/36dLkQiID-HcJSjm.mp4","video_preview_media_key":"13_2098148819175104520","belowTheFold":true}" data-component-name="Twitter2ToDOM"> If you read our V4 Pro writeup and Engram you should be up to date on the basic architectural reading for DeepSeek as of April 2026, but what we are HUGE fans of is the prefill/decode separation introduced here, 8B in prefill (input tokens), 16B in decode (output tokens), causing our alphabet soup of “DeepSeek v4.1-Flash: 763B-P8B-D16B” if you extend the established notation for MoEs. That’s a sparsity of 1-2%, and if you read the DeepSeek v4.1 Flash tech report, combined with new tweaks like Sliding-Window Attention Bounded Replay, makes for a KV cache footprint up to 1/8 that of V4 Flash… which make it much better/faster/cheaper for long running agents: We are so glad that DeepSeek is back publishing SOTA research. Our last highlight is their comments on post-training, where they largely seem to agree with Prof Jie Tang: AI News for 9/9/2026-9/10/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space. You can opt in/out of email frequencies! AI Twitter Recap DeepSeek launched V4.1-Flash as a new open-weight flagship focused on extreme inference efficiency and low cost. Independent benchmark account Artificial Analysis reported that DeepSeek V4.1 Flash surpasses DeepSeek V4 Pro 0813 despite being much cheaper, scoring 40 on the Artificial Analysis Intelligence Index, just below GLM-5.3-Flash and above the latest V4 Pro, while being priced at $0.30 / 1M input tokens and $1.20 / 1M output tokens with cached input at $0.006 / 1M and an additional 50% off-peak discount; they also describe it as a 763B total-parameter model with 8B active input and 16B active output parameters, 1M-token context, text+image input, MIT license, and US/API availability via DeepSeek first party @ArtificialAnlys, @ArtificialAnlys, @ArtificialAnlys Vals called it the new #1 open-weight model on the Vals Index, ahead of Kimi K3, at just $0.30 per test, the cheapest model in the open-weight top 10; they also note the eval ran with 1M context, 384 max output tokens, temperature 1, default top-p/top-k, and high reasoning effort @ValsAI, @ValsAI, @ValsAI Baseten shipped day-0 support and summarized the product positioning as smarter, faster, and more efficient than DeepSeek v4 Pro 0813, with text and vision, US-only, ZDR, and 1M context @baseten Ollama began rolling it out to Max and Team accounts, later expanding to Pro plan subscribers @ollama, @ollama, @ollama Architecture and paper-level technical details The most discussed technical novelty is a causal encoder-decoder design aimed at lowering active compute and KV/cache costs. Artificial Analysis says the model uses a new causal Encoder–Decoder architecture, with 8B active parameters for input/prefill and 16B active parameters for output/decode @ArtificialAnlys Sebastian Raschka characterized V4.1 as a “big overhaul” and said they “should have called it DeepSeek V5,” explicitly highlighting the encoder-decoder setup as the key break from prior DeepSeek generations @rasbt Multiple technical readers reacted to the design as unusually hybrid: one called it “a very interesting mix of very conservative and sometimes old ideas in research and potentially cutting edge efficiency and hardware design in engineering” @_xjdr A concise architecture read from Stochastic Chasm compared the design philosophy to HySparse, NSA, and DeepSeek’s own CSA/HCA from V4, summarizing it as a local sliding-window branch plus sparse retrieval branch, suggesting this sparse/local hybrid is becoming a broader pattern @stochasticchasm The same account noted multimodal changes were not radical, saying DeepSeek mostly “lets the backbone handle most of it and give it visual tokens,” with 3x3 pixel unshuffle instead of the more common 2x2 @stochasticchasm They later flagged a “big difference from K3 on vision encoders,” implying the vision front-end diverges materially from recent Chinese peers @stochasticchasm TeortaxesTex observed a recurring DeepSeek pattern of doing something unusual in the first N layers—previously dense or hash-routed, now SWA-only—speculating this may reflect repeated training difficulties in early layers @teortaxesTex Later, the same account argued the stack is “down to 40 layers, arguably only 20 legit decoder layers,” underscoring just how aggressively DeepSeek may be compressing effective depth in decode-critical paths @teortaxesTex Another thread fragment from TeortaxesTex suggested DeepSeek is doing multiple compression frequencies, “it’s just all CSA2,” in response to architectural discussion around memory compression @teortaxesTex Nrehiew’s technical notes emphasize KV cache compression as central to the design, calling it a case study in “how obsessing over KV Cache compression gets you a hyper-efficient frontier model” @nrehiew_ In a follow-up, nrehiew highlighted infrastructure specifics from the report: dispatch strategy to reduce long-tail stalls, router replay from previous checkpoints, management of shorter-completion off-policy effects via dataset-level capping, discard schemes, bounded off-policy ratio and loss masking, and persistent KVs and routers when a new checkpoint is updated; they also mention a final stage with full-vocab OPD on 40+ teacher models @nrehiew_ Nrehiew concluded that the design looks cleaner than the older HSA + CSA combination in V4, saying it was “very clearly designed for inference,” and cited a striking ~890 bytes/token KV size for the benchmarked score regime @nrehiew_ Stochastic Chasm inferred QAT for the KV cache, saying this would explain why the model performs better than peers under FP4 KV cache @stochasticchasm Benchmark results and numbers Independent evals consistently paint V4.1-Flash as unusually strong on cost-adjusted intelligence, long context, and automation, with a major caveat around verbosity. Artificial Analysis’ headline: 40 AA Index, above V4 Pro and below GLM-5.3-Flash @ArtificialAnlys, corroborated separately by Scaling01 @scaling01 Artificial Analysis reported AutomationBench-AA: 69%, tying GPT-6 Astra (69%) and above Grok 4.6 (67%), while improving 15 points over V4 Flash 0731 and sitting 12 points above V4 Pro 0813 (57%) and 7 points above GLM-5.3 (62%) @ArtificialAnlys On GDPval-AA v2 it reportedly gains 164 Elo, from 1468 to 1632, overtaking Kimi K3 at 1584 @ArtificialAnlys On AA-LCR v1.1 it scores 84%, on par with GPT-5.6 Sol and Gemini 3.8 Flash at 84% @ArtificialAnlys Artificial Analysis also says V4.1 Flash is among the most verbose models measured, averaging 89k tokens per Intelligence Index task—25% more than GLM-5.3 (71k), 29% more than GLM-5.3-Flash (69k), 62% more than V4 Pro 0813 (55k), and even above Fable 5.1 (78k) and Claude Opus 5 (73k) @ArtificialAnlys Even with that verbosity, AA estimates just $0.27 per Intelligence Index task, roughly 7x below GLM-5.3 ($2.01) and Kimi K3 ($2.00), and ~2.5x below V4 Pro 0813 ($0.67) @ArtificialAnlys Vals’ result reinforces cost leadership: $0.30/test, #1 open-weight on their board @ValsAI A separate reaction thread summarized DeepSWE-style claims more aggressively, saying V4.1 Flash offered better performance than GPT-5.6 Sol and Opus 5 in DeepSWE at 94% lower API costs, but that statement is secondhand summary rather than a primary benchmark post in this dataset @kimmonismus Running it locally and inference engineering reactions A large fraction of discussion centered on the surprising ease of running V4.1-Flash on commodity-ish local hardware through offload and SSD streaming. Fraser Price reported full-precision DeepSeek 4.1 Flash + DSpark at 200 TPS on 4 Max-Qs with just 64GB system RAM, offloading a 200GB Engram/hash table to NVMe; he says this made keeping the full structure in RAM unnecessary and promised a vLLM recipe @fraserpricee He later improved that to 300+ TPS on 4 RTX Pros, still at full precision, with 10% extinction-risk views. On governance, Thom Wolf announced a new Open Alignment team at Hugging Face, and Richard Ngo published a sharp critique of Paul joining OpenAI’s board and of what he sees as the safety community’s capture by AGI companies. Top tweets by engagement Anthropic threat intelligence report: @AnthropicAI published a detailed account of sophisticated Claude misuse across cyber, influence, biology, surveillance, and weapons. OpenAI pauses new $200 Pro signups for Astra capacity reasons: @thsottiaux said existing users are unaffected and API/other plans remain available. GPT-Live-1 API launch: @OpenAIDevs launched the new full-duplex voice model into the API. ChatGPT Work Data agent: @ChatGPT announced a data-connected enterprise agent for dashboards, answers, and actions. SWE-2 release: @cognition introduced a new coding model claiming near-frontier eval performance at materi [truncated for AI cost control]