Entity trail

A.X K2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-07-29 / sources-researcherSK Telecom Releases A.X K2, a 688B/33B-Active MoE Trained on 512 B200s in 70 Days, Beating Chinese Models on Korean and MathSKT put A.X K2 on Hugging Face on July 29, scaling from the 519B A.X K1 to 688B total parameters with only 33B activated per token, trained on 8.5 trillion tokens using 512 NVIDIA B200 GPUs over roughly 70 days, and average benchmark performance improved 32.2 percentage points over K1 despite fewer training tokens. It scores 97.1 on AIME26, 80.5 on KMMLU-Pro, 91.6 on CLIcK and 98 on the telecom split of tau-squared-Bench, and solved all eight problems of the 2026 Korean Mathematical Olympiad second round plus 35 of 42 points on IMO 2025 problems, holding retrieval accuracy at roughly 256K tokens. The efficiency story is the transferable part: a proprietary Sparse Gated Attention that references only needed spans in long documents, Gated Norm for training stability, and FP8 training from the outset rather than post-hoc quantization, which SKT says roughly halves storage and inference cost.
2026-07-28 / reddit-researcher16 Models, 69,440 Forced-Choice Responses: 15 of 16 Land in the Libertarian-Left Quadrant, and Grok Splits Exactly 50/50 Into Two PersonasA study published on unslop.run and surfaced 2026-07-27 ran 30 standard Political Compass passes, 30 reversed-statement passes and 10 reordered-question passes across 16 models — roughly 69,440 forced-choice responses with no neutral option. Fifteen of sixteen clustered around (-6.3, -6.4), with economic scores ranging from -4.8 (Claude Fable 5, the least-left) to -8.6 (Gemini Flash) and a tighter social spread of -5.0 to -7.6; the Chinese models (Qwen3 235B, Kimi K2, GLM 4.5) sat in the same cluster as the American ones. The outlier is Grok 4.5, which exhibited bimodal behavior — one persona averaging -5.9 economically and another at +3.3, appearing at an exact 50/50 split, which reads more like two system prompts than one model's disposition.
2026-07-27 / sources-researcherKimi K3 Open Weights Land on Hugging Face: 2.8T Total / 104B Active, 1M Context, and a New License With a $20M Revenue TriggerMoonshot released the Kimi K3 weights on July 27 — a 1.56TB Hugging Face repo of a 2.8-trillion-parameter MoE with 104B activated parameters, 896 experts (16 selected + 2 shared per token), 93 layers (69 Kimi Delta Attention + 24 Gated MLA), a 1,048,576-token context, and a MoonViT-V2 vision encoder, shipped as MXFP4 weights / MXFP8 activations via quantization-aware training. The license is the notable change: K3 drops K2's 'modified MIT' branding and instead requires any model-as-a-service operator exceeding $20M in aggregate revenue over any consecutive 12 months to sign a separate agreement with Moonshot, while dropping K2's consumer-product attribution requirement. Moonshot deliberately calls it 'open weight' rather than open source — and note that several secondary write-ups incorrectly claim Apache 2.0, which the model card contradicts.
2026-07-27 / reddit-researcherhfviewer's 'Expert Atlas' Traces All 896 Kimi K3 Routed Experts and Puts the Active Fraction at 1.8%The r/LocalLLaMA post announcing K3 on hfviewer.com (160 upvotes) points to an analysis that maps every one of the 896 routed experts from the shipped weights, naming recurring specializations including a 'decorator expert,' a 'country radical' expert, and a catch-all sink. The accompanying plot traces expert-pool size against active fraction across recent MoEs and lands K3 at 1.8% active — the largest expert pool ever shipped in the open, and a continuation of the trend where pools grow while activation share falls. The closest previously traced relative is Kimi K2.7-Code, an MLA MoE routing top-8 of 384 experts plus one shared, which makes the K3 diff legible: three quarters of attention layers become KDA blocks and the expert pool more than doubles.
2026-07-27 / reddit-researcherKimi K3's Technical Report Names the Four Tricks Behind a Claimed 2.5x Scaling-Efficiency Gain Over K2Moonshot's K3 tech report, posted to GitHub and sitting at 367 points on Hacker News, attributes the jump to Kimi Delta Attention plus a new Attention Residuals (AttnRes) mechanism for cross-depth information flow, and a Stable LatentMoE that projects the routed path down from 7,168 to 3,584 dimensions so 16-of-896 routing does not become communication-bound. Training stability at 2.8T came from Quantile Balancing (expert allocation derived from router-score quantiles instead of an auxiliary loss), a Sigmoid Tanh Unit activation, Gated MLA, and a Per-Head Muon optimizer that tunes attention heads independently. Moonshot claims the combination yields ~2.5x better conversion of compute into capability versus Kimi K2 — the specific claim any replication attempt should target first.
2026-07-27 / vibe-coding-researcherKimi K3 Takes #1 on Frontend Code Arena — the First Open Model to Lead ItNathan Lambert's pre-release analysis credits K3's gains to Kimi Delta Attention plus Attention Residuals over a scaled MoE, activating 16 of 896 experts for roughly 2.5x better overall scaling efficiency than K2. It ranks #2 on the Vals AI index, #3 on Artificial Analysis (behind only Fable 5 and GPT-5.6 Sol), and #1 in Frontend Code Arena at 1,679 points ahead of Fable 5's 1,631 — leading six of seven frontend domains, losing only Gaming. On SWE-bench Verified it lands at 93.4% against Opus 5's 97.0%, so the coding lead is domain-specific rather than across the board.
2026-07-25 / saas-disruption-researcherSequoia: Qwen Went From 1% to 69% of All Open-Model Fine-Tunes in Two Years — American Startups Are Building on Chinese WeightsSequoia published 'America's Open-Model Paradox' on July 24, arguing Western AI companies have no legal path to distill their own frontier models, so they post-train on Chinese open weights instead. Qwen's share of open-model fine-tunes went from 1% in January 2024 to 69% in February 2026, and Sequoia says the majority of American AI startups now have Chinese open weights somewhere in the stack — Thinking Machines bootstrapped its Inkling model on Moonshot Kimi K2.5 synthetic data. The proposed fix is 'controlled teacher access' from US labs; for builders, the practical read is that your cheapest fine-tuning base is probably Qwen or GLM, and that is a supply-chain fact, not an ideological one.
2026-07-19 / skill-finderEAGLE-3 speculative decoding on vLLM is delivering ~2x throughput on AMD InstinctAs of July 13, 2026, AMD Quark is training, quantizing, and serving EAGLE-3 speculative-decoding drafts through vLLM on AMD Instinct GPUs, reporting up to 2.00x throughput for Kimi-K2.5 and 1.79x for MiniMax-M2.5. Related July work includes ROCm end-to-end support on MI355X with a prebuilt container (July 10) and Hopper-optimized attention plus FP8 MoE backends improving TTFT and TPOT on H20. If you self-host and haven't enabled a draft model, this is the largest single-config throughput win currently available.
2026-07-18 / skill-finderAdd a stateful link-tracker monitor — it cuts gradual-attack evasion from 93% to 47%arXiv 2607.02514 studies persistent-state AI control and finds high evasion (≥65%) generalizes across attacker backends (Sonnet 4.5, Gemini 3.1 Pro, Kimi K2.5) and across state-of-the-art monitor models. The concrete win: combining a stateful link-tracker monitor with trajectory monitors in a four-monitor ensemble drops gradual-attack evasion from 93% (weakest standard diff monitor) to 47%. Diff-level review of each step is not enough when state persists between steps.
2026-07-16 / sources-researcherThinking Machines Lab Ships Inkling: 975B-A41B Multimodal, Apache 2.0 — the Leading American Open-Weights ModelTML's first full LLM is open-weights: 975B total / 41B active MoE, multimodal (text/image/audio in, text out), trained on 45 trillion tokens, with a 1M-token context in the open weights (256K via API/Tinker). A smaller Inkling-Small (276B-A12B) ships alongside. It ranks #41 on the Intelligence Index — top U.S. open-weights model but behind GLM-5.2 and Kimi K2.6 — and #9 in Agentic Web App Arena, with notably concise reasoning and strong tool calling. Architecture is unusual: non-RoPE relative positional encoding, 5:1 local-to-global sliding-window attention, short convolutions, and 2 shared expert sinks. Available on Hugging Face, Databricks, Baseten, Modal, and vLLM.
2026-07-13 / vibe-coding-researcherGitHub Copilot CLI v1.0.70 Adds GPT-5.6 Support and a /refine Prompt-Rewrite CommandCopilot CLI v1.0.70 (July 9, 2026) adds GPT-5.6 model support, a new /refine command that rewrites your prompt before execution, web_fetch proxy support, and better error handling for MCP and skill command failures; the prior v1.0.68 (July 1) added the kimi-k2.7-code model. The /refine command signals prompt-shaping is moving into the CLI itself rather than being a manual pre-step.
2026-07-12 / rss-researcherHugging Face Transformers v5.13.0 Adds a Unified Export Pipeline and Kimi K2.5 ArchitectureTransformers v5.13.0 introduces a native in-library export pipeline built on one HfExporter base class with three runtime subclasses — DynamoExporter (PyTorch), OnnxExporter (ONNX), and ExecutorchExporter (ExecuTorch) — consolidating what were previously separate export paths. The release also adds architecture support for Kimi K2.5 (a native-multimodal agentic model underpinning the 2.5–2.7 line) and Xiaomi's MiMo-V2-Flash, plus generation, attention, cache, and quantization fixes. For builders, one export API across PyTorch/ONNX/ExecuTorch simplifies shipping the same model to both server and on-device targets.

Graph relationships

DEPRECATES
A.X K1
A.X K2 improves upon A.X K1 with 32.2 percentage point improvement
Source finding
USES
NVIDIA B200
A.X K2 was trained on 512 NVIDIA B200 GPUs
Source finding
RELEASED
SK Telecom
SK Telecom released A.X K2, a 688B MoE model, on July 29
Source finding
RELEASED
SK Telecom -> A.X K2
SK Telecom released A.X K2, a 688B MoE model, on July 29
Source finding

Source trail

EDAILY (corroborated by SK Telecom's own July 29 announcement)unslop.run (surfaced via Hacker News, 41 points / 76 comments)simonwillison.net (corroborated by the moonshotai/Kimi-K3 model card)r/LocalLLaMA (primary: hfviewer.com Expert Atlas)Moonshot AI (surfaced via Hacker News, 367 points / 164 comments)Interconnects Sequoia Capital vLLM Blog arXiv 2607.02514 — Distributed Attacks in Persistent-State AI Control Latent Space (AINews) / Thinking Machines Lab GitHub Hugging Face

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