Artificial Intelligence AI News

Google AI Just Released Gemini 3.7 Flash: A Coding and Agent Model at $0.75/1M Input Tokens → Built on 3.6 Flash with algorithmic improvements to the reasoning core. Same 1M context, 64K output, March 2026 cutoff. → The gains concentrate in three places: software engineering, document-heavy knowledge work, and web development. The sharper argument is price. Performance: → FrontierCode 1.1: 43.6% vs 34.4% → DeepSWE v1.1: 65.3% vs 48.6% → WebDev Arena: 1588 Elo vs 1538 → AutomationBench: 30.4% vs 17.0% → GDP.pdf: 34.0% vs 22.0% Full analysis: https://www.marktechpost.com/2026/08/13/google-ai-just-released-gemini-3-7-flash/ Technical details: https://blog.google/innovation-and-ai/models-and-research/gemini-models/introducing-gemini-3-7-flash/

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The Video Production Stack Now Fits on One Desk: LTX-2.5 Launches as NVIDIA-Accelerated Open Weights World Model. It's an open weights world model for video, real-time apps, and physical AI — optimized with NVIDIA to run on RTX GPUs and DGX Spark. Here's what stood out: 1. The speed numbers are the story In LTX's published image-to-video benchmark (10-second clip): → 6.8 seconds on-prem (2x NVIDIA GB200) → 23.7 seconds via the LTX API → 52–70 seconds for the fastest closed rivals (Omni Flash, Grok 1.5, Veo 3.1) → 398 seconds for Kling 3.0 Pro — that's 58.5x slower On-prem generation finishes faster than the clip itself plays. 2. Multishot consistency fixes the real blocker Earlier open models generated each shot separately, so characters drifted between cuts — unusable for actual campaigns. LTX-2.5 renders the full sequence as one output, holding character, scene, and voice across cuts. A custom Gemma 4 backbone handles complex, multi-subject prompts. 3. Diffusion Fidelity Rendering is a smart cost tradeoff → Motion and structure built in an 8x temporally compressed latent space → Full detail spent only on high-fidelity keyframes → Keyframe count adapts to scene complexity Quality lands where it matters without full render cost on every frame. Full analysis: https://www.marktechpost.com/2026/08/11/the-video-production-stack-now-fits-on-one-desk-ltx-2-5-launches-as-nvidia-accelerated-open-weights-world-model/ Model weight: https://huggingface.co/Lightricks Technical blog: https://blogs.nvidia.com/blog/local-ai-open-source-models-agents-nemotron/

Meta AI Releases Muse Glimmer: A 30B Open-Weights Agentic Model That Runs on One Consumer GPU Meta has released Muse Glimmer, a 30-billion-parameter multimodal model distilled from Muse Spark. It is tuned for always-on local agent workflows, and ships under Apache 2.0. A 30B model normally needs over 55 GB of memory at full precision. Meta compresses it to roughly 4-bit, then adds block-level speculative decoding so it answers fast enough to sit inside a real agent loop. The result runs on one consumer GPU or a Mac, with no network call.... Model and training Muse Glimmer is a dense causal transformer with a dedicated perception encoder. Total parameters are roughly 30B, including the vision tower. Grouped-query attention uses 32 query heads and 2 KV heads. Attention repeats a [Local, Local, Local, Global] pattern with a 2,048 sliding window. RoPE is applied to local layers only, with theta 500,000. The vision side is a ~1.8B ViT-G/14 perception encoder accepting up to 4,096 visual tokens per image. Context length is 131,072+, vocabulary is 202,048 tokens, and the knowledge cutoff is January 4, 2026. Input is text and image; output is text. Audio is not supported, and video is processed as individual frames. Training ran in three phases: (1) Pre-training used logit distillation on Muse Spark’s outputs. (2) Mid-training added longer-context, agent-heavy data with richer reasoning traces. (3) Post-training combined supervised fine-tuning with on-policy distillation and reinforcement learning across general, reasoning, coding, and agentic domains. Full analysis: https://www.marktechpost.com/2026/08/10/meta-ai-releases-muse-glimmer/ Model weights: https://huggingface.co/collections/meta-models/muse-glimmer

Mistral AI Releases Shieldstral 1.0 3B: An Open-Weights Policy-Adaptive Multimodal Safety Classifier Matching Models 7× Its Size It's a policy-adaptive multimodal safety classifier. Most guardrail models bake a fixed harm taxonomy into their weights, so re-targeting one means retraining. This one takes the policy as a plain-language question at inference time. Here's what's actually interesting: 𝗠𝗼𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗿𝗲𝗱𝘂𝗰𝗲𝗱 𝘁𝗼 𝗼𝗻𝗲 𝘆𝗲𝘀/𝗻𝗼 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Three fields per request. <Instruct> sets evaluation context and strictness. <Query> states the policy as a single yes/no question. <Document> holds the content — a prompt, a response, a prompt-response pair, or an image with optional text. At inference the model unembeds only toward the yes and no token IDs, softmax-normalizes them, and thresholds at 0.5. One forward pass, one token, continuous score. 𝗧𝗲𝘅𝘁 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 → 84.9% average text F1 — ties GPT-OSS-Safeguard-20B → 83.8% multimodal F1 vs 77.6% for OmniGuard-7B → VLGuard 97.7, UnsafeBench 81.8, HarmBench prompt 99.4 → 91.5% refusal detection overall 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 → Shieldstral-3B: 91.3% F1 → GPT-OSS-Safeguard-20B: 94.1% → Nemotron-3.5-Safety-4B: 91.8% Full analysis: https://www.marktechpost.com/2026/08/07/mistral-ai-releases-shieldstral-1-0-3b/ Model weight: https://huggingface.co/mistralai/Shieldstral-1.0-3B Paper: https://arxiv.org/pdf/2607.25857

NVIDIA Releases Alpamayo 2 Super: A 34B Open Vision-Language-Action Model for Robotaxis and Autonomous Driving Under OpenMDW-1.1 Here are some key takeaways: 1. The architecture is split → 32B VLM backbone, built on Cosmos 3 Super Reasoner, post-trained with reinforcement learning → 2.3B diffusion-based action decoder → Roughly 3x the scale of the 10B Alpamayo 1 and Alpamayo 1.5 2. It ranks first on LingoQA → Lingo-Judge score of 79.2, first among nearly 40 models evaluated → +17.0 over Qwen2.5-VL 72B, +15.1 over Gemini 2.5 Pro, +23.2 over GPT-4o → Closed-loop AlpaSim score of 1.50 ± 0.13 across 910 NuRec scenarios → Open-loop minADE₆ of 0.911 m at 6.4s on 937 challenging samples 3. One pass produces five outputs → A trajectory: 64 waypoints from 0.1s to 6.4s, each with ego-frame XYZ and a 3x3 rotation matrix → A Chain-of-Causation trace explaining the decision → A meta-action such as yield, lane change or stop → Reasoning auto-labels for training and validation data → Visual question answering with 2D grounding 4. The training corpus → ~115,000 hours of multi-camera driving video with egomotion and trajectory annotations → ~3,700,000 Chain-of-Causation traces → Inputs are six cameras and four historical frames each in the validated public notebook profiles Full analysis: https://www.marktechpost.com/2026/08/05/nvidia-alpamayo-2-super-open-vla-model-autonomous-driving/ Model weights: https://huggingface.co/nvidia/Alpamayo2-Super

How to Secure AI Agents, MCP Servers, and LLM Apps in Production Application security rests on one assumption: software does what its code says. ---AI agents broke it. Mend.io's new practitioner guide — 𝘚𝘦𝘤𝘶𝘳𝘪𝘯𝘨 𝘈𝘐 𝘢𝘨𝘦𝘯𝘵𝘴, 𝘔𝘊𝘗 𝘴𝘦𝘳𝘷𝘦𝘳𝘴 & 𝘓𝘓𝘔 𝘢𝘱𝘱𝘴 — starts from that break. An agent's behavior emerges from the model, the system prompt, retrieved context, and the tools it's permitted to call. The failure modes never appear in a CVE feed: prompt injection through data, over-permissioned agents causing damage without a single exploit, poisoned tool descriptions on MCP servers, EOL models serving predictions after patching stops. The guide's answer is three moves: 𝗦𝗲𝗲: Inventory the agentic attack surface across five layers — interaction, agent, integration, model, code. Hunt shadow agents via repo signatures and network egress. Run every agent through a 12-point misconfiguration checklist. 𝗙𝗶𝘅: Enrich → prioritize → triage. Rank by reachability and agentic amplification, not severity scores. Automate FP closures only with evidence trails. Risk acceptance is never automated. 𝗣𝗿𝗼𝘁𝗲𝗰𝘁: Guardrails on every input and output — embedded Python SDK or standalone Docker API server. Inbound: injection patterns, jailbreaks. Outbound: credentials, PII, policy violations. The core design principle: an agent that can't call a dangerous tool doesn't need a prompt begging it not to. Includes a 15-question maturity self-assessment aligned to NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act. Full analysis: https://www.marktechpost.com/2026/08/03/how-to-secure-ai-agents-mcp-servers-and-llm-apps-in-production/ Download the full guide, free: https://pxllnk.co/lxn88m

Alibaba Qwen Releases Qwen3.8-Max: A 2.4 Trillion Parameter MoE Model and the Most Capable One in the Qwen Family to Date 1. What shipped → 2.4T parameters, mixture-of-experts → 1M context, 991K max input, 131K max output → Text, image and video input → $2.00 input, $6.00 output, $0.25 cached input per 1M tokens → Open weights next week 2. Where it leads Fable5 → Terminal Bench 2.1: 86.6 vs 84.6 → PaperBench: 93.0 vs 88.8 → IFBench: 82.8 vs 63.5 → Parametric CAD Bench: 91.5 vs 87.5 → OmniDocBench 1.5: 92.1 vs 89.5 3. Where it trails Fable5 → SWE-bench Pro: 67.7 vs 80.0 → FrontierSWE: 73.5 vs 88.8 → HLE: 43.6 vs 53.3 → Toolathlon Verified: 72.5 vs 77.9 4. The category split → Multimodal Reasoning: above Fable5 on 11 of 11 rows → Document & Office: 7 of 7 → Perception & Grounding: 9 of 10 → Coding Agent: 3 of 11 → General Agent: 1 of 8 → Visual Agent & Coding: 3 of 11 Full analysis: https://www.marktechpost.com/2026/08/03/alibaba-qwen-releases-qwen3-8-max/ Technical details: https://qwen.ai/blog?id=qwen3.8 API: https://www.qwencloud.com/models/qwen3.8-max#context

AMD released Instella-MoE-16B-A3B, a fully open Mixture-of-Experts model trained from scratch on AMD Instinct MI300X and MI325X GPUs. Here are some key takeaways: 1. The model → 16B total parameters, 2.8B active per token → 2 shared experts plus 6 of 64 routed, across 27 layers → 7.1T pre-training tokens, context extended from 4K to 64K 2. Where the efficiency comes from → FarSkip-Collective overlaps expert-parallel communication with computation → 12.7% faster pre-training → Up to 39.2% lower time to first token under expert parallelism in SGLang 3. What it scores → Base averages 76.7, strongest among fully open models → Ahead of Moonlight-16B-A3B at 76.2 and OLMo-3-7B at 70.1 → Behind Qwen3.5-4B-Base at 79.5, which is not fully open 4. What actually ships → Weights from pre-training, mid-training, long-context, SFT, DPO and RL → Data mixtures, training configs and inference code → ResearchRAIL on weights, MIT on the training codebase Full analysis: https://www.marktechpost.com/2026/08/01/amd-instella-moe-16b-a3b-fully-open-mixture-of-experts-llm/ Model weight: https://huggingface.co/collections/amd/instella-moe Repo: https://github.com/AMD-AGI/Instella-MoE

MiniMax Releases MiniMax H3: An Omni-Modal Video Model That Generates 15-Second 2K Clips With Native Stereo Audio. It ranks #1 in video editing on Artificial Analysis, at $0.13 per second of 2K output. Here are some important key takeaways: 𝟭. 𝗧𝗵𝗲 𝘁𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗿 𝗶𝘀 𝘁𝗵𝗲 main 𝘀𝘁𝗼𝗿𝘆 MiniMax rebuilt the H-series tokenizer from scratch as H3-VAE. → 4× gain in effective sequence length → That compression is what makes native 2K affordable, not an upscale of 1080p 𝟮. 𝗖𝗮𝗽𝘁𝗶𝗼𝗻𝗶𝗻𝗴 𝗯𝗲𝗰𝗮𝗺𝗲 𝗮 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 H3 does not just describe the target video. It describes how the input context relates to the target, and how elements inside that context relate to each other. → ~100K tokens of inference per source, distilled to ~4K on average → This is why one natural-language instruction replaces a fixed task list 𝟯. 𝗧𝗵𝗲𝘆 𝘁𝗵𝗿𝗲𝘄 𝗮𝘄𝗮𝘆 𝘁𝗵𝗲𝗶𝗿 𝗼𝘄𝗻 𝗯𝗲𝘀𝘁 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 Multimodal context tripled the variance in sequence length. Understanding and generation became different compute shapes. So MiniMax set aside the Hailuo-02 architecture and separated the two workloads in training. → ~30% higher end-to-end training throughput 𝟰. 𝗡𝗼 𝘀𝘂𝗽𝗲𝗿-𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝘂𝗹𝗲 For 2K, the base model regenerates its own low-res output in-context, re-reading the original multimodal context. → Recovers small text and brand marks that an upscaler can only guess at → For product labels and on-screen copy, that is the difference between usable and reshoot 𝟱. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗰𝗼𝘀𝘁𝘀 → $7.80 per minute at 2K with audio → Seedance 2.0 at 1080p: $22.45/min → Kling 3.0 at 1080p: $20.16/min → Gemini Omni Flash still undercuts it at $6.00/min Full analysis: https://www.marktechpost.com/2026/08/01/minimax-releases-minimax-h3-an-omni-modal-video-model-that-generates-15-second-2k-clips-with-native-stereo-audio/ Technical details: https://www.minimax.io/blog/minimax-h3

We just released 'Token Saver' for Claude-Desktop: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99% When you drop a 200-page document into Claude Desktop, the full context gets re-sent on every single turn. That compounding "PDF Tax" adds up fast—both in token costs and context window bloat. How it works: Instead of uploading raw documents to the cloud, Token Saver runs a lightweight Local Hybrid RAG pipeline directly on your machine: → Keyword Search (BM25): Powered by SQLite FTS5 for precise terminology. → Semantic Search: Powered by a local all-MiniLM-L6-v2 embedding model. →Zero-Upload Privacy: Files stay on your local drive and communicate via standard I/O (stdio) with folder allowlisting. Benchmark Results with Example: → 33-page FDA Drug Label: Reduced from 23,959 tokens to 1,021 (95.7% saved) → 88-page GDPR Document: Reduced from 70,260 tokens to 996 (98.6% saved) → 233-page Legal Brief: Reduced from 133,349 tokens to 740 (99.4% saved) Zero Python environment required—it installs directly in Claude Desktop via a single .mcpb bundle! Full analysis: https://www.marktechpost.com/2026/07/30/token-saver-an-open-source-mcp-extension-using-local-hybrid-rag/ GitHub Repo: https://github.com/Marktechpost/Token-Saver/tree/main

Liquid AI released two bidirectional encoders this week: LFM2.5-Encoder-230M and LFM2.5-Encoder-350M. Here's what's actually interesting: 1. They converted a decoder instead of training from scratch Both models start from the LFM2.5 decoder backbones. Three changes turn them into encoders: the causal mask is replaced with a bidirectional one, the short convolutions are made non-causal with symmetric center padding, and training uses masked language modeling at 30% instead of BERT's 15%. 2. The CPU number is the whole pitch → ~28s per forward pass at 8,192 tokens for Encoder-230M → over 1 min 30s for ModernBERT-base on the same input → 8,192 tokens is roughly 13 to 15 pages 3. The rankings hold up → Encoder-350M: 4th of 14 models, 81.02 on a 17-task suite → Encoder-230M: 6th at 79.29, above ModernBERT-base at 78.19 → The three models ahead of the 350M are all larger, one nearly 10x its size At 8,192 tokens, ModernBERT-base takes over a minute and a half per forward pass versus about 28 seconds for LFM2.5-Encoder-230M, which is about 3.7x faster. Full analysis: https://www.marktechpost.com/2026/07/29/liquid-ai-releases-lfm2-5-encoder-230m-and-lfm2-5-encoder-350m-bidirectional-encoders-that-stay-fast-at-8k-context-on-cpu/ Model weights (LFM2.5-Encoder-350M): https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M Model weights (LFM2.5-Encoder-230M): https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M Technical details: https://www.liquid.ai/blog/lfm2-5-encoders

Sakana AI Releases Fugu-Cyber: An Orchestration Model Reporting 86.9% on CyberGym and 72.1% on CTI-REALM It is not a new frontier model. It is a third endpoint on the Fugu orchestrator, tuned for security reasoning. Here's what's actually interesting. 1. The CyberGym number only means something with context → Fugu-Cyber: 86.9% → GPT-5.5-Cyber: 85.6% → Claude Mythos Preview: 83.1% → Best agent in the original CyberGym paper: ~20% The benchmark asks an agent to write a PoC that crashes the pre-patch build but not the post-patch build. 1,507 instances, 188 OSS-Fuzz projects. Sakana's score is a small step past the reported frontier, not a leap. The leap already happened. 2. The CTI-REALM figure is a different metric than it sounds Microsoft scores CTI-REALM as a 0–1 trajectory reward, not pass/fail. Its own eval put the top three configs at 0.624–0.685. Sakana reports 72.1% and calls it a success rate. Read it as reward 0.721. 3. Detection engineering still breaks on cloud Microsoft's per-platform means across evaluated models: → Linux endpoints: 0.585 → AKS: 0.517 → Azure cloud: 0.282 4. The pricing is a clean 1.2× → $6 input / $36 output / $0.60 cached, per 1M tokens → All three double above 272K context → Exactly 20% over Fugu-Ultra on every line Access is gated — manual approval, defensive-use AUP, Token Plan only, no EU/EEA, no weights. Full analysis: https://www.marktechpost.com/2026/07/25/sakana-ai-releases-fugu-cyber-orchestration-model-cybergym-cti-realm/ Technical details: https://sakana.ai/fugu-cyber-release/

Meet Open Dreamer: A JAX/Flax Reproduction of the Dreamer 4 World Model Pipeline, With the Full Training Recipe Published No VAE. No KL loss. No adversarial loss. Here's how it works: 1. Two models, one backbone A causal video tokenizer and an action-conditioned dynamics model share the same block-causal transformer. Space layers move information inside a frame. Causal time layers move it between frames. 2. The tokenizer is a Masked Autoencoder, not a VAE Masking makes the latent space more diffusible, so no KL or adversarial term is needed. → ~100× compression, 512 latent tokens at width 16 per frame → 360×640 frames padded to 368×640 for clean 16×16 patches 3. The rollout is folded into blocks Each timestep is (previous action, state, policy). Spatial attention runs inside the block, causal time attention across blocks. World-model tokens cannot read the agent token, so policy information reaches future states only through the next action. → 1.6B params, depth 30, d_model 1920, 30 heads / 3 KV heads 4. Stability, not throughput, was the bottleneck Most failures happened while the loss was still going down. MSE improves smoothly, generation quality degrades. → Muon replaced LaProp, which spiked randomly and increasingly often → ~400 B200 hours per optimizer comparison run 5. The numbers (B200 dynamics training) → 57–58% MFU, against 60% described as very healthy → 292 FLOP/byte roofline crossover, 256 frames per GPU to clear it → ~24 GiB model state, activations were the real memory cost → plain data parallelism beat FSDP, tensor and sequence parallelism Full analysis: https://www.marktechpost.com/2026/07/25/meet-open-dreamer-a-jax-flax-reproduction-of-the-dreamer-4-world-model-pipeline-with-the-full-training-recipe-published/ Research and Demo: https://next-state.github.io/open-dreamer/ Code: https://github.com/next-state/open-dreamer

Claude Opus 5 is out. The agentic numbers, not the coding ones, are the story: • FrontierBench v0.1 → 43.3% vs Opus 4.8's 18.7% • OSWorld 2.0 → 70.57% vs 55.7% • AutomationBench → 26.0% vs 17.0% (Fable 5: 17.4%) • ARC-AGI-3 → 30.16%, ~4x the previous best on the leaderboard Same $5/$25 pricing. Fable 5 still edges it on SWE-bench Pro (80.0 vs 79.2). Full analysis: https://www.marktechpost.com/2026/07/24/meet-the-new-claude-opus-5-frontier-class-agentic-coding-and-computer-use-at-unchanged-opus-pricing/ Technical details: https://www.anthropic.com/news/claude-opus-5 Paper: https://www-cdn.anthropic.com/c5fbac3f0b1280a933ebd26d3cb8bb9f5bdeaf48/Claude%20Opus%205%20System%20Card.pdf#page=73

Andrew Ng Just Released OpenWorker: An Open-Source, Local-First Desktop AI Coworker That Returns Finished Deliverables Instead of Chat Here are some key takeaways: 1. The approval layer is typed, not cosmetic Most desktop agents bolt approvals onto the UI. OpenWorker classifies every tool call into one of four risk classes before it runs: → read — no side effects, always allowed → write_local — mutates the workspace, path-scoped → exec — runs commands → external — side effects off the machine Five permission modes then decide what happens: discuss, plan, interactive (default), auto, custom. 2. Unattended ≠ more autonomous Unattended mode doesn't raise the autonomy ceiling. It only reroutes approval prompts to an Inbox and suspends the run until a human answers. Autonomy and attention are separate axes — most agent frameworks conflate them. 3. No inference service, by design You bring a key or run local: → 30 curated models, 13 providers → Native OpenAI / Anthropic / Google, open-weight via Together and Fireworks → Fully local via Ollama, no key → Matrix limited to tool-calling models — anything else stalls the agent loop The stack → Tauri 2 + React shell over a local Python FastAPI server (127.0.0.1:8765) → 35 connectors live, plus any MCP server → Built on aisuite, ~32.4k lines of Python, 78 test modules Full analysis: https://www.marktechpost.com/2026/07/23/andrew-ng-just-released-openworker-an-open-source-local-first-desktop-ai-coworker-that-returns-finished-deliverables-instead-of-chat/ Repo: https://github.com/andrewyng/openworker Project: https://openworker.com/

Meet Gigatoken: A Rust BPE Tokenizer that Encodes Text at 24.53 GB/s on a 144-core AMD EPYC 9565, against 24.8 MB/s for HuggingFace tokenizers and 36.0 MB/s for tiktoken on the same machine Both baselines are multithreaded Rust implementations. The difference comes from how the work is structured, not the language. 1. Pretokenization without a regex engine Most tokenizers delegate pretokenization to a regex engine. Gigatoken implements it directly: → A 256-byte lookup table classifies the first byte in O(1), replacing alt/backtrack dispatch → SWAR loads 8 bytes as a u64 and checks all 8 for the letter property with branchless arithmetic → Two independent cursors run from a safe split point, so the out-of-order engine overlaps their instruction streams The repo's optimization log records the progression on single-threaded GPT-2 pretokenization: fancy-regex at 47 MiB/s, NEON at 462, LUT + SWAR at 830, dual-cursor at 1,049 MiB/s. 2. Pretoken caching Words seen before are looked up rather than re-encoded through BPE. The author notes this is the hard part: the cache grows quickly and pretoken distributions are long-tailed. 3. Measured results across hardware GPT-2 on the 11.9 GB OpenWebText corpus: → EPYC 9565 (144 cores): 24.53 GB/s → Apple M4 Max (16 cores): 8.79 GB/s → Ryzen 7 9800X3D (16 cores): 6.27 GB/s Methodology note: Gigatoken encodes the full file un-split and finds its own boundaries. HuggingFace tokenizers gets the first 100 MB and tiktoken the first 1 GB, both presplit on <|endoftext|>. Best of 3 interleaved rounds, fresh process per measurement. 4. Relevant workloads Pretraining data preparation, where a corpus is retokenized on each mixture or filter change. And time-to-first-token in serving: vLLM and SGLang hash token chunks into prefix trees, so tokenization runs before the KV-cache lookup. Full analysis: https://www.marktechpost.com/2026/07/23/meet-gigatoken-a-rust-bpe-tokenizer-that-encodes-text-at-24-53-gb-s-up-to-989x-faster-than-huggingface-tokenizers/ GitHub Repo: https://github.com/marcelroed/gigatoken/#benchmarks

Poolside released Laguna S 2.1 last week, and the interesting part is not the benchmark table. It is what fits in memory. It is a 118B-parameter Mixture-of-Experts coding model that activates ~8B parameters per token. Roughly 6.8% of the network fires on any given step. 1. The weight-class claim → 78.5% SWE-Bench Multilingual — tops Poolside's published table outright → 70.2% Terminal-Bench 2.1 — first among open, disclosed-size models → 59.4% SWE-Bench Pro → 40.4% DeepSWE v1.1, against DeepSeek-V4-Pro-Max at 9.0% with ~6× the active parameters Closed frontier models still lead several of these. Claude Fable 5 hits 80.3% on SWE-Bench Pro. The claim is the weight class, not the top of the board. 2. Thinking mode is doing the heavy lifting Two modes only: off and max, with max as default. No user-configurable effort control yet. → Terminal-Bench 2.1: 60.4% → 70.2% → DeepSWE v1.1: 16.5% → 40.4% → Cost: DeepSWE trajectories go from ~99k to ~249k completion tokens That is a real inference bill, not a free lunch. Worth modelling before you switch it on in production. 3. Sizing it correctly This is where teams get MoE wrong. Every expert stays resident, so you size on 118B, not 8B. → 4-bit (NVFP4/INT4): ~59 GB — fits one NVIDIA DGX Spark (128 GB) → FP8: ~118 GB — one Spark or one H200 → BF16: ~236 GB — two linked Sparks or a multi-GPU node Day-one support for vLLM, SGLang, and Ollama. Hosted on OpenRouter at $0.10 / $0.20 / $0.01 per 1M input / output / cache-read tokens. ..... Full analysis: https://www.marktechpost.com/2026/07/21/poolside-releases-laguna-s-2-1/ Technical details: https://poolside.ai/blog/introducing-laguna-s-2-1 Trajectories: https://trajectories.poolside.ai/ Technical report: https://poolside.ai/assets/laguna/laguna-m1-xs2-technical-report.pdf

NVIDIA just put a full world model — perception, prediction, and action — inside a 4B model that runs on the robot itself, no cloud round-trip. I spent some time analyzing the Cosmos 3 Edge release. Here is what stood out to me, and why it matters for anyone building physical AI. 𝟭. 𝗢𝗻𝗲 𝗺𝗼𝗱𝗲𝗹 𝘀𝗽𝗮𝗻𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗮𝗰𝘁𝗶𝗼𝗻 A world model learns how an environment changes over time — objects, motion, and the effects of actions. Cosmos 3 Edge brings that on-device, so a system can read the current state, simulate a likely future, and connect that future to an action. 𝟮. 𝗧𝘄𝗼 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝘁𝗼𝘄𝗲𝗿𝘀, 𝗼𝗻𝗲 𝘀𝗵𝗮𝗿𝗲𝗱 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 It uses a Mixture-of-Transformers design. → Autoregressive tower (reasoner): vision + text tokens, causal attention → Diffusion tower (generator): vision + audio + action tokens, broad context attention The towers keep separate norm layers and MLPs, but share multimodal attention. So the model reasons about a scene before it generates anything. 𝟯. 𝗔 𝗰𝗼𝗺𝗺𝗼𝗻 𝗮𝗰𝘁𝗶𝗼𝗻 𝘀𝗽𝗮𝗰𝗲 𝗮𝗰𝗿𝗼𝘀𝘀 𝗲𝗺𝗯𝗼𝗱𝗶𝗺𝗲𝗻𝘁𝘀 Actions are encoded as compact geometric vectors — translation, rotation, manipulation state — so control maps directly to pixel changes. → camera / autonomous vehicle: 9D → single-arm robot: 10D · dual-arm: 20D → egocentric: 57D · humanoid: 29D 𝟰. 𝗣𝗼𝗹𝗶𝗰𝘆 𝗺𝗼𝗱𝗲 𝗿𝘂𝗻𝘀 𝗶𝗻 𝗯𝗼𝘁𝗵 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻𝘀 Current state in → action + expected visual consequence out. Run it the other way and it infers the action from an observed change. That is what connects world modeling to policy training and evaluation. 𝟱. 𝗢𝗻-𝗱𝗲𝘃𝗶𝗰𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 → 4B params (2B dense reasoner) → 640×360 robot-control resolution → 32 actions per inference on Jetson Thor → 15 Hz real-time control loop → runs on Jetson (T2000 / T3000 / Thor), RTX PRO, GeForce RTX, DGX → #1 on VANTAGE-Bench for vision analytics among 4B models (vendor-stated — benchmark on your own scenes) 𝟲. 𝗪𝗵𝗮𝘁 𝘀𝗵𝗶𝗽𝘀 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝗶𝘁 → Cosmos 3 Edge Policy (DROID): a pick-and-place manipulation policy, with post-training scripts → Cosmos 3 Super 4-Step Distillation: cuts diffusion from 35–50 denoising steps to 4, up to 25× faster for text-to-image and image-to-video → post-train for your embodiment and sensors in about a day on an H100 cluster or DGX Station Full analysis: https://www.marktechpost.com/2026/07/21/nvidia-releases-cosmos-3-edge-a-4b-parameter-open-world-model-that-reasons-and-generates-robot-actions-on-device/ Model weight: https://huggingface.co/nvidia/Cosmos3-Edge Technical details: https://huggingface.co/blog/nvidia/cosmos3edge?linkId=100000431533160