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Thursday

  1. Latent SpaceSelected

    Google DeepMind releases Gemini 4 Carbon, topping Artificial…

    Google DeepMind has released Gemini 4 Carbon, which ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59 and leads in 4 of 9 evaluations including HLE-Diamond and TB-Science. The model focuses on cybersecurity and long-horizon tasks, claiming 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench, and is initially available only to trusted cybersecurity personnel through the Fairwind Program. In terms of specifications, it supports a 1M token context and 1M maximum output, with input covering text, images, video, and speech, and output in text; Artificial Analysis lists a standard price of $4/$20 per million input/output tokens, plus a 50% launch discount.

    Google DeepMind has released Gemini 4 Carbon, which ranks first on the Artificial Analysis Intelligence Index v5 with a score of 59 and leads in 4 of 9 evaluations including HLE-Diamond and TB-Science. The model focuses on cybersecurity and long-horizon tasks, claiming 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench, and is initially available only to trusted cybersecurity personnel through the Fairwind Program. In terms of specifications, it supports a 1M token context and 1

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  2. Baseten Engineering Blog

    Baseten Optimizes Qwen3-ASR Batch Transcription, Boosting Single-GPU Throughput by 7x

    The Baseten engineering team, through native audio decoding, KV cache FP8 quantization, and reduced host-device synchronization, increased batch transcription throughput for Qwen3-ASR 1.7B on a single RTX PRO 6000 by 7x and reduced latency by 6.9x. After optimization, the transcription cost for one hour of audio is approximately $0.0013, i.e., 1/10 of a cent per audio hour, with no significant change in word error rate. The optimized version has been made the default deployment in the Baseten Model Library and can be called through an OpenAI-compatible transcription client.

    How Baseten gets 7x throughput on Qwen3-ASR batch transcription

    The Baseten engineering team, through native audio decoding, KV cache FP8 quantization, and reduced host-device synchronization, increased batch transcription throughput for Qwen3-ASR 1.7B on a single RTX PRO 6000 by 7x and reduced latency by 6.9x. After optimization, the transcription cost for one hour of audio is approximately $0.0013, i.e., 1/10 of a cent per audio hour, with no significant change in word error rate. The optimized version has been made the default deployment in the Baseten Mo

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  3. Ed Zitron long-form article / Where's Your Ed At

    Ed Zitron's Long-Form Article Questions Anthropic IPO: S-1 Conceals Q3 Data, $430 Billion Compute Commitment About to Take Effect

    In his long-form article, Ed Zitron analyzes Anthropic's S-1, arguing that the document only discloses Q2 2026 data, and that the quarter's $974 million in profit came from two months of SpaceX compute discounts, while Q3 revenue, cash balance, and payment schedule were not disclosed. He tallies Anthropic's non-cancelable compute commitments with Google, Amazon, Microsoft, SpaceX, Broadcom, and others at approximately $269.4 billion, with an additional $125 billion to $150 billion in TPU purchase commitments not included in the contract table, and notes that Q3 revenue grew 45% quarter-over-quarter to $16.8 billion, while compute spending grew 76.3% to $9.88 billion. The article also mentions that 5% to 7% of revenue comes from customers found to have violated rules by using its models to train their own models, as well as lock-up arrangements that allow insiders to sell down relatively quickly.

    In his long-form article, Ed Zitron analyzes Anthropic's S-1, arguing that the document only discloses Q2 2026 data, and that the quarter's $974 million in profit came from two months of SpaceX compute discounts, while Q3 revenue, cash balance, and payment schedule were not disclosed. He tallies Anthropic's non-cancelable compute commitments with Google, Amazon, Microsoft, SpaceX, Broadcom, and others at approximately $269.4 billion, with an additional $125 billion to $150 billion in TPU purchas

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  4. Phoenix Tech

    Iluvatar CoreX Tianyao 300 Benchmarked Against H200: Training Throughput Reaches 1.33x That of H100

    Iluvatar CoreX held its first live demonstration of the Tianyao 300 at a media open day on October 28, claiming that in comparative tests on large language models such as GLM-5.3 as well as text-to-image and text-to-video scenarios, it outperformed the H200 overall. On the training side, in an 8-GPU full-system FP8-precision demonstration based on Qwen3-32B, the Tianyao 300 achieved per-GPU effective throughput 1.33 times that of the H100. The Tianyao 300 was unveiled at this year's World Artificial Intelligence Conference in July, features 144GB of memory, and its IXL144 supernode integrates 144 GPUs with roughly 10.1TB of memory in a single standard rack, reducing the cost per million effective tokens by about 40% compared with mainstream international solutions.

    Iluvatar CoreX held its first live demonstration of the Tianyao 300 at a media open day on October 28, claiming that in comparative tests on large language models such as GLM-5.3 as well as text-to-image and text-to-video scenarios, it outperformed the H200 overall. On the training side, in an 8-GPU full-system FP8-precision demonstration based on Qwen3-32B, the Tianyao 300 achieved per-GPU effective throughput 1.33 times that of the H100. The Tianyao 300 was unveiled at this year's World Artifi

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  5. The Rundown AI

    Google releases Gemini 4 Carbon, topping 15 of 17 benchmarks

    Google has released Gemini 4 Carbon, a frontier model aimed at cybersecurity and long-horizon coding tasks, topping 15 of Google's own 17 benchmarks, but it has not yet been made public and is available only to some cybersecurity defenders. It scores 59 on Artificial Analysis' Intelligence Index, surpassing Claude Opus 5.5 and up 9 points from Argon four weeks ago; on long-input reasoning it leads OpenAI Astra and Opus by 24 points. The introductory API price is $2 per million input tokens and $10 per million output tokens, half that of Opus 5.5, doubling after the discount period to Opus's rates. Google has not announced when it will broaden access.

    Google has released Gemini 4 Carbon, a frontier model aimed at cybersecurity and long-horizon coding tasks, topping 15 of Google's own 17 benchmarks, but it has not yet been made public and is available only to some cybersecurity defenders. It scores 59 on Artificial Analysis' Intelligence Index, surpassing Claude Opus 5.5 and up 9 points from Argon four weeks ago; on long-input reasoning it leads OpenAI Astra and Opus by 24 points. The introductory API price is $2 per million input tokens and $

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  6. Mistral AISelected

    Mistral Releases Open Weights for Mistral Large 4 (Le Chonk)

    Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model supports three reasoning modes, uses a modified MIT-based Mistral Ecosystem Open-Weight license, and is priced via API at $0.70 per million input tokens, $0.07 for cached input, and $2.10 for output.

    Le Chonk signal

    Mistral has released the open weights for Mistral Large 4 (Le Chonk), a 1-trillion-parameter natively multimodal model that continues reinforcement learning on top of the preview version, with improved coding and defensive cybersecurity capabilities. It scores 19.7% on FrontierCode 2.0 and 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but uses an average of 47% fewer tokens per task; on Terminal-Bench 4.0 it improves by 13 percentage points to 41%. The model sup

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  7. TechSpot

    Microsoft's AI infrastructure spending is $6.5 billion less than expected due to delayed data center deliveries

    Microsoft's capital expenditure (including finance leases) for the September quarter was $45.2 billion, $6.5 billion less than analysts' expected $51.7 billion, but still up about 30% year-over-year. CFO Amy Hood attributed the shortfall to delays in third-party data center capacity deliveries, with the related facilities expected to come online in the second half of Microsoft's fiscal year, while also lowering its calendar 2026 spending forecast from $175 billion to $170 billion. Microsoft added 24 data centers in the quarter and has signed contracts worth more than $80 billion with CoreWeave, Nebius, and Nscale since 2023; Azure revenue grew 48% to $33.2 billion.

    Microsoft's capital expenditure (including finance leases) for the September quarter was $45.2 billion, $6.5 billion less than analysts' expected $51.7 billion, but still up about 30% year-over-year. CFO Amy Hood attributed the shortfall to delays in third-party data center capacity deliveries, with the related facilities expected to come online in the second half of Microsoft's fiscal year, while also lowering its calendar 2026 spending forecast from $175 billion to $170 billion. Microsoft adde

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  8. MarkTechPost

    MiniMax Open-Sources MCode Stack: 17 Agent Skills and 5 Checklists

    On October 28, MiniMax open-sourced MCode Stack under the MIT license, including the 17 SKILL.md skills and 5 checklists its team used to build MiniMax Code, but not the MiniMax Code source code. The skills are grouped into planning and decomposition, context and tools, long sessions and commands, review, and experiments and hotfixes, among which independent-review provides reviewers only with the diff, requirements, and test results. It can be installed into MiniMax Code by copying the skill directories, via a local plugin, or through the plugin marketplace, and can also be installed in Claude Code with /plugin marketplace add MiniMax-AI/mcode-stack; the original article did not publish evaluation results.

    On October 28, MiniMax open-sourced MCode Stack under the MIT license, including the 17 SKILL.md skills and 5 checklists its team used to build MiniMax Code, but not the MiniMax Code source code. The skills are grouped into planning and decomposition, context and tools, long sessions and commands, review, and experiments and hotfixes, among which independent-review provides reviewers only with the diff, requirements, and test results. It can be installed into MiniMax Code by copying the skill di

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  9. MarkTechPostSelected

    Google DeepMind releases Gemini 4 Carbon, focused on cybersecurity defense and coding

    Google DeepMind has launched Gemini 4 Carbon, calling it a frontier model for long-horizon agentic coding and cybersecurity defense, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0. The model achieved 96.2% on the GraphWalks long-context test, and 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, but on OSWorld-2.1 it trailed Claude Opus 5.5's 87.2% with 81.5%. Carbon is currently available only to vetted cybersecurity defenders, with introductory pricing of $2 per million input tokens and $10 per million output tokens, later $4 and $20; the timeline for opening it to developers and the public has not been announced.

    Google DeepMind has launched Gemini 4 Carbon, calling it a frontier model for long-horizon agentic coding and cybersecurity defense, ranking first with 59 points on the Artificial Analysis Intelligence Index v5.0. The model achieved 96.2% on the GraphWalks long-context test, and 88.2%, 54.8%, and 71.0% on SEC-bench Pro, ExploitGym, and CWE-bench, respectively, but on OSWorld-2.1 it trailed Claude Opus 5.5's 87.2% with 81.5%. Carbon is currently available only to vetted cybersecurity defenders, w

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  10. MarkTechPost

    AssemblyAI Releases Its First Speech Synthesis Model, AssemblyAI-TTS-1, at $15 per Million Characters

    AssemblyAI has released its first text-to-speech model, AssemblyAI-TTS-1, and officially opened it up, targeting real-time telephony voice agents, priced at $15 per million characters, with no annual contracts and no concurrency tiers. The model is offered only as a hosted API in us-east-1 and eu-north-1, supports HTTP, WebSocket and OpenAI-compatible interfaces, and comes with more than 4,000 voices, instant voice cloning and 6 languages. The vendor's own testing shows 93.7% entity accuracy and a median time to first byte of 47 ms, but Cekura's independent benchmark measured 159 ms streaming latency and 81.5% reading accuracy, and at launch it does not yet support SSML, speaking-rate control or emotion tags.

    AssemblyAI has released its first text-to-speech model, AssemblyAI-TTS-1, and officially opened it up, targeting real-time telephony voice agents, priced at $15 per million characters, with no annual contracts and no concurrency tiers. The model is offered only as a hosted API in us-east-1 and eu-north-1, supports HTTP, WebSocket and OpenAI-compatible interfaces, and comes with more than 4,000 voices, instant voice cloning and 6 languages. The vendor's own testing shows 93.7% entity accuracy and

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  11. MarkTechPost

    Infini-AI-Lab open-sources the Hermit harness adapter framework

    Infini-AI-Lab has released the open-source framework Hermit, which adapts AI agents by training external harness adapters while keeping the model and harness frozen; v0.1.0 was released on October 25, 2026. The framework is implemented in Python under the Apache-2.0 license and can be installed via pip install hermit-harness, and it includes five evolution methods: GEPA, SkillOpt, MetaWorld Model, Harness², and Continual Harness. On GLM 5.3 Flash, the MWM adapter achieved 0.795 accuracy on the CL-Bench database exploration task, higher than the in-context baseline of 0.580, but most results are self-reported data from a single run.

    Infini-AI-Lab has released the open-source framework Hermit, which adapts AI agents by training external harness adapters while keeping the model and harness frozen; v0.1.0 was released on October 25, 2026. The framework is implemented in Python under the Apache-2.0 license and can be installed via pip install hermit-harness, and it includes five evolution methods: GEPA, SkillOpt, MetaWorld Model, Harness², and Continual Harness. On GLM 5.3 Flash, the MWM adapter achieved 0.795 accuracy on the C

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Wednesday

  1. The Rundown AI

    Google DeepMind releases the Genie 4 world model, which can generate sound effects and converse with characters in the scene

    Google DeepMind has released Genie 4, a new-generation world model that can turn prompts into explorable, realistic scenes in real time, and for the first time the Genie model itself generates background sounds and music, while characters in the scene can also hold real-time conversations. Users move via the keyboard and use text prompts to modify content in the scene instantly, with the scene remaining coherent across multiple inputs. In the demo, a simulated Apptronik Apollo robot plays checkers, with each move planned by Gemini 4 Argon, pointing to Genie's future use in robot training.

    Google DeepMind has released Genie 4, a new-generation world model that can turn prompts into explorable, realistic scenes in real time, and for the first time the Genie model itself generates background sounds and music, while characters in the scene can also hold real-time conversations. Users move via the keyboard and use text prompts to modify content in the scene instantly, with the scene remaining coherent across multiple inputs. In the demo, a simulated Apptronik Apollo robot plays checke

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  2. QbitAI

    LimX Dynamics updates the FluxVLA Engine V2 technical report, adding online reinforcement learning and real-robot deployment evaluations

    LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 percentage points, respectively; the inference frequency of 8 model-and-device combinations increased to 2.31–9.64 times. On the real-robot side, the deployment pipeline from model checkpoint to closed-loop execution was validated on ALOHA, the dual-arm Franka, Rebot, and the self-developed humanoid robot LimX Oli.

    LimX Dynamics has updated the second version of the technical report for its open-source embodied engineering foundation FluxVLA Engine. Compared with the first version in September, it newly adds post-training experiments with online reinforcement learning and expands the coverage of simulation evaluations and real-robot deployment. The report shows that after online reinforcement learning, SmolVLA's success rates on the ten tasks of LIBERO-10 and RoboTwin Clean increased by 10.0 and 15.4 perce

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  3. MarkTechPost

    Google DeepMind Releases Genie 4 Real-Time World Model with Native Audio Generation

    Google DeepMind has released the Genie 4 world model as a research preview to trusted testers; it can generate explorable worlds in real time from text and image prompts, and natively generate ambient sound, music, and character dialogue. The model introduces Gemini 3.8 Flash as a planner within the simulation loop, responsible for breaking down goals, inspecting frames, and replanning, which DeepMind calls an early step toward hybrid world models. Genie 4 is hosted only on Google's platform and will later be opened to AI Ultra users and Gemini API partners; all video and audio carry SynthID watermarks.

    Google DeepMind has released the Genie 4 world model as a research preview to trusted testers; it can generate explorable worlds in real time from text and image prompts, and natively generate ambient sound, music, and character dialogue. The model introduces Gemini 3.8 Flash as a planner within the simulation loop, responsible for breaking down goals, inspecting frames, and replanning, which DeepMind calls an early step toward hybrid world models. Genie 4 is hosted only on Google's platform and

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  4. MarkTechPost

    Polymathic AI and Princeton University Release FlashMap: Achieving 6.14x LLM Decoding Speedup with a Flow-Mapping Drafter

    Polymathic AI, Princeton University, and other institutions have released FlashMap, which replaces discrete diffusion drafters with continuous flow mapping to draft a complete block of 16 tokens in a single forward pass. On Qwen3-8B, it achieves up to 6.14x lossless speedup on GSM8K at temperature 0, with the drafter having only 0.5B parameters. The drafter weights for Qwen3-8B and Qwen3-4B are now available on Hugging Face, along with vLLM and SGLang plugins.

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  5. Rohan Paul

    Google TPUs will run on a hyperscale cloud outside Google for the first time, with Oracle deploying starting in 2027

    Google's TPUs will run on a hyperscale cloud outside Google for the first time, with Oracle deploying capacity starting in calendar year 2027 and gradually expanding over several years. Oracle provides power, cooling, and data center networking; its Acceleron network connects TPU clusters to Oracle's databases and storage, letting enterprises fine-tune and deploy models next to their data without cross-cloud transfers. Google says the eighth-generation TPU delivers up to 12.6 PFLOPS of FP4 compute per chip, achieves over 97% effective cluster uptime during large-scale training, and improves performance per dollar by up to 80% for low-latency serving; its open-source TorchTPU stack lets existing PyTorch code and tools such as vLLM and SGLang run on TPUs without rewriting models.

    Google's TPUs will run on a hyperscale cloud outside Google for the first time, with Oracle deploying capacity starting in calendar year 2027 and gradually expanding over several years. Oracle provides power, cooling, and data center networking; its Acceleron network connects TPU clusters to Oracle's databases and storage, letting enterprises fine-tune and deploy models next to their data without cross-cloud transfers. Google says the eighth-generation TPU delivers up to 12.6 PFLOPS of FP4 compu

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  6. OpenBMB

    OpenBMB Open-Sources MiniCPM-OCR Suite, Featuring a 1B Parsing Model and a 2B Verification Model

    OpenBMB has released the open-source MiniCPM-OCR suite, comprising the 1B MiniCPM-OCR 1.0 and the 2B decision model MiniCPM-OCR Jev. MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2; Jev can perform cross-model verification of text, formulas, and tables, scoring 93.6 on JevOCRBench, surpassing JEV-35B-A3B's 90.1 and approaching Qwen3.5-397B-A17B's 93.8. Powered by DSpark, its speed is 1.49 times that of PaddleOCR-VL-1.6 and 3.62 times that of MinerU2.5 Pro; with SDK concurrency optimization, it can reach 128 pages per minute, and it supports deployment on vLLM, SGLang, llama.cpp, and Ollama.

    OpenBMB has released the open-source MiniCPM-OCR suite, comprising the 1B MiniCPM-OCR 1.0 and the 2B decision model MiniCPM-OCR Jev. MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2; Jev can perform cross-model verification of text, formulas, and tables, scoring 93.6 on JevOCRBench, surpassing JEV-35B-A3B's 90.1 and approaching Qwen3.5-397B-A17B's 93.8. Powered by DSpark, its speed is 1.49 times that of PaddleOCR-VL-1.6 and 3.62 times that of MinerU2.5 Pro; with SDK con

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  7. Unite.AI

    Liquid AI Releases Six 2B-Class Open-Weight Models in the LFM3 Series

    On October 27, Liquid AI released the LFM3 series. All six 2B-parameter-class open-weight models branch from the same LFM3-1.7B-Base, covering text, vision, audio, omni-modal, and decision models. LFM3-1.7B has 1.73B parameters, a 256K token context, supports 16 languages, and adopts a Tri-Hybrid architecture with 13 layers of gated short convolutions, 5 layers of sliding-window attention, and 6 layers of global attention. It is available under the LFM Open License v1.0 on Hugging Face, the Liquid AI Playground, and via API. The company claims that LFM3-1.7B surpasses LFM2.5-2.6B with 35% fewer parameters and occupies about 1 GB of memory at 4-bit precision, but the model card states that it is not suitable for agentic coding or complex multi-step reasoning.

    On October 27, Liquid AI released the LFM3 series. All six 2B-parameter-class open-weight models branch from the same LFM3-1.7B-Base, covering text, vision, audio, omni-modal, and decision models. LFM3-1.7B has 1.73B parameters, a 256K token context, supports 16 languages, and adopts a Tri-Hybrid architecture with 13 layers of gated short convolutions, 5 layers of sliding-window attention, and 6 layers of global attention. It is available under the LFM Open License v1.0 on Hugging Face, the Liqu

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Friday

  1. MarkTechPost

    Prime Intellect Releases Prime Mega MoE Training Kernel, Up to 3.11x Faster in Forward Pass

    Prime Intellect has released Prime Mega MoE, which builds on the Mega MoE kernel from DeepSeek's open-source DeepGEMM library by adding a fused and overlapped backward pass, making it usable for training rather than inference only. In the prime-rl trainer, the full training step for Qwen3 MoE models is 32% to 34% faster than the baseline and 48% to 55% faster than TorchTitan; the single-layer forward pass is up to 3.11x faster (from 12.0 ms to 3.9 ms), but the backward pass for Qwen3-235B-A22B is only 1.24x faster. The kernel supports BF16, MXFP8 on Blackwell, and blockwise FP8 on Hopper, is released with prime-rl under Apache 2.0, and is enabled through the mega_moe dispatch backend.

    Prime Intellect has released Prime Mega MoE, which builds on the Mega MoE kernel from DeepSeek's open-source DeepGEMM library by adding a fused and overlapped backward pass, making it usable for training rather than inference only. In the prime-rl trainer, the full training step for Qwen3 MoE models is 32% to 34% faster than the baseline and 48% to 55% faster than TorchTitan; the single-layer forward pass is up to 3.11x faster (from 12.0 ms to 3.9 ms), but the backward pass for Qwen3-235B-A22B i

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  2. MarkTechPost

    Baseten Releases 2B Decision Model joey-1, Priced at $0.01 per Million Tokens

    Baseten's Base Labs has released a 2B decision model, joey-1, post-trained on Qwen3.5-2B, which directly returns labels and calibrated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 2000-token request; the weights are open-sourced under the MIT license on Hugging Face and can be self-hosted on a single CUDA GPU.

    Baseten's Base Labs has released a 2B decision model, joey-1, post-trained on Qwen3.5-2B, which directly returns labels and calibrated probabilities instead of generating text, targeting high-concurrency routing, classification, and guardrail checks. The model scores 63.30 on the public Jev Decision Index 0.4 set, the highest among models under 4B, but 8.94 points lower than the 9B elephant-1. Model APIs are priced at $0.01 per million input tokens, with a P50 latency of 20 ms for an average 200

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