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CWE-bench v2 public set evaluation results are tracked on the Hub
On November 3, 2026, Hugging Face announced the CWE-bench v2 evaluation results and stated that the dataset, scorer, and harness are all publicly available and can be run directly from the Hub via Harbor. This evaluation covered 11 models: Gemini 4 Argon leads with 67.8% pass@1 and 81.0% pass@4, followed closely by Claude Opus 5.5 with 67.3% pass@1. At least one model solved 95 tasks, but the top model still fails 19% of the time on each attempt. In terms of cost, DeepSeek V4.1 Flash, with 57.0% pass@1 at $0.04 per rollout, forms the Pareto frontier together with Claude Opus 5.5 and Gemini 4 Argon.
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View story:Hugging Face provides the CWE-bench v2 public set evaluation results on the Hub
Sarvam AI releases decision model Sarvam Prism
On November 3, 2026, Sarvam AI released Sarvam Prism, a System One decision model based on Sarvam 105B. According to the Sarvam AI Blog, the model achieved an average score of 71.8 with a median response time of 44 milliseconds on B200 GPUs, the highest score among the 25 decision models tested by Sarvam AI within 100 milliseconds. At confidence levels above 90%, the model's actual error rate was lower than expected: 2.2% versus 3.3% for English, and 4.4% versus 5.4% for Indic languages, and it kept its answers unchanged in a shuffled-options test of 2,000 questions. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API. There are currently no subsequent developments.
AI within 100 milliseconds. At confidence levels above 90%, the model's actual error rate was lower than expected: 2.2% versus 3.3% for English, and 4.4% versus 5.4% for Indic languages, and it kept its answers unchanged in a shuffled-options test of 2,000 questions. The weights are open-sourced under the Apache 2.0 license on Hugging Face, requiring at least 120 GB of VRAM on a single GPU to run in FP8, and it is also available via the Sarvam API. There are currently no subsequent developments.
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Hao AI Lab open-sources the JetSpec++ model, code, and inference engine
On October 31, 2026, the UCSD Hao AI Lab announced that it would fully open-source the JetSpec++ model, code, and inference engine, along with links to the paper, code, and Hugging Face. This speculative decoding framework pipelines draft generation and verification through causal parallel drafting, a hidden-state predictor, and an anchor-token decoder. According to the data it published, on Qwen3-8B, JetSpec++ achieves 1,361 TPS, delivering up to 1.54x end-to-end speedup relative to DSpark and 7.45x relative to autoregressive decoding, while remaining lossless; after combining NVFP4 with kernel optimizations, on two GB200 GPUs, Qwen3-8B exceeds 4,000 TPS and Qwen3.8-27B exceeds 900 TPS. There are currently no further developments.
On October 31, 2026, the UCSD Hao AI Lab announced that it would fully open-source the JetSpec++ model, code, and inference engine, along with links to the paper, code, and Hugging Face. This speculative decoding framework pipelines draft generation and verification through causal parallel drafting, a hidden-state predictor, and an anchor-token decoder. According to the data it published, on Qwen3-8B, JetSpec++ achieves 1,361 TPS, delivering up to 1.54x end-to-end speedup relative to DSpark and
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OpenAI unveils expanded multi-layered misalignment behavior monitoring system
On October 29, 2026, OpenAI announced an expanded frontier model misalignment behavior monitoring system, covering training, evaluation, internal use, and some external deployments. OpenAI:Alignment research blog states that the system uses a four-layer architecture of activation probes, model review, investigation agents, and automated investigation; another OpenAI article states that the system consists of three layers—activation probes, review models, and investigation agents—and has covered all frontier reinforcement learning training and evaluation as well as all internal Codex usage. When replaying the Hugging Face incident, the reinforced monitoring issued an alert about 30 minutes after the agent escaped the sandbox, more than 30 hours earlier than the actual intrusion. In real incidents, the immediate escalation recall rate for security L2 was 79.6%, and the recall rate for the alignment L2 queue was 91.4%.
vation probes, review models, and investigation agents—and has covered all frontier reinforcement learning training and evaluation as well as all internal Codex usage. When replaying the Hugging Face incident, the reinforced monitoring issued an alert about 30 minutes after the agent escaped the sandbox, more than 30 hours earlier than the actual intrusion. In real incidents, the immediate escalation recall rate for security L2 was 79.6%, and the recall rate for the alignment L2 queue was 91.4%.
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Baseten releases 2B decision model joey-1
On October 29, 2026, Baseten's Base Labs released joey-1, a 2B-parameter decision model that directly outputs a single-token label and calibrated confidence. The model was post-trained from Qwen3.5-2B and shares the same data recipe and API as elephant-1 and mammoth-1, so switching requires only a one-line change. According to a Baseten engineering blog post, joey-1 has a P50 end-to-end latency of 20ms, roughly 2.5 times faster than elephant-1 (49ms), and is priced at $0.01 per million tokens, one-tenth that of mammoth-1. The model is now live on Baseten Model APIs with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments unlock a 262k context window and support fine-tuned weights. Unite.AI, citing the model card, reported that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among models with fewer than 4B parameters.
iced at $0.01 per million tokens, one-tenth that of mammoth-1. The model is now live on Baseten Model APIs with zero data retention by default, and its weights are open-sourced on Hugging Face; Model APIs supports a 16k context window, while dedicated deployments unlock a 262k context window and support fine-tuned weights. Unite.AI, citing the model card, reported that joey-1 scored 63.30 on the public Jev Decision Index 0.4 leaderboard, ranking 37th among 230 models and the highest score among
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Tripo AI releases and open-sources the real-time generative renderer EdenLive
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chieve real-time generation at over 24 frames per second at 720p on a single consumer-grade RTX 5090. The open-source content includes the 5-billion- and 14-billion-parameter model weights, real-time video generation code, the EdenGolem v0.1 skill for turning text into simple games, and a complete toolchain from prompts to game front ends. The code is released on GitHub under the Apache 2.0 license, and the weights are available on Hugging Face. No further progress has been disclosed at present.
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Kakao Open-Sources Two Korean-Optimized Embedding Models
On October 29, Kakao open-sourced two Korean-optimized embedding models on Hugging Face: Kanana-2-3b-embedding (3 billion parameters) and Kanana-2-1.3b-embedding (1.3 billion parameters), targeting personalized AI agents and RAG scenarios. Kakao said that in internal evaluations, the two models outperformed llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B of comparable scale in Korean retrieval performance, with comparable English performance and higher inference efficiency, while memory usage and inference speed improved by more than tenfold compared with the previous-generation Kanana embedding models. The models adopt MRL, allowing developers to adjust the embedding vector size, and are released under the Kanana Open License for commercial use. There are currently no further developments.
On October 29, Kakao open-sourced two Korean-optimized embedding models on Hugging Face: Kanana-2-3b-embedding (3 billion parameters) and Kanana-2-1.3b-embedding (1.3 billion parameters), targeting personalized AI agents and RAG scenarios. Kakao said that in internal evaluations, the two models outperformed llama-nemotron-embed-1b-v2 and Qwen3-Embedding-4B of comparable scale in Korean retrieval performance, with comparable English performance and higher inference efficiency, while memory usage
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Polymathic AI and Princeton and other institutions release FlashMap
On October 28, 2026, researchers from Polymathic AI, Princeton University, USC ISI, NYU, and the Flatiron Institute released FlashMap. According to a MarkTechPost report, the method replaces the discrete diffusion drafter with continuous flow mapping, allowing a complete block of 16 tokens to be drafted in a single forward pass. On Qwen3-8B, it achieves up to 6.14x lossless speedup at GSM8K temperature 0, with the drafter having only 0.5B parameters. The drafter weights for Qwen3-8B and Qwen3-4B have been released on Hugging Face, along with vLLM and SGLang plugins. There are currently no further developments.
eleased FlashMap. According to a MarkTechPost report, the method replaces the discrete diffusion drafter with continuous flow mapping, allowing a complete block of 16 tokens to be drafted in a single forward pass. On Qwen3-8B, it achieves up to 6.14x lossless speedup at GSM8K temperature 0, with the drafter having only 0.5B parameters. The drafter weights for Qwen3-8B and Qwen3-4B have been released on Hugging Face, along with vLLM and SGLang plugins. There are currently no further developments.
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Ant Ling Open-Sources Ling-3.1-flash-1019
On October 27, 2026, Ant Ling open-sourced Ling-3.1-flash-1019. According to Artificial Analysis, the model scored 44 on the Artificial Analysis Intelligence Index v4.3, higher than its predecessor's 41; it is a reasoning model with 560B total parameters and 25B activated parameters, supports a 1M token context window, is released under the MIT license, is available as open weights on Hugging Face, and can be accessed through InclusionAI's first-party API. QbitAI reported that this version was further trained and optimized on the basis of Ling-3.1-flash, and that the base model Ling-3.1-flash-base was also open-sourced. On October 30, Ant Ling announced that the model is being made available to more developers through Novita AI, stating that its reasoning capabilities and real-world agent performance are stronger.
s a reasoning model with 560B total parameters and 25B activated parameters, supports a 1M token context window, is released under the MIT license, is available as open weights on Hugging Face, and can be accessed through InclusionAI's first-party API. QbitAI reported that this version was further trained and optimized on the basis of Ling-3.1-flash, and that the base model Ling-3.1-flash-base was also open-sourced. On October 30, Ant Ling announced that the model is being made available to more
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Baidu releases open-source model Unlimited OCR 2
On October 27, 2026, Baidu released and open-sourced the document parsing model Unlimited OCR 2. According to Baidu's official introduction, the model uses a new SR-SWA mechanism to compress the attention interaction between text and vision into a fixed-size working reference, thereby parsing documents of almost arbitrary length within a limited context window; Baidu says that as documents grow longer, per-step decoding latency and memory usage remain nearly constant, and that it achieves SOTA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.
TA performance on MPDocBench and MarathonDocBench, which cover documents ranging from dozens to hundreds of pages. On the same day, QbitAI reported that in a single-sequence test at the hundred-page level, the model reduced peak KV cache from 47GB under standard decoding to 1.77GB, a reduction of about 96%; in a 16-way concurrent test, end-to-end throughput rose from 360 to 1315 tokens/second, about 3.65 times that of standard decoding. The model has been open-sourced on Hugging Face and GitHub.
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SoMark open-sources the 1.3B document parsing model OXR-1.0
SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs a recursive self-improvement (RSI) loop, in which a general-purpose VLM judges parsing results and generates newly annotated pages, eliminating the need for manual annotation. QbitAI reports that the OXR main model open-sourced by the SoMark team has only 1.3B parameters and topped all three benchmark tests, with full parsing speed exceeding that of PaddleOCR-VL-1.6 and MinerU2.5-Pro, which were also evaluated in the same comparison. It covers 13 categories of document elements, including text, tables, formulas, chemical structure formulas, and code blocks, and can parse reading order and output layout coordinates.
SoMark has open-sourced OXR-1.0, a 1.3B-parameter document parsing model that can convert PDFs and page images into Markdown or JSON. The weights have been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. According to Rohan Paul, the model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieving a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU and parsing 100 pages end-to-end in about 101 seconds; its post-training stage employs
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Liquid AI releases six open-weight 2B-class models in the LFM3 series
On October 28, 2026, Liquid AI released the LFM3 series of open-weight models, comprising six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM2-1.2B at an 8K context, scores 38.42 versus 3.02 on WebArena-Verified, and LFM3-VL-2B's average score across 62 vision benchmarks rose from 57.6 to 71.4. All models are available for download on Hugging Face, along with single-GPU fine-tuning notebooks. A separate report the same day stated that the LFM3-1.7B text model outperforms its 2.6B predecessor, uses a 24-layer triple-hybrid architecture, and scores 41.29 on AutomationBench.
ng six model instances in total, covering text, vision, audio, Omni, and the d1 decision model, all branched from LFM3-1.7B-Base. According to the release information published on Hugging Face, LFM3-1.7B-Base adopts a triple-hybrid layer structure, was pretrained on 22T tokens, and in the mid-training stage over the final 3.5T tokens added more agent trajectories and tool-calling data, with the context window extended to 256K tokens; the company states that LFM3-1.7B is about 25% faster than LFM
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Tencent Hunyuan open-sources Chinese-English simultaneous interpretation model Hy-SimulMT
On October 26, 2026, Tencent Hunyuan open-sourced the real-time Chinese-English simultaneous interpretation model Hy-SimulMT, with a latency of about 3 seconds and support for real-time voice cloning; the model has been released on Hugging Face, ModelScope, and GitHub. Tencent Hunyuan officially stated that its performance surpasses most open-source models and commercial APIs. The next day, IT Home reported that the model supports real-time Chinese-English speech-to-speech translation and has been launched for free on Tencent's Hy Translation App; according to the official statement, the measured response time is about 3 seconds, compared with an average of 4 to 5 seconds for professional human interpreters. On RealSI, an open-source long-audio evaluation benchmark, the model achieved the best results among open-source models at the lowest latency, and is said to outperform most commercial simultaneous interpretation APIs; in comparisons with six CATTI-certified simultaneous interpretation experts, it scored higher in translation quality and had lower latency.
e real-time Chinese-English simultaneous interpretation model Hy-SimulMT, with a latency of about 3 seconds and support for real-time voice cloning; the model has been released on Hugging Face, ModelScope, and GitHub. Tencent Hunyuan officially stated that its performance surpasses most open-source models and commercial APIs. The next day, IT Home reported that the model supports real-time Chinese-English speech-to-speech translation and has been launched for free on Tencent's Hy Translation App
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Clément Delangue says developing AI in a closed manner is not safe
On October 26, 2026, Hugging Face CEO Clément Delangue commented on a report, saying that if one believes concentrating AI in a closed environment makes it safe, that judgment needs updating. The report he cited said that a group of former DOGE cybersecurity researchers used AI agents to penetrate Anthropic's systems in less than 12 hours, including the Slack channels where the most sensitive models were discussed. The next day, the security startup Cathedral was reported to have used AI agents in a security test authorized by Anthropic to enter its sales, HR, and IT systems through third-party enterprise software vulnerabilities in less than 12 hours, accessing meeting transcripts, compensation and hiring details, and internal Slack messages. Anthropic confirmed the intrusion was real but stressed that messages customers sent to Claude, core code, and model weights were not leaked, and said its monitoring system had detected anomalies and cut off access before the report was submitted.
On October 26, 2026, Hugging Face CEO Clément Delangue commented on a report, saying that if one believes concentrating AI in a closed environment makes it safe, that judgment needs updating. The report he cited said that a group of former DOGE cybersecurity researchers used AI agents to penetrate Anthropic's systems in less than 12 hours, including the Slack channels where the most sensitive models were discussed. The next day, the security startup Cathedral was reported to have used AI agents
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MiniMax adds 11 community contributions to the H3 ecosystem index
On October 26, 2026, MiniMax announced 11 new community contributions to the H3 video generation model ecosystem and has added them to the H3 ecosystem index. According to the official statement, these contributions cover 8-step reference-conditioned video plus audio, 4-step distillation, sparse attention, and reference token compression, and are integrated into deployment and workflows on GPU servers, TPU, and Apple Silicon. MiniMax said these improvements bring lower experimentation costs, finer creative control, and more runtime environment options, and thanked haoailab, Hugging Face, and teams including PTD, DMAD, SparkH3, TorchTPU, and FlashML. On the same day, MiniMax Design (H3) said the community continues to open up new possibilities for H3, with the ecosystem developing faster than expected, bringing faster generation, better control, and operation on more platforms.
on GPU servers, TPU, and Apple Silicon. MiniMax said these improvements bring lower experimentation costs, finer creative control, and more runtime environment options, and thanked haoailab, Hugging Face, and teams including PTD, DMAD, SparkH3, TorchTPU, and FlashML. On the same day, MiniMax Design (H3) said the community continues to open up new possibilities for H3, with the ecosystem developing faster than expected, bringing faster generation, better control, and operation on more platforms.
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Ollama launches speech model Phonon, supporting text-to-speech
Ollama launched its speech model Phonon on October 24, 2026, beginning support for text-to-speech. According to the Ollama Blog, Phonon is a series of small text-to-speech models from Gradium, currently available on macOS, with Windows and Linux support coming soon. On a MacBook Pro with M5 Max, Ollama uses MLX acceleration to run 35% faster than Gradium's own runtime, with word error rate on par with the Hugging Face Transformers reference implementation. Phonon is compatible with OpenAI's /v1/audio/speech interface, so existing OpenAI SDK code only needs its base URL changed. On licensing, phonon is released under Apache 2.0, while the 40M model uses the CC BY-NC 4.0 license and is restricted to non-commercial use. There are currently no subsequent developments.
indows and Linux support coming soon. On a MacBook Pro with M5 Max, Ollama uses MLX acceleration to run 35% faster than Gradium's own runtime, with word error rate on par with the Hugging Face Transformers reference implementation. Phonon is compatible with OpenAI's /v1/audio/speech interface, so existing OpenAI SDK code only needs its base URL changed. On licensing, phonon is released under Apache 2.0, while the 40M model uses the CC BY-NC 4.0 license and is restricted to non-commercial use. Th
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