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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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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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