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The article reports that Ant's Ring-2.6, an open-weight trillion-parameter model under MIT license, reportedly matches closed frontier models on reasoning and agent benchmarks, raising questions about the impact of open models catching up.
Richard Sutton launches Oak Lab, a new AI research lab focused on real-time learning and planning algorithms, with the ambitious goal of creating a trillion-parameter agent that operates on just 20 watts of energy.
Meituan released LongCat-2.0, a 1.6 trillion parameter AI model trained entirely on domestic Chinese chips, claiming it matches or exceeds leading proprietary models on coding and agent benchmarks.
Meituan released LongCat-2.0, a 1.6T-parameter MoE model with 1M context, claimed as the first to train on a 50,000-GPU Chinese cluster, now available on OpenRouter for agentic coding.
This technical report presents Ling-2.6 and Ring-2.6, a family of trillion-parameter models designed for efficient and instant agentic intelligence, featuring architectural upgrades like hybrid linear attention and specialized training methods including KPop reinforcement learning. All checkpoints are open-sourced.
Prime Intellect released prime-rl v0.6.0, enabling reinforcement learning at trillion-parameter MoE scale with sub-5-minute step times and optimized inference, training, and rollout.
This technical report introduces Ling and Ring 2.6, a family of large language models at the trillion-parameter scale designed for efficient and instant agentic intelligence.
Xiaomi and TileRT achieved over 1,000 tokens per second inference on a 1-trillion parameter model using standard commodity GPUs, suggesting a major alternative to custom silicon.
Xiaomi achieved over 1,000 tokens per second inference on its trillion-parameter MiMo-V2.5-Pro-UltraSpeed model using commodity 8-GPU nodes via FP4 quantization and DFlash speculative decoding, outpacing GPT-5.5 and Claude Opus by over 10x.
Xiaomi MiMo releases MiMo-V2.5-Pro-UltraSpeed, achieving over 1,000 tokens per second on a 1 trillion parameter model using speculative decoding, the first practical deployment of such speed at scale.
Xiaomi released MiMo-V2.5-Pro-UltraSpeed in collaboration with TileRT, achieving over 1000 tokens/s decode speed on a 1-trillion-parameter model, enabling real-time AI interaction and accelerating coding agents and reasoning tasks.
A discussion on where to allocate reasoning budget in AI agents, referencing the trillion-parameter Ring-2.6-1T model with high/xhigh reasoning-effort modes.
Discussion about routing failure classes (bad tool choice, bad replanning, final-answer verification) to Ring-2.6-1T, a trillion-parameter reasoning model for agent workflows with high reasoning-effort modes.
A reflection on the trade-offs between using a single trillion-parameter reasoning model with adjustable depth (like Ring-2.6-1T) versus routing between separate specialized models, exploring which approach is cleaner or more cost-effective for agent workflows.
Cerebras is now running Kimi K2.6, a trillion-parameter model, in enterprise trials at ~1,000 tokens/s, the fastest frontier model performance ever measured by Artificial Analysis.
Cerebras announces it is running Kimi K2.6, a trillion parameter model, at approximately 1,000 tokens per second in enterprise trials, claiming the fastest frontier model performance ever measured by Artificial Analysis.
The article discusses how the next important model advancement may be about reducing the cost of agent workflows, highlighting Ant Group's Ling-2.6-1T as a trillion-parameter model designed for efficient reasoning and task execution with low compute overhead.
inclusionAI releases Ring-2.6-1T, a trillion-parameter reasoning model with enhanced agent execution, a reasoning effort mechanism, and an asynchronous RL training paradigm, aimed at complex real-world tasks.