81 summarised stories about Mixture-of-Experts, each linking back to the original source. Browse all topics →
SiliconANGLE 1 week ago 11 ● 12 sources
Mistral AI SAS released Mistral Large 4 for public preview and said it plans to publish the model weights later this month. The model has 1 trillion total parameters with 49 billion activated per prompt. Mistral Large 4 is positioned to power follow-on larger models and a series of use-case optimized models.
Reflection 1 week ago 16 ● 6 sources
Reflection introduced Beam, its first open-weight sparse mixture-of-experts model for coding, reasoning, and agentic workloads. Beam totals 501 billion parameters with 23 billion active per token, trained with 10.5K NVIDIA GB300 GPUs over 4 weeks to generate 100 million+ rollouts. After red-teaming and evaluations, Reflection will release Beam’s weights and related developer materials for early access and a later full release this month.
Trending Topics 1 week ago 35 ● 6 sources
Reflection AI launched Beam, its first open-weight text model intended to compete with top Chinese open models. Beam has 501 billion total parameters, with 23 billion active per token, and it will release its weights later this month under the Apache 2.0 license. Independent benchmarks and broader evaluation will change once the weights are published, while early access via a waitlist begins as red-teaming and evaluations finish.
Latent Space 1 week ago 23 ● 6 sources
Reflection launched Beam, a text-only 501B-total / 23B-active MoE model for coding, agentic, and scientific work, trained from scratch. Beam is described as using 23.8T pretraining tokens and plans to release full weights under Apache 2.0 this month. The release adds a new open-weight option to the US-trained coding model ecosystem and pushes further comparisons against GLM 5.3 and other recent releases.
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14 October 2026
Sakana AI added a new transparency layer to Sakana Chat with “My Fish,” letting users see what the chatbot remembers, when those memories get used, and how related facts connect. The interface surfaces memory activity as small “chips” during responses and backs it up with a heatmap of memory use plus read/write history and a connection map. It’s a practical answer to a familiar problem: people don’t just want outputs from AI systems, they want a way to audit what the system is carrying forward.
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