451 summarised stories about Large Language Models, each linking back to the original source. Browse all topics →
SiliconANGLE 1 month ago 41
Deep Cogito announced a $43 million Series A led by TQ Ventures to build self-improving AI models. Its open-source LLM Cogito v2.1 671B debuted in November and was designed to use fewer tokens than comparable reasoning models. The funding will be used to grow its enterprise platform business, expand its research team, and release additional open-source models.
Amazon Science 1 month ago 28 ● 2 sources
Researchers propose a dependence-aware way to aggregate outputs from multiple LLM judges so that agreement counts don’t overstate independent evidence when judges share biases. The method’s strongest results using 10 judge models at temperature 0 reach 0.912 accuracy for relevance, up from 0.820 with weighted majority vote and 0.804 with uniform majority vote. As a result, evaluation scores adjust for learned pairwise correlation between judges rather than assuming independent errors, improving accuracy across three binary tasks.
Trending Topics 1 month ago 39 ● 3 sources
Z.ai confirmed to Bloomberg that the model circulating as “Ox Alpha” is a new GLM-series iteration from the company (formerly Zhipu AI). The weights are due to be published tonight, making Ox Alpha an open-weight release. This extends Z.ai’s push for frequent open-weight GLM releases focused on coding and agent use, and it should strengthen its standing among open models if official benchmarks match community tests.
Ars Technica 1 month ago 21 ● 5 sources
IBM released Granite 4.2, a new set of open-weight, decoder-only large language models meant to be downloaded and self-hosted. The lineup includes 3B, 8B, and 30B parameter variants with a 128,000-token context window. The 8B and 30B models add an agentic reinforcement learning stage for capabilities like terminal use and web/tool interaction, expanding what these smaller local models can do.
MIT Technology Review 1 month ago 14
AI models have performed poorly on multiple puzzle-based intelligence tests, especially those involving spatial/visual reasoning and small changes that break memorized patterns. In late 2024, top models solved only 18% of the New York Times Connections puzzles. The article changes by reframing puzzles as a way to pinpoint specific model weaknesses and invites readers to try the same challenges that tripped up AI.
MarkTechPost 1 month ago 13 ● 5 sources
IBM released Granite 4.2, an open family of reasoning language models that can switch between thinking, non-thinking, and low-effort modes. The models were pre-trained from scratch on roughly 15 trillion tokens and come in 3B, 8B, and 30B parameter sizes. The 8B and 30B are additionally trained with agentic reinforcement learning for code editing, terminal control, and web searches in sandboxed environments, while all models ship under Apache 2.0.
Apple Machine Learning Research 1 month ago 14
The paper proposes an integrated enlarge-and-prune pipeline for pretraining generative language models before structured pruning. It frames the work around efficiency under limited inference budgets and emphasizes token efficiency versus training target-size models from scratch. As a result, pruning methods explicitly include enlarged model pretraining and aim to determine when this step is worthwhile even if the enlarged model is not deployed.
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19 October 2026
Amazon’s biggest push against Nvidia this year landed today in the form of TorchNeuron, an AWS development tool designed to let PyTorch-based AI apps run on its Trainium chips with a one-line configuration change instead of a code rewrite. AWS says roughly 85% of AI apps use PyTorch, and that number matters because it turns the usual “porting tax” into a packaging decision: keep writing in PyTorch, switch the backend, move on. The company also plans upstream PyTorch changes to make the framework more hardware-agnostic, aiming to loosen the gravity that keeps developers clustered around specific accelerator ecosystems—an underappreciated lever in enterprise spending, where platform lock-in often lasts longer than model lifecycles.
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