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MiniMax has officially open-sourced MiniMax M2.7, making the model weights publicly available on Hugging Face. Originally announced on March 18, 2026, MiniMax M2.7 is the MiniMax’s most capable open-source model to date — and its first model to actively participate in its own development cycle, a meaningful shift in how large language models are built
Liquid AI just released LFM2.5-VL-450M, an updated version of its earlier LFM2-VL-450M vision-language model. The new release introduces bounding box prediction, improved instruction following, expanded multilingual understanding, and function calling support — all within a 450M-parameter footprint designed to run directly on edge hardware ranging from embedded AI modules like NVIDIA Jetson Orin, to mini-PC
Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache
Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step
Deploying a deep learning model into production has always involved a painful gap between the model a researcher trains and the model that actually runs efficiently at scale. TensorRT exists, Torch-TensorRT exists, TorchAO exists — but wiring them together, deciding which backend to use for which layer, and validating that the tuned model still produces
Meta Superintelligence Labs recently made a significant move by unveiling ‘Muse Spark’ — the first model in the Muse family. Muse Spark is a natively multimodal reasoning model with support for tool-use, visual chain of thought, and multi-agent orchestration. https://ai.meta.com/static-resource/muse-spark-eval-methodology What ‘Natively Multimodal’ Actually Means When Meta describes Muse Spark as ‘natively multimodal,’ it means
A deep neural network can be understood as a geometric system, where each layer reshapes the input space to form increasingly complex decision boundaries. For this to work effectively, layers must preserve meaningful spatial information — particularly how far a data point lies from these boundaries — since this distance enables deeper layers to build
Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context Read More »
Writing a research paper is brutal. Even after the experiments are done, a researcher still faces weeks of translating messy lab notes, scattered results tables, and half-formed ideas into a polished, logically coherent manuscript formatted precisely to a conference’s specifications. For many fresh researchers, that translation work is where papers go to die. A team
Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating
Z.AI, the AI platform developed by the team behind the GLM model family, has released GLM-5.1 — its next-generation flagship model developed specifically for agentic engineering. Unlike models optimized for clean, single-turn benchmarks, GLM-5.1 is built for agentic tasks, with significantly stronger coding capabilities than its predecessor, and achieves state-of-the-art performance on SWE-Bench Pro while
