A research team introduces Automated Search for Artificial Life (ASAL). This novel framework leverages vision-language FMs to automate and enhance the discovery process in ALife research. The post Automating Artificial Life Discovery: The Power of Foundation Models first appeared on Synced.

Researchers from the University of Texas at Austin and NVIDIA proposes upcycling approach, an innovative training recipe enables the development of an 8-Expert Top-2 MoE model using Llama 3-8B with less than 1% of the compute typically required for pre-training. The post Llama 3 Meets MoE: […]

A DeepMind research team introduces JetFormer, a Transformer designed to directly model raw data. This model maximizes the likelihood of raw data without depending on any pre-trained components, and is capable of both understanding and generating text and images seamlessly. The post DeepMind’s […]

An NVIDIA research team proposes the normalized Transformer, which consolidates key findings in Transformer research under a unified framework, offering faster learning and reduced training steps—by factors ranging from 4 to 20 depending on sequence length. The post NVIDIA’s nGPT: […]

A research team at Meta introduces the Large Concept Model (LCM), a novel architecture that processes input at a higher semantic level. This shift allows the LCM to achieve remarkable zero-shot generalization across languages, outperforming existing LLMs of comparable size. The post From Token to […]

An NVIDIA research team proposes Hymba, a family of small language models that blend transformer attention with state space models, which outperforms the Llama-3.2-3B model with a 1.32% higher average accuracy, while reducing cache size by 11.67× and increasing throughput by 3.49×. The post […]

In a new paper Time-Reversal Provides Unsupervised Feedback to LLMs, a research team from Google DeepMind and Indian Institute of Science proposes Time Reversed Language Models (TRLMs), a framework that allows LLMs to reason in reverse—scoring and generating content in a manner opposite to the […]

In a new paper Navigation World Models, a research team from Meta, New York University and Berkeley AI Research proposes a Navigation World Model (NWM), a controllable video generation model that enables agents to simulate potential navigation plans and assess their feasibility before taking […]

An Apple research team introduces AIMV2, a family of vision encoders that is designed to predict both image patches and text tokens within a unified sequence. This combined objective enables the model to excel in a range of tasks, such as image recognition, visual grounding, and multimodal […]

In a new paper Music Foundation Model as Generic Booster for Music Downstream Tasks, a Sony research team presents SoniDo, a groundbreaking music foundation model that offers robust framework for improving the effectiveness and accessibility of music processing. The post Redefining Music AI: The […]