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Neuroscience has long been a field of divide and conquer. Researchers typically map specific cognitive functions to isolated brain regions—like motion to area V5 or faces to the fusiform gyrus—using models tailored to narrow experimental paradigms. While this has provided deep insights, the resulting landscape is fragmented, lacking a unified framework to explain how the […]
Google has released Gemini 3.1 Flash Live in preview for developers through the Gemini Live API in Google AI Studio. This model targets low-latency, more natural, and more reliable real-time voice interactions, serving as Google’s ‘highest-quality audio and speech model to date.’ By natively processing multimodal streams, the release provides a technical foundation for building
In the landscape of enterprise AI, the bridge between unstructured audio and actionable text has often been a bottleneck of proprietary APIs and complex cascaded pipelines. Today, Cohere—a company traditionally known for its text-generation and embedding models—has officially stepped into the Automatic Speech Recognition (ASR) market with the release of their latest model ‘Cohere Transcribe‘.
Tencent AI Lab has released Covo-Audio, a 7B-parameter end-to-end Large Audio Language Model (LALM). The model is designed to unify speech processing and language intelligence by directly processing continuous audio inputs and generating audio outputs within a single architecture. System Architecture The Covo-Audio framework consists of four primary components designed for seamless cross-modal interaction: Audio
Post-training Large Language Models (LLMs) for long-horizon agentic tasks—such as software engineering, web browsing, and complex tool use—presents a persistent trade-off between computational efficiency and model generalization. While Supervised Fine-Tuning (SFT) is computationally inexpensive, it frequently suffers from out-of-domain (OOD) performance degradation and struggles to generalize beyond its training distribution. Conversely, end-to-end reinforcement learning (E2E
When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention
Paged Attention in Large Language Models LLMs Read More »
Researchers from FAIR at Meta, Cornell University, and Carnegie Mellon University have demonstrated that large language models (LLMs) can learn to reason using a remarkably small number of trained parameters. The research team introduces TinyLoRA, a parameterization that can scale down to a single trainable parameter under extreme sharing settings. Using this method on a
World Models (WMs) are a central framework for developing agents that reason and plan in a compact latent space. However, training these models directly from pixel data often leads to ‘representation collapse,’ where the model produces redundant embeddings to trivially satisfy prediction objectives. Current approaches attempt to prevent this by relying on complex heuristics: they
The dream of recursive self-improvement in AI—where a system doesn’t just get better at a task, but gets better at learning—has long been the ‘holy grail’ of the field. While theoretical models like the Gödel Machine have existed for decades, they remained largely impractical in real-world settings. That changed with the Darwin Gödel Machine (DGM),
NVIDIA has announced the release of Nemotron-Cascade 2, an open-weight 30B Mixture-of-Experts (MoE) model with 3B activated parameters. The model focuses on maximizing ‘intelligence density,’ delivering advanced reasoning capabilities at a fraction of the parameter scale used by frontier models. Nemotron-Cascade 2 is the second open-weight LLM to achieve Gold Medal-level performance in the 2025
