World Wire

Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Voxtral Mini Transcribe V2 for batch transcription with diarization. Voxtral Realtime (Voxtral Mini 4B Realtime 2602) for low-latency streaming transcription, released as open weights. Both models are designed for 13 languages : English, Chinese, Hindi, Spanish, Arabic, French, Portuguese, Russian, German, Japanese, Korean, Italian, and Dutch. Model family: batch and streaming, with clear roles Mistral positions Voxtral Transcribe 2 as ‘two next-generation speech-to-text models’ with state-of-the-art transcription quality, diarization, and ultra-low latency . Voxtral Mini Transcribe V2 is the batch model . It is optimized for transcription...

NVIDIA has released VIBETENSOR, an open-source research system software stack for deep learning. VIBETENSOR is generated by LLM-powered coding agents under high-level human guidance. The system asks a concrete question: can coding agents generate a coherent deep learning runtime that spans Python and JavaScript APIs down to C++ runtime components and CUDA memory management and validate it only through tools. Architecture from frontends to CUDA runtime VIBETENSOR implements a PyTorch-style eager tensor library with a C++20 core for CPU and CUDA, a torch-like Python overlay via nanobind, and an experimental Node.js / TypeScript interface. It targets Linux x86_64 and NVIDIA GPUs via CUDA, and builds without CUDA are intentionally disabled. https://ift.tt/HmLrTAY The core stack includes its own tensor and storage system, a schema-lite dispatcher, a reverse-mode autograd engine, a CUDA subsystem with streams, events, and CUDA graphs, a stream-ordered caching allocator with diagnost...

In this tutorial, we implement an agentic chain-of-thought pruning framework that generates multiple reasoning paths in parallel and dynamically reduces them using consensus signals and early stopping. We focus on improving reasoning efficiency by reducing unnecessary token usage while preserving answer correctness, demonstrating that self-consistency and lightweight graph-based agreement can serve as effective proxies for reasoning quality. We design the entire pipeline using a compact instruction-tuned model and progressive sampling to simulate how an agent can decide when it has reasoned “enough.” Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U transformers accelerate bitsandbytes networkx scikit-learn import re, time, random, math import numpy as np import torch import networkx as nx from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig from sklearn.feature_extraction.text import TfidfVectorizer ...

Frontier multimodal models usually process an image in a single pass. If they miss a serial number on a chip or a small symbol on a building plan, they often guess. Google’s new Agentic Vision capability in Gemini 3 Flash changes this by turning image understanding into an active, tool using loop grounded in visual evidence. Google team reports that enabling code execution with Gemini 3 Flash delivers a 5–10% quality boost across most vision benchmarks , which is a significant gain for production vision workloads. What Agentic Vision Does ? Agentic Vision is a new capability built into Gemini 3 Flash that combines visual reasoning with Python code execution . Instead of treating vision as a fixed embedding step, the model can: Formulate a plan for how to inspect an image. Run Python that manipulates or analyzes that image. Re examine the transformed image before answering. The core behavior is to treat image understanding as an active investigation rather than a frozen sna...

In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a behavior dataset from a constrained policy, and then train both a Behavior Cloning baseline and a Conservative Q-Learning agent using d3rlpy. By structuring the workflow around offline datasets, careful evaluation, and conservative learning objectives, we demonstrate how robust decision-making policies can be trained in settings where unsafe exploration is not an option. Check out the  FULL CODES here . Copy Code Copied Use a different Browser !pip -q install -U "d3rlpy" "gymnasium" "numpy" "torch" "matplotlib" "scikit-learn" import os import time import random import inspect import numpy as np import matplotlib.pyplot as plt import gymnasium as gym from gymnasium import spaces import torch import d3rlpy SEED = 42...

In this tutorial, we present an advanced, hands-on tutorial that demonstrates how we use Qrisp to build and execute non-trivial quantum algorithms. We walk through core Qrisp abstractions for quantum data, construct entangled states, and then progressively implement Grover’s search with automatic uncomputation, Quantum Phase Estimation, and a full QAOA workflow for the MaxCut problem. Also, we focus on writing expressive, high-level quantum programs while letting Qrisp manage circuit construction, control logic, and reversibility behind the scenes. Check out the  FULL CODES here . Copy Code Copied Use a different Browser import sys, subprocess, math, random, textwrap, time def _pip_install(pkgs): cmd = [sys.executable, "-m", "pip", "install", "-q"] + pkgs subprocess.check_call(cmd) print("Installing dependencies (qrisp, networkx, matplotlib, sympy)...") _pip_install(["qrisp", "networkx", "m...