Meet LingBot-World-Infinity: An Open Causal World Model With An Agentic Harness

Robbyant, Ant Group’s embodied-intelligence unit, has released LingBot-World-Infinity (LingBot-World 2.0). It is a causal video generation model that behaves as an interactive world simulator. It is how the team attacks two failure modes: long-horizon drift and interactive latency. What is LingBot-World-Infinity? An interactive world model generates video frame by frame, conditioned on a stream of…

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After Apple, India’s smartphone manufacturing boom enters new phase with Vivo JV

India on Thursday approved a manufacturing joint venture between China’s Vivo and local manufacturer Dixon Technologies, a move that could mark the next phase of the country’s smartphone manufacturing boom after Apple helped turn India into a global smartphone production hub. The approval allows Vivo to proceed with a long-delayed manufacturing partnership first announced in…

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OpenAI Releases GPT-Live and GPT-Live-1 mini: Full-Duplex Voice Models That Delegate Deeper Reasoning to GPT-5.5

“””Illustrative simulation of the GPT-Live full-duplex decision loop. Teaching model of the described architecture, NOT the real API.””” import random random.seed(7) # reproducible output class BackgroundModel: # stands in for GPT-5.5 def run(self, query): return f”answer to ‘{query}'” class GPTLive: def __init__(self, background): self.background = background self.pending = None # a delegated task, if one…

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Meet Nemotron Labs 3 Puzzle 75B A9B: A Compressed Hybrid MoE LLM Delivering 2.03x Server Throughput

Large hybrid MoE models like Nemotron-3-Super are accurate but expensive to serve. Their active parameters, KV cache, and Mamba state cap how many users a node can hold at a given per-user token rate. NVIDIA AI team has released Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super. The parent model has 120.7B total and 12.8B active parameters….

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Meta Superintelligence Labs Releases Muse Spark 1.1: A Multimodal Reasoning Model for Agentic Tasks on Meta Model API

Today, Meta Superintelligence Labs released Muse Spark 1.1. Alongside it, Meta opened a public preview of the Meta Model API. That second part is the structural change. Meta’s models previously reached developers mainly as open weights. Muse Spark 1.1 is closed, hosted, and metered per token. So the question is narrow. Where does it belong…

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