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Microsoft Research Introduces CORPGEN To Manage Multi Horizon Tasks For Autonomous AI Agents Using Hierarchical Planning and Memory

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Microsoft researchers have introduced CORPGEN, an architecture-agnostic framework designed to manage the complexities of realistic organizational work through autonomous digital employees. While existing benchmarks evaluate AI agents on isolated, single tasks, real-world corporate environments require managing dozens of concurrent, interleaved tasks with complex dependencies. The research team identifies this distinct problem class as Multi-Horizon Task […]

Microsoft Research Introduces CORPGEN To Manage Multi Horizon Tasks For Autonomous AI Agents Using Hierarchical Planning and Memory Read More »

Google DeepMind Researchers Apply Semantic Evolution to Create Non Intuitive VAD-CFR and SHOR-PSRO Variants for Superior Algorithmic Convergence

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In the competitive arena of Multi-Agent Reinforcement Learning (MARL), progress has long been bottlenecked by human intuition. For years, researchers have manually refined algorithms like Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO), navigating a vast combinatorial space of update rules via trial-and-error. Google DeepMind research team has now shifted this paradigm with

Google DeepMind Researchers Apply Semantic Evolution to Create Non Intuitive VAD-CFR and SHOR-PSRO Variants for Superior Algorithmic Convergence Read More »

Forget Keyword Imitation: ByteDance AI Maps Molecular Bonds in AI Reasoning to Stabilize Long Chain-of-Thought Performance and Reinforcement Learning (RL) Training

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ByteDance Seed recently dropped a research that might change how we build reasoning AI. For years, devs and AI researchers have struggled to ‘cold-start’ Large Language Models (LLMs) into Long Chain-of-Thought (Long CoT) models. Most models lose their way or fail to transfer patterns during multi-step reasoning. The ByteDance team discovered the problem: we have

Forget Keyword Imitation: ByteDance AI Maps Molecular Bonds in AI Reasoning to Stabilize Long Chain-of-Thought Performance and Reinforcement Learning (RL) Training Read More »

A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half

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For the last few years, the AI world has followed a simple rule: if you want a Large Language Model (LLM) to solve a harder problem, make its Chain-of-Thought (CoT) longer. But new research from the University of Virginia and Google proves that ‘thinking long’ is not the same as ‘thinking hard’. The research team

A New Google AI Research Proposes Deep-Thinking Ratio to Improve LLM Accuracy While Cutting Total Inference Costs by Half Read More »

NVIDIA Releases DreamDojo: An Open-Source Robot World Model Trained on 44,711 Hours of Real-World Human Video Data

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Building simulators for robots has been a long term challenge. Traditional engines require manual coding of physics and perfect 3D models. NVIDIA is changing this with DreamDojo, a fully open-source, generalizable robot world model. Instead of using a physics engine, DreamDojo ‘dreams’ the results of robot actions directly in pixels. https://arxiv.org/pdf/2602.06949 Scaling Robotics with 44k+

NVIDIA Releases DreamDojo: An Open-Source Robot World Model Trained on 44,711 Hours of Real-World Human Video Data Read More »

Zyphra Releases ZUNA: A 380M-Parameter BCI Foundation Model for EEG Data, Advancing Noninvasive Thought-to-Text Development

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Brain-computer interfaces (BCIs) are finally having their ‘foundation model’ moment. Zyphra, a research lab focused on large-scale models, recently released ZUNA, a 380M-parameter foundation model specifically for EEG signals. ZUNA is a masked diffusion auto-encoder designed to perform channel infilling and super-resolution for any electrode layout. This release includes weights under an Apache-2.0 license and

Zyphra Releases ZUNA: A 380M-Parameter BCI Foundation Model for EEG Data, Advancing Noninvasive Thought-to-Text Development Read More »

Google DeepMind Proposes New Framework for Intelligent AI Delegation to Secure the Emerging Agentic Web for Future Economies

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The AI industry is currently obsessed with ‘agents’—autonomous programs that do more than just chat. However, most current multi-agent systems rely on brittle, hard-coded heuristics that fail when the environment changes. Google DeepMind researchers have proposed a new solution. The research team argued that for the ‘agentic web’ to scale, agents must move beyond simple

Google DeepMind Proposes New Framework for Intelligent AI Delegation to Secure the Emerging Agentic Web for Future Economies Read More »

Google DeepMind Introduces Aletheia: The AI Agent Moving from Math Competitions to Fully Autonomous Professional Research Discoveries

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Google DeepMind team has introduced Aletheia, a specialized AI agent designed to bridge the gap between competition-level math and professional research. While models achieved gold-medal standards at the 2025 International Mathematical Olympiad (IMO), research requires navigating vast literature and constructing long-horizon proofs. Aletheia solves this by iteratively generating, verifying, and revising solutions in natural language.

Google DeepMind Introduces Aletheia: The AI Agent Moving from Math Competitions to Fully Autonomous Professional Research Discoveries Read More »

NVIDIA Researchers Introduce KVTC Transform Coding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Serving

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Serving Large Language Models (LLMs) at scale is a massive engineering challenge because of Key-Value (KV) cache management. As models grow in size and reasoning capability, the KV cache footprint increases and becomes a major bottleneck for throughput and latency. For modern Transformers, this cache can occupy multiple gigabytes. NVIDIA researchers have introduced KVTC (KV

NVIDIA Researchers Introduce KVTC Transform Coding Pipeline to Compress Key-Value Caches by 20x for Efficient LLM Serving Read More »

Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies

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Earth observation (EO) constellations capture huge volumes of high-resolution imagery every day, but most of it never reaches the ground in time for model training. Downlink bandwidth is the main bottleneck. Images can sit on orbit for days while ground models train on partial and delayed data. Microsoft Researchers introduced ‘OrbitalBrain’ framework as a different

Microsoft AI Proposes OrbitalBrain: Enabling Distributed Machine Learning in Space with Inter-Satellite Links and Constellation-Aware Resource Optimization Strategies Read More »