AI Agents

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Google DeepMind Researchers Introduce Evo-Memory Benchmark and ReMem Framework for Experience Reuse in LLM Agents

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Large language model agents are starting to store everything they see, but can they actually improve their policies at test time from those experiences rather than just replaying context windows? Researchers from University of Illinois Urbana Champaign and Google DeepMind propose Evo-Memory, a streaming benchmark and agent framework that targets this exact gap. Evo-Memory evaluates […]

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The Future of Work: How Agentic AI Is Powering the Human-in-the-Loop 2.0 Revolution

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The conversation around artificial intelligence is shifting dramatically.We’ve evolved beyond the binary narrative of humans versus machines. The new paradigm is human–AI orchestration, led by a new generation of Agentic AI systems — autonomous, reasoning, goal-driven agents that collaborate with humans instead of replacing them. Welcome to the era of Human-in-the-Loop 2.0 — a world

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Plug-and-Play Is a Myth: Why AI Agents Need Integration, Not Just Prompts

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Introduction: The Illusion of Simplicity in AI Agents Many teams today believe that AI agents are plug-and-play tools — install them, type a prompt, and watch them magically transform workflows. It sounds like the dream of automation finally realized. But here’s the truth: it doesn’t work that way. While AI agents are incredibly powerful, their

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DeepSeek Researchers Introduce DeepSeek-V3.2 and DeepSeek-V3.2-Speciale for Long Context Reasoning and Agentic Workloads

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How do you get GPT-5-level reasoning on real long-context, tool-using workloads without paying the quadratic attention and GPU cost that usually makes those systems impractical? DeepSeek research introduces DeepSeek-V3.2 and DeepSeek-V3.2-Speciale. They are reasoning-first models built for agents and targets high quality reasoning, long context and agent workflows, with open weights and production APIs. The

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MiniMax-M2: Technical Deep Dive into Interleaved Thinking for Agentic Coding Workflows

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The AI coding landscape just got a massive shake-up. If you’ve been relying on Claude 3.5 Sonnet or GPT-4o for your dev workflows, you know the pain: great performance often comes with a bill that makes your wallet weep, or latency that breaks your flow.This article provides a technical overview of MiniMax-M2, focusing on its

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A Coding Implementation for an Agentic AI Framework that Performs Literature Analysis, Hypothesis Generation, Experimental Planning, Simulation, and Scientific Reporting

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In this tutorial, we build a complete scientific discovery agent step by step and experience how each component works together to form a coherent research workflow. We begin by loading our literature corpus, constructing retrieval and LLM modules, and then assembling agents that search papers, generate hypotheses, design experiments, and produce structured reports. Through snippets

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