LLMs

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LLMs believe false statements even after explicit warnings that they’re false

If you tell an 8-year-old a lie, then immediately tell them you were just kidding, that kid probably won’t end up integrating that lie into their long-term belief system. But new research on so-called “negation neglect” finds that LLMs have a robust tendency to accept false or fictitious statements even when they are clearly and […]

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Gemini 3.5 Flash: frontier intelligence with speed

Google Gemini’s next-generation family offering: Gemini 3.5 is here!  Gemini 3.5 Flash combines frontier intelligence with real-world action and supports high-speed agentic workflows, coding, and multimodal reasoning while maintaining the low latency expected from the Flash series. With Gemini 3.5 Pro, slated to be released in the next month, let’s take a look at the

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Top 10 LLM Research Papers of 2026

Large language models are no longer just about scale. In 2026, the most important LLM research is focused on making models safer, more controllable, and more useful as real-world agents. From persuasion risk and harmful-content mechanisms to tool-calling, temporal reasoning, and agent privacy, these papers show where LLM research is heading next. Here are the

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Feature Engineering with LLMs: Techniques & Python Examples

Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language Models change this by helping machines understand language, extract meaning, and generate

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Anthropic’s Claude Managed Agents can now “dream,” sort of

SAN FRANCISCO—At its Code with Claude developers’ conference, Anthropic has introduced what it calls “dreaming” to Claude Managed Agents. Dreaming, in this case, is a process of going over recent events and identifying specific things that are worth storing in “memory” to inform future tasks and interactions. Dreaming is a feature that is currently in

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Top 10 Open-Source Libraries to Fine-Tune LLMs Locally

Fine-tuning LLMs has become much easier because of open-source tools. You no longer need to build the full training stack from scratch. Whether you want low-VRAM training, LoRA, QLoRA, RLHF, DPO, multi-GPU scaling, or a simple UI, there is likely a library that fits your workflow. Here are the best open-source libraries worth knowing for

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agentic ai models

Why Agentic AI Requires More Than Better Models

Agentic artificial intelligence (AI) is set to fundamentally reshape the structure of enterprise work and commerce. Rather than simply responding to instructions, these agents actively participate in workflows by planning tasks, creating and using tools, correcting their own errors, and pursuing multistep goals autonomously. The result is faster, more adaptive workflows. The emergence of the

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Meta Muse Spark Review: Is It Worth the Hype?

Meta’s big moment is here. The Meta Superintelligence Labs has launched Muse Spark, its first AI model aiming at “personal superintelligence.” The journey to this point has been eventful, from building the widely adopted Llama family of open-source models to aggressive talent acquisitions that sent shockwaves through the AI industry. But the backstory is not

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Cursor V3 Explained: The AI Coding Agent That’s Replacing Traditional IDEs in 2026

In 2026, AI-powered coding tools began revolutionizing software development, with Cursor v3 emerging as a leading example. Unlike traditional development environments, Cursor v3 offers a new way for developers to interact with their code by utilizing AI agents that assist in coding tasks. Cursor v3 goes beyond basic autocompletion offered by most IDEs by executing AI agents on tasks and using

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DeepSeek-V4: The Most Powerful Open-Source Model Ever

The latest set of open-source models from DeepSeek are here. While the industry anticipated the dominance of “closed” iterations like GPT-5.5, the arrival of DeepSeek-V4 has ticked the dominance in the favour of open-source AI. By combining a 1.6 trillion parameter MoE architecture with a massive 1 million token context window, DeepSeek-V4 has effectively commoditized

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