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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 […]
Have you ever asked an LLM a question, changed the wording a few times, and still felt the answer wasn’t quite right? If you’ve worked with tools like ChatGPT or Gemini, you’ve probably rewritten prompts, added more context, or used phrases like “be concise” or “think step by step” to improve results. But what if
Prompt Repetition: The Overlooked Hack for Better LLM Results Read More »
Researchers today can draft entire papers with AI assistance, run experiments faster than ever, and summarise literature in minutes. Yet one stubborn bottleneck remains: creating clear, publication-ready diagrams. Poor diagrams look unprofessional and can obscure ideas and weaken a paper’s impact. Google now seems to have a solution to this – and it is called
Meet PaperBanana: Google’s AI That Auto-Generates Research Diagrams Read More »
Researchers have uncovered an unexpected flaw in one of the most common techniques used to build smaller, cheaper AI models: Distillation. When a “student” model is trained on filtered outputs from a larger “teacher,” it can still inherit the teacher’s quirks and unsafe behaviors, even when those traits never appear in the training data. They’re
Subliminal Learning: How AI Models Inherit Hidden Dangers Read More »
