Do LLMs Know When They're Wrong?

We're moving past LLMs that just predict the next word. Discover a new frontier: models that can gauge their own uncertainty to improve reasoning. This video explores two brand new papers that turn the "Entropix" meme into practical, working code. Current methods like Chain-of-Thought are powerful, but they are essentially a model "thinking out loud." What if a model could recognize when it's on a bad path and correct itself? This is the core idea behind using token entropy and logprobs as a "confidence" signal. This video is for the AI builder, developer, and enthusiast who wants to look under the hood. We break down the history of this idea (from OpenAI's o-1 hints to Twitter theories) and then dive into the mechanics of two pivotal papers: 1. **ARPO**: Agentic Reinforced Policy Optimization 2. **Deep Think with Confidence**: A practical vLLM implementation from Meta By the end, you'll understand not just *what* LLM confidence is, but *how* it works, and *why* it's a compelling direction for building more capable and efficient agentic systems. --- ### Papers & Resources Mentioned * [ARPO : Agentic Reinforced Policy Optimization (Dong et al., 2025)](https://arxiv.org/abs/2507.19849) + [ARPO GitHub Repo](https://github.com/dongguanting/ARPO) * [Deep Think with Confidence (Fu et al., 2025)](https://arxiv.org/abs/2508.15260) + [DeepThink Project Page (Meta AI)](https://jiaweizzhao.github.io/deepconf/) + [DeepThink Pull Request for vLLM](https://github.com/vllm-project/vllm/pull/23201) * [OpenAI o-1 Blog Post](https://openai.com/index/learning-to-reason-with-llms/) + [Let's Verify Step-by-Step (OpenAI, 2023)](https://arxiv.org/abs/2305.20050) * [ICML 2024 Tutorial: Physics of Language Models](https://www.youtube.com/watch?v=yBL7J0kgldU) --- ### Chapters 00:00 - Introduction: The Idea of LLM Confidence 00:31 - Background: From OpenAI's o-1 to the "Entropix" Meme 05:26 - Paper 1: ARPO & Agentic Rollout Confidence 07:55 - Paper 2: Meta's "Deep Think wi