Can AI Think Its Own Thoughts? Learning to Question Inputs in LLMs

Can AI Think Its Own Thoughts? Learning to Question Inputs in LLMs

LLMs can generate code amazingly fast — but what happens when the input premise is wrong? In this episode of Decode: Science, we explore “Refining Critical Thinking in LLM Code Generation: A Faulty Premise–based Evaluation Framework” (FPBench). Jialin Li and colleagues designed an evaluation system that tests how well 15 popular models recognize and handle faulty or missing premises, revealing alarming gaps in their reasoning abilities. We decode what FPBench is, why it matters for AI trust, and what it could take to make code generation smarter.

Teaching AI to Hear the Universe - Automating Gravitational-Wave Discovery

Gravitational waves whisper across the cosmos — and now, AI might finally hear them with clarity. In this episode of Decode: Science, we explore “Automated Algorithmic Discovery for Gravitational‑Wave Detection Guided by LLM‑Informed Evolutionary Monte Carlo Tree Search”, by Wang and Zeng (2025). They introduce Evo‑MCTS: an automated, interpretable framework that discovers novel detection algorithms through evolutionary search and large language model heuristics. With over 20% improved accuracy and transparent logic, this paper rewrites how we might detect cosmic signals using AI.

Agent Lightening: Train Any AI Agent with Reinforcement Learning

Meet Agent Lightning, a framework that decouples how agents act in the world from how they’re trained—with almost zero code modifications. Introduced in 2025 by Luo et al., this paper reimagines reinforcement learning for AI agents, making it compatible with everything from LangChain to custom agents. In this episode of Decode: Science, we explore how Agent Lightning formulates agent behavior as an MDP, uses LightningRL for hierarchical credit assignment, and makes scalable agent learning a reality. Tech paper: https://arxiv.org/pdf/2508.03680

Delving Deep: A Breakthrough in Deep Learning

Before ResNet changed everything, this 2015 paper pushed CNNs to new depths and beat human-level performance on ImageNet. The team behind it—led by Kaiming He—showed that with Parametric ReLU and Batch Normalization, deep models could finally be trained efficiently and accurately. In this episode of Plain Science, we explore how Delving Deep into Rectifiers laid the groundwork for the next wave of breakthroughs in computer vision. Paper: https://openaccess.thecvf.com/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf

How AI Learned to Understand Us

In this episode of Decode: Science, we explore the 2018 paper that introduced BERT, a model that transformed how machines understand human language. By learning from both left and right context simultaneously, BERT became the foundation for a new generation of smarter, context-aware AI systems — from Google Search to intelligent assistants. We’ll break down how it works, why it matters, and what made it so effective.