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.

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.

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

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

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.

In this episode of Decode: Science, we dive into “Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture”, a breakthrough in how machines learn to understand the visual world — with no human-labeled data.

What does it mean for a machine to “learn” by itself? How can you train a system to see, recognize, and understand — without telling it what it’s looking at? Welcome to the frontier of AI vision research.

In this episode of Decode: Science, we break down “Attention Is All You Need”, the 2017 paper that introduced the Transformer — the architecture behind today’s most powerful AI models.

Discover how a single innovation replaced complex recurrence and convolutions, enabling machines to understand context, translate languages, and generate text like never before. This episode explores the core idea of attention and why it became the foundation of the modern AI revolution.

Cancer isn’t one disease — it’s a constellation of traits that evolve to defy the body’s checks and balances.

In this episode of Decode: Science, we explore “Hallmarks of Cancer: New Dimensions”, a 2022 update to one of the most influential frameworks in biomedical science. What makes a cell cancerous? How has our understanding changed in the past two decades? And what might this mean for future treatments?

We decode the blueprint of cancer, one hallmark at a time.