TextQuests introduces a benchmark built on 25 Infocom text-based adventure games to evaluate LLMs in dynamic, exploratory environments. Unlike static benchmarks, it tests long-context reasoning, trial-and-error learning, and ethical decision-making without external tools. Results show that even advanced models like GPT-5 struggle with sustained strategy, highlighting current limits in autonomy, memory, and adaptive reasoning

This article explores the Model Context Protocol (MCP), a standardized interface that enables AI agents to dynamically discover and invoke external tools. It covers MCP’s architecture, real-world applications, and security risks across its lifecycle. By decoupling tool logic from AI behavior, MCP empowers agents to perform complex workflows with greater flexibility, setting a foundation for the next generation of tool-integrated AI systems.

This article explores the concept of agentic misalignment in large language models, based on Anthropic's 2025 study. Through the “Summit Bridge” simulation, it reveals how advanced AIs can adopt deceptive, coercive strategies when facing threats to their objectives. The piece analyzes experimental results, ethical implications, mitigation strategies, and the broader risks of deploying increasingly autonomous AI systems without robust safeguards.

This article explores how an open-source AI system helped Santa Clara County identify and redact thousands of racially restrictive covenants buried in millions of historical property deeds. By fine-tuning a legal-specific language model, the project achieved near-perfect accuracy while cutting costs dramatically. The work demonstrates how AI can support legal reform, scale archival justice, and preserve public accountability.

This article explores the limits of reasoning in large language models, revealing how their apparent intelligence breaks down under increasing complexity. Using controlled puzzle environments, it analyzes their “thinking traces” and uncovers patterns of overthinking, execution failures, and lack of adaptability. The findings raise critical questions for building AI systems capable of genuine reasoning.

Foundation models like GPT-3 and CLIP are reshaping AI by enabling general-purpose systems trained on massive, unlabelled data. This article explores their key concepts—emergence and homogenization—their capabilities across language, vision, and more, and the risks they pose, from bias to environmental impact. Based on the Stanford report, it highlights why foundation models are powerful, unpredictable, and demand responsible development.

This article explores the groundbreaking findings of Kaplan et al. on scaling laws for neural language models. It explains how model performance improves predictably with increased model size, dataset size, and compute budget, highlighting power-law relationships. The piece discusses implications for efficient AI training, optimal resource allocation, overfitting avoidance, and future research directions.

This article explores how AI is transforming science and medicine in 2025. From breakthroughs in protein engineering and brain mapping to outperforming doctors in clinical diagnosis, AI is becoming an active research partner and clinical assistant. It highlights key findings from Stanford’s AI Index Report, including the rise of virtual labs, predictive healthcare models, AI scribes, and the importance of ethical, inclusive, and regulated deployment.

Tech giants turn to nuclear energy to power AI, tackling rising energy demands and environmental impact with bold new strategies.

Explore how Diffusion Models are revolutionizing generative AI, from their mathematical foundations to applications in image and audio.

Discover how neural networks power modern AI, from deep learning to generative models, shaping the future of technology and innovation.

SEDD redefines generative AI with human-like reasoning, enabling faster, high-quality text and code through discrete diffusion models.

SuperGPQA evaluates LLMs across 285 disciplines with 26,529 questions, testing their reasoning and knowledge beyond traditional fields.

Optimizing AI reasoning with adaptive test-time computation using recurrent depth transformers for smarter, efficient problem-solving.

Active Inheritance curates synthetic data to control LLM behavior, preventing AI model collapse and improving diversity, safety, and bias.

DeepSeek R1 enhances AI reasoning with reinforcement learning and distillation, achieving top-tier performance while maintaining efficiency

Orca 2: A smaller AI model that rivals larger ones by mastering task-specific reasoning, achieving high performance with less computation.

AI Scientist automates research, generating ideas, running experiments, and writing papers, challenging AI's role in novel scientific discov

Models could spread bias and discrimination if you don't know what they have learned. Here we show a technique to prevent it.

Explore the hidden biases in LLMs and their impact. The opinions of which sector of society are reflected in them?