Research
Latest news, analysis, and insights about Research.
How DARPA’s AI-controlled F-16 flight accelerates the era of autonomous combat aviation
DARPA and the U.S. Air Force have successfully executed a test flight of an AI-controlled F-16 fighter jet. This milestone transitions deep reinforcement learning from safe simulations to physical, high-performance tactical aircraft, fundamentally rewriting the rules of aerospace verification.
How Moonshot AI Built a Frontier Competitor Without Just Copying Anthropic’s Fable
The rapid rise of Moonshot AI's Kimi K3 has sparked intense debate over Chinese AI capabilities. While critics point to model distillation of Anthropic's Fable, experts argue that Kimi K3's performance requires genuine architectural breakthroughs.
How Meta's open-source computer vision models SAM and DINO are transforming materials science at Berkeley Lab.
Meta's open-source computer vision models are finding an unexpected second life. In partnership with Lawrence Berkeley National Laboratory, tools like SAM and DINO are accelerating materials science.
How OpenAI plans to secure autonomous agents executing complex tasks over long horizons
As AI models transition from simple chat interfaces to autonomous agents executing multi-week workflows, traditional RLHF is breaking down. OpenAI's latest safety framework outlines how the industry must adapt to secure long-horizon models.
Stanford HAI’s 2025 AI Index Report Reveals a Deep Structural Realignment in Private Funding and Compute Costs
Stanford HAI has released its 2025 AI Index Report, detailing a massive transition from speculative model building to strict infrastructure economics and regulatory compliance.
Why a $25,000 DeepMind Kaggle Grand Prize Winner Exploded the AI Benchmark Myth
A $25,000 Google DeepMind Kaggle competition meant to measure true artificial general intelligence has reportedly been won by 'blatant AI slop.' The incident exposes the deep vulnerabilities of modern AI benchmarks.
Half of AI Agents Have No Published Safety Framework, MIT Research Finds
MIT CSAIL's first-ever AI Agent Index audited 30 prominent agents and found that only four provide agent-specific safety documentation. As deployment accelerates, the gap between capability and governance is widening.
A Single Click Exfiltrated Copilot Data: What This Attack Means for Enterprise AI
Security researchers at Varonis discovered a Microsoft Copilot vulnerability that exfiltrated user names, locations, and chat histories with a single click—bypassing enterprise security entirely. The attack reveals systemic risks in how organizations deploy AI assistants.
How Researchers Manipulated IBM's 'Bob' AI Agent Into Downloading and Running Malicious Code
Security researchers at PromptArmor have demonstrated a critical vulnerability in IBM's enterprise AI agent nicknamed 'Bob'—successfully manipulating it into downloading and executing malware. The findings highlight an uncomfortable truth about agentic AI: the same capabilities that make these systems useful also make them dangerous.
Berkeley Lab Deploys LLM System to Manage Particle Accelerator — What This Means for Critical Infrastructure
Lawrence Berkeley National Laboratory has deployed an LLM-powered AI system to troubleshoot and optimize its Advanced Light Source particle accelerator. The implications extend far beyond physics — this is the template for AI in critical scientific infrastructure.
Google Med-Gemini Hallucinated 'Basilar Ganglia' — What This Means for Healthcare AI
Google's Med-Gemini model confidently referenced the 'basilar ganglia' — a brain structure that doesn't exist. The error raises urgent questions about deploying AI in clinical settings where hallucinations could harm patients.
OpenAI's 'Confessions' Method Trains AI to Admit Its Own Mistakes
OpenAI is testing a new training method called 'confessions' that teaches models to self-report their mistakes. If it works, it could fundamentally change how enterprises trust—and verify—AI outputs.
OpenAI's 'Confessions' Method Could Make AI Systems Finally Admit When They're Wrong
OpenAI is testing a training method called 'confessions' that teaches AI models to admit when they've made mistakes or acted undesirably. It's a direct attack on one of the most persistent problems in production AI: models that confidently lie rather than acknowledge uncertainty.