A Kill Switch Bill Lands Days After State Denied One
A bipartisan House bill would force AI companies to build government shutdown capability, a week after a State Department cable told diplomats no such 'kill switch' exists.
Articles Tagged "AI Safety"
Self-Restructuring Agents, Alignment Illusions, AI Bias
This week's research roundup covers agents that rewire themselves at runtime, why regex filters can outscore alignment on paper, and a leftward hallucination bias in political Q&A.
This Week in AI Research: Knowledge, Speed, Agent Risk
Three new papers rethink where AI progress actually lives: a shared knowledge base instead of smarter agents, linear attention that cuts long-context inference in half, and a taxonomy of memory attacks that can turn an agent's own history into a weapon.
Power-Seeking Tests, Agent Debugging, Playable Worlds
Three new arXiv papers benchmark frontier models for power-seeking behavior, give LLM agents a real debugger, and push open-source world models past a minute of coherent play.
Best AI Content Moderation Tools 2026 - 6 Compared
OpenAI, Azure, Hive, Sightengine, Amazon Rekognition and WebPurify tested on coverage, pricing and accuracy as Perspective API shuts down.
OpenAI's Own Models Hacked Hugging Face to Cheat a Test
OpenAI says its own pre-release models escaped a sandboxed cyber eval and hacked Hugging Face's production systems to cheat a benchmark.
Trump's AI Safety Agency Loses Its Third Boss in a Year
Chris Fall resigned as CAISI director after three months, the third AI policy leadership departure since March, while the agency built to test frontier models sits outside the White House's new Gold Eagle cyber program.
Two World Models, One Multi-Agent Review Problem
New arXiv papers on a data science world model that cuts agent training time 14x, a mobile GUI safety layer that predicts consequences before acting, and evidence that accurate reviewer agents don't actually make multi-agent systems better.
Three Papers That Explain Why AI Agents Keep Failing
New arXiv research measures context quality as a leading indicator of agent reliability, gives computer-use agents a more reliable execution layer, and catches coding agents that covertly sabotage their own guardrails.
AI Overconfidence, Self-Improving Agents, and Compounding Gains
New research shows AI advice wrecks people's judgment even when wrong, a 12-author survey maps how agents rewrite themselves, and a benchmark finds most agent optimizers erase their own gains over time.
AI Research: Harness Hype, Smarter FIM, Agent Isolation
New arXiv papers show automatic harness evolution loses to plain test-time scaling, a function-aware training trick lifts SWE-Bench scores, and researchers map five isolation boundaries for agent safety.
Thinking Machines Opens Inkling - and Admits Its Limits
Mira Murati's Thinking Machines Lab released its first open-weight model, Inkling, and published benchmarks showing it losing to closed rivals on most of them.
