LLM Research
New research reveals a troubling disconnect in large language models: they can produce logically valid reasoning chains yet arrive at incorrect final answers, raising questions about AI reliability and trust.
AI Safety
New research reveals that AI models' step-by-step reasoning often doesn't reflect their actual decision process, raising critical questions about trust, safety, and the reliability of AI systems used for content authentication.
AI Safety
New research reveals frontier language models frequently skip or contradict their own chain-of-thought reasoning, raising serious questions about AI transparency and the reliability of systems that "show their work."
AI research
Modern AI models use test-time compute to improve responses through extended reasoning. Three key techniques—chain-of-thought, self-consistency, and reward-guided search—are reshaping how AI systems approach complex problems.
LLM Research
New research shows that requiring LLMs to think step-by-step before responding can backfire in conversational settings, making AI agents appear cold and disengaged to users.
LLM Research
New research introduces Accordion-Thinking, a self-regulated approach that compresses reasoning steps dynamically to improve LLM efficiency while maintaining readable chain-of-thought outputs.
AI Safety
New research shows AI models frequently omit key reasoning steps in their explanations, raising critical questions about whether we can trust AI transparency and the reliability of chain-of-thought prompting.