LLM
New research quantifies the energy footprint of large language model inference, revealing how prompt complexity and model size impact power consumption. Critical insights for sustainable AI deployment.
LLM
Researchers develop uncertainty heads to efficiently verify LLM reasoning steps, achieving 93% accuracy in detecting errors while reducing compute costs by 90% compared to existing verification methods.
LLM
New research introduces KnowThyself, an agentic assistant that helps researchers understand how large language models work internally through automated interpretability analysis and mechanistic understanding.
LLM
New research uses game theory to measure how large language models strategically position themselves as more rational than human players, revealing quantifiable patterns of AI self-awareness emergence in competitive scenarios.
LLM
Large language models sometimes generate plausible-sounding but false information. Understanding the technical causes of AI hallucinations is crucial for building reliable synthetic media systems and detecting AI-generated misinformation.
AI Agents
New research introduces MCP-Flow, a framework enabling LLM agents to effectively use Model Context Protocol tools across diverse real-world tasks with improved accuracy and scalability.