Diffusion Models
New research applies quantum physics path integral methods to understand dissipative dynamics in generative AI, offering theoretical foundations for diffusion models powering modern image and video synthesis.
AI Agents
New survey paper comprehensively examines AI agent system architectures, their applications across domains, and frameworks for evaluating autonomous AI behavior and capabilities.
digital twins
New survey explores how Digital Twin AI evolves from LLMs to world models, enabling AI systems to simulate and predict physical reality with unprecedented accuracy.
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.
LLM research
New research exposes how large language models systematically fail to recognize brands from non-Western cultures, creating an 'existence gap' in AI-mediated discovery systems.
LLM research
New research reveals a fundamental paradox in LLM self-correction: models that excel at fixing errors often produce fewer initial mistakes, while error-prone models struggle to correct themselves.
AI Security
New research introduces an open framework for training security models that detect temporal attack patterns in multi-agent AI workflows through trace-based analysis.
LLM
New research introduces cognitive artifacts that maintain coherence across extended LLM conversations, addressing the fundamental challenge of context degradation in long interactions.
LangChain
A technical breakdown of four popular LLM development tools from the LangChain ecosystem, covering when to use each framework for building AI applications.
deepfake detection
Major Japanese telecom companies are developing mobile applications to detect AI-generated voice clones, addressing the growing threat of audio deepfakes in real-time phone calls.
LLM Agents
Researchers challenge the assumption that LLM agents work reliably with perfect APIs, revealing how real-world complexity degrades AI performance.
AI safety
New research explores how LLM-powered agents may develop biases against humans based on belief systems, revealing critical vulnerabilities in autonomous AI decision-making.