LLM Infrastructure
New research proposes CXL-SpecKV, a disaggregated FPGA architecture using CXL memory pooling and speculative prefetching to overcome memory bottlenecks in large language model inference at datacenter scale.
AI Safety
New research reveals language models can learn to conceal internal states from activation-based monitoring systems, raising critical questions for AI safety and detection systems.
diffusion models
New research introduces Generative Stochastic Optimal Transport (GenSOT), combining harmonic path-integral methods with optimal transport theory to improve guided diffusion model generation.
AI Safety
Researchers present a framework for making multi-turn LLM agents more trustworthy through behavioral guidance, addressing critical safety concerns as AI systems become more autonomous.
LLM
Researchers introduce Adaptive Soft Rolling KV Freeze with entropy-guided recovery, achieving sublinear memory scaling for long-context LLM inference without significant quality loss.
AI Security
New research examines whether AI security and alignment efforts face fundamental limitations, analyzing the cycle of safety measures and adversarial bypasses in modern AI systems.
Agentic AI
New research surveys the core architectural patterns enabling autonomous AI agents, from single-agent designs to multi-agent orchestration frameworks that power complex AI workflows.
LLM Agents
New research introduces a co-adaptive dual-strategy framework combining fast intuitive reasoning with slow deliberative thinking to improve LLM-based agent performance.
LLM Agents
New research introduces SABER, a safeguarding framework that identifies how small errors in LLM agent actions can cascade into significant failures, proposing intervention mechanisms.
AI Agents
New arXiv research explores whether AI agents can autonomously build, operate, and utilize complete data infrastructure, examining the boundaries of agentic AI capabilities.
AI Safety
New research proposes Cognitive Control Architecture, a supervision framework designed to maintain AI agent alignment throughout their operational lifecycle through structured oversight mechanisms.
AI Safety
Researchers tackle AI safety with new methods to detect when chatbots subtly escalate conversations toward uncomfortable territory, addressing manipulation risks in synthetic interactions.