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 research
New research examines how users develop calibrated trust strategies when interacting with hallucination-prone LLMs, offering frameworks for safer human-AI collaboration.
DeepMind
Google DeepMind deepens collaboration with UK AI Security Institute on frontier AI safety evaluation, establishing frameworks that could shape how synthetic media and generative models are assessed globally.
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 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.
LLM Verification
Researchers introduce BEAVER, an efficient deterministic verification system for large language models that ensures reliable and consistent output validation for AI safety applications.
AI safety
ArXiv research introduces a co-improvement paradigm where humans and AI systems evolve together toward safer superintelligence, addressing critical alignment challenges.
deepfake detection
Monash University partners with international institutions to develop advanced deepfake detection methods and combat AI-driven misinformation across digital platforms.
AI safety
New research introduces Factor(T,U) framework that uses task decomposition to improve monitoring of untrusted AI systems, addressing critical safety challenges as models become more capable.
LLM Security
Data poisoning attacks targeting large language models can manipulate outputs by corrupting training datasets. Understanding these vulnerabilities is critical for maintaining AI system integrity and authenticity.
AI safety
Comprehensive technical guide to implementing AI safety guardrails, from prompt-based filtering to advanced validation architectures. Covers practical methods for ensuring secure and relevant AI interactions with code examples.