LLM research
Agent Drift: Measuring LLM Behavior Decay in Multi-Agent Systems
New research quantifies how LLM agents degrade over extended interactions in multi-agent systems, revealing critical reliability challenges for production AI deployments.
LLM research
New research quantifies how LLM agents degrade over extended interactions in multi-agent systems, revealing critical reliability challenges for production AI deployments.
LLM research
New research demonstrates how Sparse Autoencoders can steer LLM reasoning processes, enabling precise control over chain-of-thought behavior without retraining models.
neural architecture
New research explores whether large language models can creatively design novel neural network architectures rather than simply recombining existing patterns from training data.
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.
LLM research
New research explores whether deliberation improves LLM-based forecasting, examining how AI agents can leverage collective reasoning to make better predictions through structured discussion.
AI Agents
New research proposes multi-agent deliberation framework where AI agents debate decisions before acting, generating human-readable rationales that improve transparency and reduce harmful behaviors.
LLM research
New research introduces Behaviorally Calibrated Reinforcement Learning to reduce AI hallucinations by aligning model confidence with actual accuracy, improving reliability in language models.
LLM research
New research reveals that standard dense language models contain secret Mixture-of-Experts structures, challenging our understanding of neural network architectures and opening paths to more efficient AI.
LLM research
New research introduces explicit problem modeling for LLM agents, offering a structured approach to reduce hallucinations and improve reasoning reliability in AI systems.
LLM research
New research introduces ReflCtrl, a method for controlling when large language models engage in extended reasoning by manipulating internal representations rather than prompts.