ARMAP: Scaling Autonomous Agents via Automatic Reward Modeling and PlanningLLMs struggle with multi-step decision-making and real-world interaction. The ARMAP framework introduced by Chen et al. (2025) and…11h ago11h ago
Structured Retrieval Orchestration: Why Multi-Agent Systems Need More Than RAGThe Limitation of RAG as an Isolated System4d ago14d ago1
I Read The Chief AI Officer’s Handbook So You Don’t Have To — Here’s What Actually MattersAI leadership is evolving, and The Chief AI Officer’s Handbook attempts to capture what it takes to lead AI initiatives successfully. But…4d ago4d ago
Why Trusting LLM Outputs in Production Can Be Misleading and How We Can Quantify ItWhy Should We Care About LLM Uncertainty?5d ago5d ago
MARCO: Multi-Agent Real-time Chat OrchestrationReal-world deployment of multi-agent LLM based automation still faces major hurdles — inconsistencies, hallucinations, and inefficient…6d ago6d ago
The Internet of Agents (IoA): Protocol for Autonomous AI CollaborationWhy Multi-Agent Systems Need a RethinkFeb 16Feb 16
DeepSeek-R1: RL for LLMs RethoughtFor years, supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) have been the dominant training methods for…Jan 311Jan 311
Reimagining Plugin Orchestration with Turn-Based Multi-Agent Reinforcement LearningModern AI systems often rely on orchestrating multiple interconnected components — plugins for data sourcing, analysis, and advanced…Jan 182Jan 182
Scaling Search with PEM-BiHSEfficient search algorithms form the backbone of solving complex optimization and planning problems. As the scale of these problems grows…Jan 10Jan 10
GroverGPT: Quantum Simulations with Large Language ModelsQuantum computing is a transformative field, offering the promise of solving computational problems exponentially faster than classical…Jan 5Jan 5
