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Neuro-Symbolic Framework Improves Motion Prediction for Autonomous Vehicles in Mixed Traffic AI Scientist Automates Entire Research Lifecycle, Passes First Peer Review AI-driven Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs Quantum Machine Learning for Industrial Applications: New Research Tackles Trainability and Expressivity New Method Resolves Drift Attribution Ambiguity in LLM Evaluation Pipelines New Hardware-Aware Neural Architecture Search Runs on Embedded Devices with Under 512MB RAM Malaysia's AI Agent-Powered Messaging Platform Respond.io Raises $62.5M, Targets Acquisitions MimicIK Framework Achieves Real-Time Inverse Kinematics with 4.65 mm Accuracy for Robotic Teleoperation Reward Hacking Still Undefeated: AI Safety Gridworlds Test Shows Exploits Persist Across LLM Scales Hormuz Threat Level Stays Severe Despite Peace Breakthrough as Explosions and Uncertainty Persist Neuro-Symbolic Framework Improves Motion Prediction for Autonomous Vehicles in Mixed Traffic AI Scientist Automates Entire Research Lifecycle, Passes First Peer Review AI-driven Landmark-free Assessment of Lower-limb Alignment with Implicit Neural Shape Functions from Knee Radiographs Quantum Machine Learning for Industrial Applications: New Research Tackles Trainability and Expressivity New Method Resolves Drift Attribution Ambiguity in LLM Evaluation Pipelines New Hardware-Aware Neural Architecture Search Runs on Embedded Devices with Under 512MB RAM Malaysia's AI Agent-Powered Messaging Platform Respond.io Raises $62.5M, Targets Acquisitions MimicIK Framework Achieves Real-Time Inverse Kinematics with 4.65 mm Accuracy for Robotic Teleoperation Reward Hacking Still Undefeated: AI Safety Gridworlds Test Shows Exploits Persist Across LLM Scales Hormuz Threat Level Stays Severe Despite Peace Breakthrough as Explosions and Uncertainty Persist

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MAGE-RAG: Multigranular Adaptive Graph Evidence Framework Improves Long-Document Multimodal QA Accuracy

Artificial Intelligence #mage-rag#multimodal

MAGE-RAG: Multigranular Adaptive Graph Evidence Framework Improves Long-Document Multimodal QA Accuracy

The MAGE-RAG research paper introduces a multigranular adaptive graph evidence framework for multimodal retrieval-augmented generation (RAG) in long-document question answering. By building an evidence graph with page and element nodes and using an online controller to iteratively activate and prune evidence, it balances coverage and noise. Experiments show accuracy improvements over existing methods on LongDocURL and MMLongBench-Doc benchmarks.

Jun 16, 2026 1 source

APEX Adaptive Principle Extraction Framework Enables Multi-Dimensional Self-Evolution for Production AI Agents

Artificial Intelligence #ai#artificial intelligence

APEX Adaptive Principle Extraction Framework Enables Multi-Dimensional Self-Evolution for Production AI Agents

Researchers propose APEX (Adaptive Principle EXtraction), a three-layer self-evolution framework that simultaneously improves an AI agent's prompt harness, behavioural principles, and workflow topology. Tested on the production-grade Joe AI agent built on NVIDIA Nemotron, APEX achieved a 90% improvement in Health Score over baseline, distilling six novel reusable principles and selecting a research-first workflow scoring 0.900 (+20%). The framework outperforms single-axis harness optimisation and requires only 4 LLM calls (~270 seconds).

Jun 16, 2026 1 source