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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.LG English(EN) ·

    Distilling latent electrostatics from foundation machine learning interatomic potentials

    Researchers have developed a method called Latent Ewald Summation (LES) to extract electrostatic properties from foundation machine learning interatomic potentials (MLIPs). This technique allows for the creation of more efficient MLIPs that can model long-range interactions and electrical responses, which are crucial for many chemical and materials science simulations. The study benchmarks LES-distilled models derived from various foundation MLIPs, demonstrating their ability to predict infrared spectra and Born effective charge tensors. AI

    IMPACT Enables more efficient and physically accurate simulations by extracting electrostatic properties from existing AI potentials.

  2. TOOL · arXiv cs.CL English(EN) ·

    Catalyst-Agent: Autonomous heterogeneous catalyst screening with an LLM Agent

    Researchers have developed Catalyst-Agent, an AI system designed to autonomously screen for novel catalysts. This LLM-powered agent utilizes material databases and computational models to suggest structural modifications and calculate adsorption energies. Tested on key reactions like ORR, NRR, and CO2RR, Catalyst-Agent demonstrated a success rate of 33-41% and converged on successful materials within 1-4 trials on average, showcasing the potential of AI agents in accelerating scientific discovery. AI

    IMPACT Accelerates scientific discovery by automating complex material screening processes.

  3. RESEARCH · arXiv cs.LG English(EN) ·

    Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators

    Researchers have developed Tessera, a new architecture designed to securely stream model weights to edge accelerators in Unified Memory Architecture (UMA) systems. This approach addresses the challenge of protecting proprietary deep neural networks on commodity devices by enabling inline, cache-line granularity decryption of weights. Tessera intercepts memory bursts and decrypts them in parallel with DRAM fetches, streaming plaintext directly into isolated NPU SRAM with minimal bandwidth overhead. AI

    Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators

    IMPACT Enhances security for deploying proprietary models on edge devices by enabling efficient, hardware-backed DRM.