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

  1. Towards Code-Oriented LM Embeddings for Surrogate-Assisted Neural Architecture Search

    Researchers have developed a novel method called Code-Oriented LM Embeddings (COLE) to improve Neural Architecture Search (NAS). This technique uses off-the-shelf language models to generate embeddings from code representations of neural architectures, bypassing the need for expensive fine-tuning or complex feature engineering. Experiments on NAS-Bench-201 and einspace demonstrated that COLE embeddings outperform other text-based encodings and significantly reduce the evaluation budget required to find high-performing architectures. AI

    IMPACT Introduces a more efficient method for designing neural networks, potentially accelerating AI model development.

  2. Butter is joining Modal

    Modal has acquired Butter, a company specializing in agent harness engineering. The acquisition brings Butter's founder, Erik Dunteman, and researcher Raymond Tana to Modal's Sandbox team. Their expertise, particularly in creating lightweight sandboxes like bVisor, is expected to enhance Modal Sandboxes into a leading product. AI

    Butter is joining Modal

    IMPACT Enhances specialized tooling for AI agent development and deployment.