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

  1. RESEARCH · arXiv cs.AI English(EN) · · [2 sources]

    DASH: Fast Differentiable Architecture Search for Hybrid Attention in Minutes on a Single GPU

    Researchers have developed DASH, a novel framework for efficiently designing hybrid attention architectures in large language models. This differentiable approach significantly speeds up the architecture search process, reducing the computational cost from billions of tokens to just millions. DASH outperforms existing methods and even surpasses models like Jet-Nemotron in certain benchmarks, all within minutes on a single GPU. AI

    DASH: Fast Differentiable Architecture Search for Hybrid Attention in Minutes on a Single GPU

    IMPACT Enables rapid, low-cost discovery of optimized LLM architectures, potentially accelerating inference efficiency across the industry.

  2. TOOL · arXiv cs.IR (Information Retrieval) English(EN) ·

    NewsLens: A Multi-Agent Framework for Adversarial News Bias Navigation

    Researchers have developed NewsLens, a novel five-agent framework designed to navigate and expose nuanced aspects of news bias beyond simple classification. This system utilizes a collaborative pipeline of agents, including fact verifiers and framing analysts, to deconstruct articles into interpretable framing maps. The framework aims to reveal ideological omissions and rhetorical manipulation, offering a more structured approach to understanding media bias. Evaluations using Qwen2.5-3B-Instruct and Mistral 7B models on geopolitical events indicate that center outlets exhibit higher perspective divergence, while conservative-framing outlets show greater manipulation. AI

    IMPACT Offers a more sophisticated method for analyzing news bias, moving beyond simple classification to expose omissions and manipulation.