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3月25日周三
3月24日周二
  5. Mistral AI

    Speaking of Voxtral

    【RSS 原文 · 未经 AI 筛选或翻译】Voxtral TTS: A frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents.

3月23日周一
  1. OpenAI News

    Creating with Sora Safely

    【RSS 原文 · 未经 AI 筛选或翻译】To address the novel safety challenges posed by a state-of-the-art video model as well as a new social creation platform, we’ve built Sora 2 and the Sora app with safety at the foundation. Our approach is anchored in concrete protections.

3月21日周六
3月19日周四
3月18日周三
  5. Mistral AI

    Introducing Forge

    【RSS 原文 · 未经 AI 筛选或翻译】Today, we’re introducing Forge, a system for enterprises to build frontier-grade AI models grounded in their proprietary knowledge.

3月17日周二
3月16日周一
3月13日周五
  1. Berkeley AI Research

    Identifying Interactions at Scale for LLMs

    【RSS 原文 · 未经 AI 筛选或翻译】--> Understanding the behavior of complex machine learning systems, particularly Large Language Models (LLMs), is a critical challenge in modern artificial intelligence. Interpretability research aims to make the decision-making process more transparent to model builders and impacted humans, a step toward safer and more trustworthy AI. To gain a comprehensive understanding, we can analyze these systems through different lenses: feature attribution , which isolates the specific input features driving a prediction ( Lundberg & Lee, 2017 ; Ribeiro et al., 2022 ); data attribution , which links model behaviors to influential training examples ( Koh & Liang, 2017 ; Ilyas et al., 2022 ); and mechanistic interpretability , which dissects the functions of internal components ( Conmy et al., 2023 ; Sharkey et al., 2025 ). Across these perspectives, the same fundamental hurdle persists: complexity at scale . Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns. To achieve state-of-the-art performance, models synthesize complex feature relationships, find shared patterns from diverse training examples, and process information through highly interconnected internal components. Therefore, grounded or reality-checked interpretability methods must also be able to capture these influential interactions . As the number of features, training data points, and model components grow, the number of potential interactions grows exponentially, making exhaustive analysis computationally infeasible. In this blog post, we describe the fundamental ideas behind SPEX and ProxySPEX , algorithms capable of identifying these critical interactions at scale. Attribution through Ablation Central to our approach is the concept of ablation , measuring influence by observing what changes when

3月12日周四
3月11日周三
3月10日周二
3月9日周一
  2. OpenAI News

    OpenAI to acquire Promptfoo

    【RSS 原文 · 未经 AI 筛选或翻译】OpenAI is acquiring Promptfoo, an AI security platform that helps enterprises identify and remediate vulnerabilities in AI systems during development.