An AI harness, or agent harness, is the software around a language model that turns it into an agent. What it does, its parts, examples and how to choose one.
Cheaper and more easily accessible AI developer tools are part of the reason why traditional retailers are better at marketing personalized experiences to customers.
The surge comes amid an intensifying global race among technology companies to expand AI computing capabilities, driving demand for advanced memory chips and server equipment.
The surge comes amid an intensifying global race among technology companies to expand AI computing capabilities, driving demand for advanced memory chips and server equipment.
Quick ReadSung Cho at Goldman Sachs picks Lumentum (LITE), up 600% in one year, and Coherent (COHR) as AI infrastructure rotates from GPUs to optical connectivity.Optical capacity is extremely hard to scale while inference demand accelerates,
Days after Google unveiled Gemini 4 Carbon for cyber defenders, an updated Carbon checkpoint has surfaced on Arena. Testers say it's faster, better at 3D, and thinks a lot longer.
El Museo de Arte Sacro 'Tesoros de La Concepción' alberga una de las colecciones más valiosas de Canarias relacionadas con la liturgia y el culto. Sus diferentes estancias están llenas de piezas y contenidos muy diversos, como imágenes, pinturas, mobiliario ritual, andas procesionales, vasos sagrados, custodias, cantorales, instrumentos musicales y ornamentos. Una visita por sus instalaciones supone todo un recorrido por un legado patrimonial de cinco siglos de historia. (Virtual tour…
Data attribution methods for Language Models (LMs) are typically evaluated using downstream tasks like fact retrieval. However, existing evaluations vary multiple dataset properties simultaneously, making it impossible to isolate exactly why an attribution algorithm fails. To diagnose these failures, we explore the design space of LLM data attribution, with a primary focus on evaluation. We introduce the Controlled Attribution Benchmark (CATT) for fact tracing, in which we fine-tune LLMs on entirely fictitious knowledge, providing unambiguous ground truth for attribution. This synthetic setup allows us to systematically alter one dataset configuration at a time, such as document redundancy and lexical distractors. With this configurable evaluation, we demonstrate that redundancy and distractors consistently degrade attribution performance. Rather than proposing a new estimator, we examine two design choices that are underexplored: the loss used to compute gradients and how per-token attribution scores are aggregated into a document score. We find that the true causal evidence within a training document is highly localized, but standard methods dilute this evidence into noise by…
Chinese artificial intelligence start-up DeepSeek has previewed a plan for a more autonomous AI agent that can handle multiple tasks with minimal human intervention, as it steps up competition with US rivals OpenAI and Anthropic in developing coding assistants. The proposed…
The prompt is built automatically as "" * num_pages + "Multi page parsing.", and each page is encoded as one global view (base mode). The output contains one per input page, in the same order as image_files.
