Researchers have developed QwenPaw-Data, an agentic data system designed to handle semantics, methodology, and execution for autonomous enterprise data analytics.
This study evaluates how structured reasoning interventions affect the strategic economic decision-making of instruction-following and reasoning-optimized language models.
This paper introduces a transformer-based foundation model designed to process multimodal event sequences for diverse financial predictive tasks.
This diagnostic study evaluates five machine learning model families to analyze how structural priors impact performance when governing physical dynamics are violated, using macroeconomic forecasting as a test case.
The Graph-Augmented Evolution framework combines large language models with reinforcement optimization to improve automated scientific discovery by addressing limitations in evolutionary program search.
Researchers introduce DUNE, a training-free refinement method that reduces artifacts in diffusion models by analyzing and stabilizing early-stage latent fluctuations.
This study explores whether looped architectures and adaptive exit strategies can improve the computational depth and reasoning of state-space language models.
This research explores norm enforcement mechanisms to prevent competitive AI agents from engaging in socially harmful behaviors within shared environments.
The ScaleCUA framework aims to improve computer use agents by combining verifiable task synthesis with efficient online reinforcement learning.
Chinese startup Novasilicon has secured multi-million yuan in seed funding to develop AI-driven chip design solutions.
Developers have successfully compiled the Firefox browser into WebAssembly, allowing it to run entirely within another web browser.
Researchers analyzed the citation faithfulness and coverage of a four-billion parameter research agent running locally on a consumer laptop.
Verrus, an Alphabet spin-out, is planning the construction of a new grid-reactive data center campus in Oregon to support infrastructure needs.
The paper introduces NameRank, a metric designed to measure how well large language models recognize specific researchers and tools within their parametric memory.
The paper introduces the Internet of Agentic Things, an architectural framework that connects autonomous AI agents across cloud, edge, and physical IoT layers.
This paper critiques the prevailing optimization culture in AI alignment, arguing that evaluating models solely on predefined, measurable axes fails to capture true value.
Chinese storage company Demingli projects a first-half net profit of up to 6.5 billion yuan, reversing a previous loss due to surging AI-driven storage demand.
The global artificial intelligence boom has driven up memory and solid-state drive prices in Shenzhen's Huaqiangbei electronics market, significantly increasing costs for PC builders.
This paper provides a theoretical analysis of the Muon optimizer to clarify the mechanisms behind its performance advantages in large-scale deep learning.
The authors introduced Monty, a framework designed to improve the accuracy of autoformalization by converting natural language specifications into executable software assertions.
The SteinGate framework introduces a new safety certification method for reinforcement learning to better mitigate rare but catastrophic risks.
The authors introduce STKAN, a spatio-temporal forecasting architecture that leverages Kolmogorov-Arnold Networks to better model complex traffic data.
A study evaluates the scalability and classroom performance of an AI tutoring agent that integrates retrieval-augmented generation with structured knowledge models.
The proposed ScanFocus framework addresses computational efficiency and precision in spatio-temporal video grounding through a coarse-to-fine processing approach.