artificial intelligence

VETNET: Assembling Benchmark Datasets for AI-Driven Veterinary Innovation

VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and veterinary medicine through standardized, high-quality datasets and multi-institutional collaboration across academic and non-academic sectors. This framework addresses t...

Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress

Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary check. We present Long-Horizon-Terminal-Bench 2.0 (LHTB 2.0), an extension of Long-Horizon-Terminal-Bench that preserves its 46 original tasks...

Correction Training for Diffusion Models

The method substantially improves upon previously reported FID scores for unguided generation and, notably, unconditional generation, where producing high-quality samples without guidance has been particularly challenging.

ClawLoop: Self-Improving Agents via Harness-Native Training Loop

The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents operate in sandbox workspaces and interact directly with file systems and shells. While numerous post-training pipelines exist for tool-use agents, their counterparts for...

The Silent Failures in Agentic Data Science

Large language models have significantly lowered the barrier to writing code. Naturally, this has also led to the development of data science agents that promise automating the design and execution of data science from task formulation to model development. Existing evaluations of agentic data science (agentic DS) asse...

Artificiality's First Exam: An LLM-Referenced C-test for Supraintelligence

Under the accelerated pace of AI development, any benchmark whose difficulty is calibrated with humans or current AI systems becomes circular, or saturates soon. How can we then evaluate machines that may become more capable than the current machines and the people who write the tests? One answer to the question hinges...

Proteomics in the age of artificial intelligence

Proteomics is now generating unprecedented volumes of mass spectrometry data, driven by increasingly powerful instrumentation and large-scale studies. Yet our ability to translate these measurements into biological knowledge remains a major challenge. The limiting factor is no longer data generation, but our capacity t...

Impact of artificial intelligence on project planning, scheduling, and control

Abstract The growing complexity of engineering and construction projects has revealed the weaknesses of conventional project planning, scheduling, and control approaches. Traditional tools often employ fixed assumptions, deterministic models, and past averages, which are inadequate for dealing with uncertainty, dynamic...

Artificial intelligence-enabled mass spectrometry-based metabolomics: current status and future prospects

A RISK-BASED QUALITY ASSURANCE FRAMEWORK FOR FHIR-BASED CLINICAL APPLICATIONS: ALIGNING VERIFICATION EFFORT WITH CLINICAL CONSEQUENCE

Verification effort for FHIR-based clinical systems can be allocated by clinical consequence rather than by test-suite convention, extending quality obligations into the operational-behavior layer where specification-conformant systems have been shown to fail.

Fabrication of Conformal FSS Radome Based on MOPSO inverse design and mask spraying

Conformal electromagnetic structures play an important role in modern aerospace and communication systems by combining spatial filtering capability with aerodynamic compatibility. However, transferring planar frequency-selective surfaces (FSSs) onto complex curved profiles can induce resonant-frequency drift and geomet...

From the Bazaar to the Algorithm: AI as a Technology Friend, Not a Human Friend

This paper explores the philosophical and architectural implications of artificial intelligence, arguing that AI should be understood as a historical continuation of tools rather than an independent agent. Focusing on the core concept of the "technology friend," the study examines the dangers of profit-driven design ch...