arXiv:2606. 05384v1 Announce Type: new Abstract: LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators. These pipelines typically assume that judgments are stable properties of fixed inputs. We show that this assumpti

📰미디어arXiv cs.AI

원문

1일 전

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

arXiv:2606. 05357v1 Announce Type: new Abstract: Purpose: To develop an interpretable and trustworthy AI framework that combines deep learning based MRI Osteoarthritis Knee Score (MOAKS) prediction with interpretable statistical modeling to study structure-pain relationships at scale using data from

📰미디어arXiv cs.AI

원문

1일 전

SentinelBench: A Benchmark for Long-Running Monitoring Agents

arXiv:2606. 05342v1 Announce Type: new Abstract: AI agents are increasingly asked to carry out work that spans minutes, hours, or longer. Yet the default model of agent behavior is continuous action: issuing tool calls, refreshing pages, searching for alternatives, or otherwise trying to force progr

📰미디어arXiv cs.AI

원문

LLMs Predict Words. LCMs Predict Ideas.

본문 미리보기

관련 글

Thousand Token Wood: shipping a multi-agent economy on a 3B model

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

An interpretable and trustworthy AI framework for large-scale longitudinal structure-pain association studies using data from the Osteoarthritis Initiative (OAI)

SentinelBench: A Benchmark for Long-Running Monitoring Agents