What Spider 2.0 reveals about AI-generated SQL, business definitions and enterprise workflows—and how to evaluate answers before trusting them.

How Content ARCs connects AI content provenance, permissions and compensation—and where identification, attribution and governance remain unresolved.

How automatic prompt engineering generates and tests instructions, why high scores can mislead, and how to evaluate prompts on your own workflows.

How E2ETune learns database settings, why it reduces tuning time, and when its training costs make sense for a PostgreSQL fleet.

MCCoder connects AI-generated motion code with simulation and trajectory checks. Explore the experiment, original diagrams and a practical evaluation path.

Learn when AI fairness metrics mislead, how causal paths change an audit, and how to use Fairlearn and DoWhy without overstating the evidence.

Learn how EHR timestamps, joins and preparation can leak future information, and how to test a prediction pipeline against what was actually known.

A study of 628 participants tests whether contrastive AI explanations improve independent decisions. Explore the design, original charts and limits.

Why office AI agents lose context across turns, what the research tests, and how to evaluate tool arguments, meeting updates and recovery.

Explore graph features for Ethereum scam detection, the limits of eleven scam labels, and the tests needed before trusting a contract-risk model.