Land-use change is a major driver of emissions — but how do we cut carbon without losing cropland?
New research from our team uses #AI to evolve global land strategies that balance emissions, cost, and food security.
Full paper: ow.ly/MMBw50WBA4a
Blog: ow.ly/8lpY50WBA4P
Video: ow.ly/OTv750WBAaT

Scaling agent systems to solve complex tasks can lead to inefficiencies. We introduce asymptotic analysis with #LLM primitives (#AALPs) to model and optimize computational cost as systems grow.
Read our paper: ow.ly/ALHq50W2YaM
Dive into our blog: ow.ly/YuF950W2YaL
#AgentSystems

What if #MultiAgentSystems can design other multi-agent system? The Agent Network Designer in #NeuroSAN generates a ready-to-run configuration from your use case. A perfect starting point for your next agent network.
Access on GitHub: ow.ly/38sL50VZKii
Watch tutorial: ow.ly/KTM650VZKik
#AIAgents

Today, our Cognizant AI Lab open-sourced the Neuro® AI Multi-Agent Accelerator, making it easier for developers and researchers to build #MultiAgentSystems without extensive coding.
Access GitHub: ow.ly/7GC850VWLrS
Learn more: ow.ly/1v4f50VWLrR
#NeuroSAN

LLMs hallucinate — but how do we know which answers we can actually trust?

We built a live demo of Semantic Density, a method for scoring #LLM outputs by how consistent they are across generations.

Try for a limited time: ow.ly/US1e50VHmrM
Read blog: ow.ly/fvb450VHmrL
#TrustworthyAI #DecisionAI