Introducing LMX Brainstorming: Evolving Better Ideas With AI

Cognizant AI Lab introduces a new AI-powered brainstorming application that combines evolutionary AI, human creativity, and specialized AI agents to generate, evaluate, and improve ideas.

Every team has been in a brainstorming session that ends with a whiteboard full of ideas and no clear sense of which ones to pursue. AI has made that first part faster than ever. Ask for twenty ideas and you'll have them in seconds. But getting from that list to a solution you'd actually bet on still takes a lot of work.

That's because coming up with ideas is rarely the hardest part of brainstorming. The real challenge is figuring out which ideas have potential, how they compare against competing priorities, and whether combining elements from different approaches could lead to something better. Consider a business trying to improve customer experience while reducing operating costs. One idea might deliver exceptional service but be too expensive to implement. Another might be affordable but compromise quality. Neither is quite right on its own, but what if the strongest elements of both could be combined into something better?

Most AI brainstorming tools are good at generating suggestions, but finding the right combination, weighing tradeoffs, and improving on those initial ideas still largely falls to the user. What if brainstorming didn't have to stop at the first set of answers?

At Cognizant AI Lab, we've been exploring how evolutionary AI can take ideas further, using principles inspired by natural selection to discover new possibilities and improve existing solutions. Now, we're bringing that research together with our work in multi-agent systems.

Today, we're introducing LMX Brainstorming, a new AI-powered brainstorming application built on research from the Lab and powered by Cognizant Neuro® San. Now available through a BYOK model, it combines human creativity with evolutionary AI and a network of specialized agents that evaluate ideas, helping turn initial concepts into increasingly promising solutions.

What is LMX Brainstorming?

LMX Brainstorming is an AI-powered brainstorming application that takes inspiration from a process nature has been using for billions of years: evolution.

Just as offspring inherit characteristics from their parents, new ideas can inherit useful elements from existing solutions while introducing variations of their own. Through a process known as evolutionary computation, promising characteristics can be carried forward and recombined over successive generations, allowing new possibilities to emerge from what came before.

LMX Brainstorming brings this approach into an interactive experience where users can explore business challenges, develop creative concepts, or work through everyday decisions. Rather than simply requesting a list of suggestions, users contribute their own ideas and establish the objectives they want to achieve. The application then explores new combinations, evaluates the results, and uses that feedback to guide further refinement.

This becomes particularly valuable when problems involve competing priorities. A company might want to improve customer satisfaction while reducing costs, or develop a more sustainable product without compromising performance. There's rarely a single perfect answer, and improving one outcome may mean sacrificing another. LMX Brainstorming helps users explore these tradeoffs and uncover promising approaches that might not emerge from conventional brainstorming alone.

From award-winning research to a working application

LMX Brainstorming builds on two research efforts from Cognizant AI Lab that explore how evolutionary computation can extend the capabilities of AI and human problem-solving.

The first is Language Model Crossover (LMX), research conducted with academic and industry collaborators that investigates how language models can generate new solutions by recombining characteristics from existing examples.

Traditional evolutionary algorithms often require specialized mechanisms for combining and modifying candidate solutions. LMX takes a different approach, using the pattern-recognition and generative capabilities of language models to produce meaningful variations without requiring a custom crossover method for every problem. The research demonstrated this approach across domains including mathematical expressions, text-to-image prompts, and computer programs, highlighting its potential as a flexible method for evolutionary optimization.

The second foundation is RHEA (Realizing Human Expertise through AI), a framework that explores how AI can build on human expertise to discover improved solutions. Rather than treating individual contributions as competing final answers, RHEA uses evolutionary methods to identify and recombine valuable characteristics from different approaches while balancing multiple objectives.

The research was demonstrated through the XPRIZE Pandemic Response Challenge, where RHEA combined expert-designed strategies to explore solutions that balanced public health outcomes with the economic and social costs of interventions. This work earned the Gold Humies Award at GECCO 2025, recognizing its achievements in human-competitive evolutionary computation.

Together, these research efforts provide the foundation for LMX Brainstorming. RHEA demonstrates the value of starting with human knowledge and exploring competing objectives, while LMX provides a flexible way to generate new solutions from existing ideas. The application translates these principles into an accessible experience that anyone can explore.

How LMX Brainstorming works

You don't need to understand evolutionary algorithms to use LMX Brainstorming. The application guides users through a process that begins with a problem and develops potential solutions through successive rounds of AI-powered evolution and evaluation.

Start with a challenge and define your objectives

Users can begin with a brainstorming topic of their own or browse ready-made starting points spanning different industries, business challenges, and everyday decisions. From there, they identify the objectives they want to optimize.

For example, a product team might want to maximize functionality and sustainability while minimizing manufacturing costs. These goals don't always align, which is precisely what makes the challenge interesting. By defining the objectives upfront, users establish what matters most and provide a basis for evaluating potential solutions.

Bring your own ideas to the table

Next, users contribute their initial ideas, approaches, or potential solutions. These provide the context and starting material for the evolutionary process, reflecting the human-first approach behind the RHEA research.

And the ideas don't need to be perfect. An approach that falls short in one area might still contain an insight worth keeping, particularly when combined with elements from another solution.

Let AI explore new combinations

Using the principles of Language Model Crossover, AI begins generating new candidates by identifying and recombining characteristics from existing ideas.

Instead of producing an unrelated list of suggestions, the system builds on what came before, exploring variations and combinations that could offer improvements. Over successive generations, promising characteristics can be carried forward while new possibilities continue to emerge.

Evaluate and refine the results

As ideas evolve, they're evaluated against the objectives established at the beginning of the process. This helps identify which candidates show the most promise and where further improvements might be possible.

The evaluation feedback informs subsequent generations, creating an iterative process in which ideas can be explored, compared, and refined rather than treated as finished answers after a single round.

Powered by Cognizant Neuro San: AI agents that evaluate ideas

Generating new ideas is only half of the equation. For an evolutionary process to be useful, it also needs a way to determine which candidates are worth pursuing. That's where Cognizant Neuro® San comes in.

Neuro San is Cognizant AI Lab's open-source framework for building and orchestrating networks of AI agents. Rather than relying on a single AI model to handle every part of a task, it enables specialized agents to work together within a coordinated system.

In LMX Brainstorming, Neuro San powers the agents responsible for evaluating the ideas generated through evolution. These agents assess candidates against the user's defined objectives, assign ratings, and provide feedback on their strengths and weaknesses. For problems involving competing priorities, this makes it possible to examine how different ideas perform across the dimensions that matter most.

Consider a product concept that scores highly on functionality but falls short on affordability. Another might be inexpensive to produce but less innovative. Agent-based evaluation can help surface these tradeoffs, providing feedback that informs which characteristics are worth preserving, combining, or improving in subsequent generations.

This creates a relationship between two complementary AI capabilities. Evolutionary AI explores the space of possible solutions, while Neuro San-powered agents provide the evaluation and feedback needed to guide that exploration. The agents aren't simply generating more ideas. They're helping assess the ideas that emerge, making evaluation an integral part of the process.

The integration also demonstrates how technologies developed at Cognizant AI Lab can work together. By combining our research in evolutionary computation with Neuro San's multi-agent orchestration capabilities, LMX Brainstorming brings generation, evaluation, and iterative improvement into a single application.

What makes LMX Brainstorming different from other AI brainstorming tools?

AI assistants like ChatGPT are useful for exploring topics, generating initial ideas, and developing concepts through conversation. LMX Brainstorming is designed for a different part of the problem-solving process: systematically exploring, evaluating, and improving ideas against specific goals.

Ideas evolve rather than starting from scratch: Instead of repeatedly asking for another set of suggestions, LMX Brainstorming builds on existing candidates, combining promising characteristics to explore new possibilities. Each generation provides material for further experimentation and refinement.

Evaluation is built into the process: With Neuro San powering the agent network, candidate solutions receive ratings and feedback against user-defined objectives. This creates a more structured way to identify promising ideas and decide which directions to explore further.

Competing objectives are part of the process. Many real-world problems involve balancing priorities that don't naturally align. LMX Brainstorming allows users to define multiple objectives and explore the Pareto front, a set of solutions where improving one objective would require compromising another. Rather than identifying a single winner, it helps users compare the strongest tradeoffs and decide which solution best fits their priorities.

Human creativity remains the starting point: The application doesn't assume AI should come up with everything on its own. By building on human contributions, it brings domain knowledge, experience, and individual perspectives into the evolutionary process.

The goal isn't to replace conversational AI or human judgment. It's to offer another way to approach problems where the first idea is rarely the final answer, and where exploring combinations and tradeoffs can lead to more promising solutions. Agent-based ratings provide useful guidance, but real-world testing remains important for validating the results.

How AI Innovation Leaders Used LMX Brainstorming

LMX Brainstorming was used during the AI-First Enterprises Workshop, held in collaboration with the World Economic Forum (WEF). The workshop brought together more than 35 AI innovation leaders from industry, AI-native companies, academia, and the investment community to explore the opportunities and challenges of building AI-first organizations.

During a collaborative whiteboard brainstorming session, participants contributed ideas around a shared challenge, which were then entered into LMX Brainstorming for evaluation and further exploration. The application rated the participants' ideas and generated new possibilities by building on their contributions. When the results were revealed, the group could see how their original ideas had been evaluated alongside the new AI-generated concepts, sparking further discussion and giving participants fresh ideas to build on.

The session demonstrated how LMX Brainstorming can become part of a collaborative brainstorming process, bringing AI-generated possibilities back into the conversation rather than treating them as final answers. And while the workshop focused on AI-first enterprises, the application can support a much broader range of challenges.

Where can evolutionary brainstorming make a difference?

The approach behind LMX Brainstorming isn't limited to one industry or type of challenge. Any problem with multiple potential solutions and competing objectives can provide an opportunity to explore what evolutionary AI has to offer.

A retail business might investigate ways to improve customer experience while controlling operating costs. A manufacturing team could explore product designs that balance performance, sustainability, and affordability. A marketing team might experiment with campaign concepts that combine originality, clarity, and audience appeal.

And it doesn't have to be a business problem. The same principles can be applied to everyday decisions, where finding the right solution often means weighing different priorities and exploring alternatives.

What makes these problems interesting is that the strongest answer may not exist among the initial suggestions. It may emerge from combining elements of several ideas, testing different approaches, and continuing to refine them.

Try LMX Brainstorming today

At Cognizant AI Lab, we're interested in what happens when AI moves beyond producing individual answers and becomes part of a broader process of discovery, evaluation, and improvement.

LMX Brainstorming brings that thinking into a new application, combining our research in evolutionary AI with the multi-agent capabilities of Neuro San to help people explore ideas in a different way.

LMX Brainstorming is now available to try using a Bring Your Own Key (BYOK) model. Start with your own challenge or explore a ready-made brainstorming topic, define what matters most, and see where the evolutionary process takes your ideas.