# Semantic Navigation for AI-assisted Ideation

arXiv: https://arxiv.org/abs/2411.03575 · HTML: https://mlsanity.com/paper/2411.03575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [DeepResearchEco: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology](https://mlsanity.com/paper/2507.10522/) (2025)

## Abstract (arXiv)

We present a novel AI-based ideation assistant and evaluate it in a user study with a group of innovators. The key contribution of our work is twofold: we propose a method of idea exploration in a constrained domain by means of LLM-supported semantic navigation of problem and solution spaces, and employ novel automated data input filtering to improve generations. We found that semantic exploration is preferred to the traditional prompt-output interactions, measured both in explicit survey rankings, and in terms of innovation assistant engagement, where 2.1x more generations were performed using semantic exploration. We also show that filtering input data with metrics such as relevancy, coherence and human alignment leads to improved generations in the same metrics as well as enhanced quality of experience among innovators.