Update README.md

README.md

@@ -15,17 +15,32 @@ For instance, Graph-PReFLexOR demonstrates creative reasoning by generating poet
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/D-j4cBOcTrFqLrM-DXYq_.png)
 
-Figure: Overview of the approach used in this paper, presenting The concept of multi-step reflection (panel a), graph-based modeling of context and tasks (panel b), abstract pattern formulation (panel c), and finally, integrated in the multi-stage reasoning mechanisms (panel d).
+Figure 1: Overview of the approach used in this paper, presenting The concept of multi-step reflection (panel a), graph-based modeling of context and tasks (panel b), abstract pattern formulation (panel c), and finally, integrated in the multi-stage reasoning mechanisms (panel d).
 
 ## Graph reasoning examples
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/jTAqcNvICvaODJAc5aOou.png)
 
-Figure: Visualization of the integrated knowledge graph based on the graph reasoning strategy invoking knowledge expansion. The data is organized here effectively as an integrated graph rather than by prompt, and laid out using the Fruchterman Reingold layout algorithm. Panel a, node size by node degree. Panel b, node size by page rank. Panel c, node size by bridging coefficient. Panel d, node size by domain prestige (metric defined by fraction of nodes within a network that are directly od indirectly pointing to it). 
+Figure 2: Visualization of the integrated knowledge graph based on the graph reasoning strategy invoking knowledge expansion. The data is organized here effectively as an integrated graph rather than by prompt, and laid out using the Fruchterman Reingold layout algorithm. Panel a, node size by node degree. Panel b, node size by page rank. Panel c, node size by bridging coefficient. Panel d, node size by domain prestige (metric defined by fraction of nodes within a network that are directly od indirectly pointing to it). 
 
 ## Knowledge garden algorithm
 
 
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/HMIGSBDN7wXL9S3AF-4aN.png)
 
-Grown knowledge graph based on the prompt ```Discuss protein design based on collagen and elastin.``` Here, the agentic system is specifically charged to develop new questions that integrate dissimilar fields such as philosophy or art. The visualization depicts nodes sized and colored by page rank, with top nodes being ```Biolumniscent Biomaterial``` (most significant node also per node degree), ```Protein```, ```Installation```, ```Elastin``` and ```Concept```. A unique feature is the incorporation of the concept of ```thin places```, a concept drawn from various mythological and spiritual traditions that invokes sites or moments where the boundary between the physical world and a transcendent realm is perceived to be exceptionally thin or permeable. The model identifies this as an interesting association for this particular task, which is particularly interesting as a way to create a new concept that incorporates highly multidisciplinary relationships. 
+Figure 3: Grown knowledge graph based on the prompt ```Discuss protein design based on collagen and elastin.``` Here, the agentic system is specifically charged to develop new questions that integrate dissimilar fields such as philosophy or art. The visualization depicts nodes sized and colored by page rank, with top nodes being ```Biolumniscent Biomaterial``` (most significant node also per node degree), ```Protein```, ```Installation```, ```Elastin``` and ```Concept```. A unique feature is the incorporation of the concept of ```thin places```, a concept drawn from various mythological and spiritual traditions that invokes sites or moments where the boundary between the physical world and a transcendent realm is perceived to be exceptionally thin or permeable. The model identifies this as an interesting association for this particular task, which is particularly interesting as a way to create a new concept that incorporates highly multidisciplinary relationships. 
+
+
+## Reference
+```bibtex
+@article{buehler2025GraphPRefLexOR,
+      title={In-situ graph reasoning and knowledge expansion using Graph-PReFLexOR}, 
+      author={Markus J. Buehler},
+      year={2025},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.AI},
+      url={https://arxiv.org/abs/XXX.YYYYY}, 
+}
+```
+```