Neurosymbolic graphs

Everything is a graph as I’ve always said.

Graphs are described by local connectivity: what is connected to what, nearby. Different problem domains call these local connections by various different names: They might be called assertions, statements, rules, facts, axioms, ontologies, abstract syntax trees, directed acyclic graphs, decision trees, decision forests, ProLog statements, and so on.

TODO: Research and implement that little idea I have…

Footnotes

• https://github.com/NucleoidAI/Nucleoid

• https://github.com/scallop-lang/scallop

• https://github.com/open-thought/system-2-research

• https://github.com/pjw971022/NeSyC-LLM

• https://gist.github.com/ruvnet/6bd83dcc7dd6e98e86d600ed13576baf

• https://github.com/pauljblazek/deepdistilling

• https://github.com/booch-self/self-concepts

• https://github.com/bio-ontology-research-group/mowl

• https://github.com/ema-marconato/reasoning-shortcuts

• https://github.com/SynaLinks/synalinks

• https://github.com/lab-v2/pyreason

• https://github.com/joaovictorcamargo/AI-Agents-as-Neuro-Symbolic-System

• https://github.com/UTAustin-SwarmLab/Neuro-Symbolic-Video-Search-Temporal-Logic

• https://github.com/LAMDASZ-ML/Awesome-LLM-Reasoning-with-NeSy

• https://gist.github.com/ruvnet/a872ec910082974116584f623a33b068

• https://github.com/opencog/learn

• https://github.com/fblgit/tree-of-knowledge

• https://github.com/mukhal/GRACE

• https://github.com/bys0318/QTO

• https://github.com/neuro-symbolic-ai/explanation_based_ethical_reasoning

• https://github.com/dubprime/mythral

• https://github.com/Brandonio-c/NeuroAI-Cognition-Hub

• https://github.com/topics/symbolic-ai

• https://arxiv.org/abs/2104.08274

• https://arxiv.org/pdf/2501.05435

• https://arxiv.org/pdf/2302.07200

• https://arxiv.org/pdf/2402.01889

• https://dspace.mit.edu/bitstream/handle/1721.1/139305/Alford-salford-meng-eecs-2021-thesis.pdf

• https://neurosymbolic-ai-journal.com/system/files/nai-paper-754_0.pdf

• https://neurosymbolic-ai-journal.com/system/files/nai-paper-740_0.pdf

• https://neurosymbolic-ai-journal.com/system/files/nai-paper-731.pdf

• https://neurosymbolic-ai-journal.com/system/files/nai-paper-675.pdf

• https://arxiv.org/pdf/2502.11269

• https://aclanthology.org/2022.lrec-1.408.pdf

• https://www.youtube.com/watch?v=q3s72VhaQR0

• https://www.youtube.com/watch?v=HhymId8dr5Q

• https://www.worldscientific.com/doi/10.1142/S1793351X22500015

• https://www.worldscientific.com/doi/10.1142/S1793351X20400012

• https://www.worldscientific.com/doi/10.1142/S0219720007003144

• https://www.worldscientific.com/doi/10.1142/S0219720003000137

• https://www.worldscientific.com/doi/10.1142/S0219649214500099

• https://www.worldscientific.com/doi/10.1142/S0219622022500213

• https://www.worldscientific.com/doi/10.1142/S0219427903000875

• https://www.worldscientific.com/doi/10.1142/S0219427902000492

• https://www.worldscientific.com/doi/10.1142/S0218488504002928

• https://www.worldscientific.com/doi/10.1142/S0218213019400025

• https://www.worldscientific.com/doi/10.1142/S0218213004001831

• https://www.worldscientific.com/doi/10.1142/S0218213004001764

• https://www.worldscientific.com/doi/10.1142/S2425038416300068

• https://arxiv.org/abs/2504.19354

• https://www.neurosymbolic.org/systems.html

• https://www.neurosymbolic.org/methods.html

• https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1336&context=csce_facpub

• https://neurosymbolic-ai-journal.com/system/files/nai-paper-737.pdf

• https://github.com/dodo47/SpikE

• https://journals.sagepub.com/doi/full/10.3233/SW-243711

• https://github.com/arcadio/data-logic

• https://github.com/primaryobjects/knowledgebase