LuaN1aoAgent is a cognitive-driven AI hacker. It is a fully autonomous AI penetration testing agent powered by DeepSeek V3.2. Using dual-graph reasoning, LuaN1ao achieves a success rate of over 90% on the XBOW Benchmark, with a median exploit cost of just $0.09.

Tutorials on Causal Inference and pgmpy

Algorithms for quantifying associations, independence testing and causal inference from data.

CausIL is an approach to estimate the causal graph for a cloud microservice system, where the nodes are the service-specific metrics while edges indicate causal dependency among the metrics. The approach considers metric variations for all the instances deployed in the system to build the causal graph and can account for auto-scaling decisions.

A curated list of amazingly awesome things regarding Graph Structure Learning.

🔎 Benchmarking Framework for Extendability of Causal Graphs 🔍

🧠 R2T Prototype: An LLM pre-trained on causal graphs (not just text) to build provably faithful step-by-step reasoning.

🧠 Implement a bi-factual contrastive explanation system for AI decisions, enhancing understanding through formal definitions and optimized algorithms.

Logistic optimization: Delivery drivers location optimization with Causal Inference

A Minimal model for causal invariance: path merging via DP-like optimization

Implémentation d’un système d’IA Explicable (XAI) basé sur les explications contrastives bi-factuelles, avec optimisations algorithmiques et interface graphique CausaLytics.

R code for causal graph animations

Code for the python model `actualcauses` that implements algorithms for HP-causes identification.

One core, any surface - It just works, anytime, anywhere.

🔍 Enhance reasoning accuracy with the Reflective Reasoning Transformer, leveraging causal reasoning graphs for better dynamic reasoning performance.