Скачать или смотреть Large Causal Models from Large Language Models (Dec 2025)

Title: Large Causal Models from Large Language Models (Dec 2025)
Link: http://arxiv.org/abs/2512.07796v1
Date: December 2025

Summary:
This paper introduces DEMOCRITUS, a new paradigm for building Large Causal Models (LCMs) by extracting and organizing the vast causal knowledge latent in Large Language Models (LLMs). Unlike traditional causal discovery that relies on numerical data from narrow domains, DEMOCRITUS aggregates millions of specific causal claims from diverse fields into a coherent geometric structure using a pipeline that includes topic expansion, causal triple extraction, and a Geometric Transformer. The authors demonstrate the system's ability to create navigable causal manifolds in domains ranging from economics and biology to the archaeology of the Indus Valley Civilization collapse, organizing fragmented text into structured Topos Causal Models.

Key Topics:
Large Causal Models (LCMs)
Large Language Models (LLMs)
Causal Discovery
Geometric Transformer
DEMOCRITUS System
Topos Causal Models
Manifold Learning

Chapters:
00:00 - Introducing System Democritus
01:28 - Shifting to Causal Breadth
03:12 - Topos Causal Models
04:30 - Pipeline: Text to Graph
05:51 - The Geometric Transformer
07:41 - Indus Valley Case Study
08:52 - Solving the "Hairball" Problem
10:06 - Computational Costs and Scaling
11:23 - Critical Limitations
12:42 - Future: Dynamical Causal Models

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