The Cognitive Lattice: A Neuro-Symbolic Hypergraph Architecture for Deterministic Reasoning and Autonomous Knowledge Topology
Fuente:
Zenodo
Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866902150921060352 |
|---|---|
| author | Dev, Gadgil |
| author_facet | Dev, Gadgil |
| contents | <p>Current Enterprise AI relies heavily on Vector Retrieval-Augmented Generation (Vector RAG), which excels at semantic similarity but fails at structural reasoning, temporal causality, and auditability. This paper introduces the Cognitive Lattice, a Neuro-Symbolic architecture deployed within an enterprise intelligence engine. We propose a Hypergraph Schema based on “Hex-Tuples” that unifies probabilistic weights, temporal validity, and epistemic context into a single edge construct. Inference is conducted via a Monte Carlo Semantic Walker (MCSW) that deterministically traverses this lattice, achieving ≈6.4×token compression via Holographic Context Injection. Furthermore, we introduce an Autonomous Optimization Loop utilizing Fine-Tuned Low-Rank Adapters (LoRA) to perform synthetic reinforcement learning on the graph topology, effectively allowing the system to “self-heal” and optimize its own logic without human intervention.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17876645 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Cognitive Lattice: A Neuro-Symbolic Hypergraph Architecture for Deterministic Reasoning and Autonomous Knowledge Topology Dev, Gadgil neuro-symbolic AI knowledge graph hypergraph hex-tuple graph reasoning LLM + symbolic reasoning temporal knowledge graph retrieval-augmented generation (RAG) context compression enterprise AI symbolic reasoning graph-based inference knowledge ingestion explainable AI agentic AI semantic walker self-healing graph federated reasoning multi-tenant AI AI architecture <p>Current Enterprise AI relies heavily on Vector Retrieval-Augmented Generation (Vector RAG), which excels at semantic similarity but fails at structural reasoning, temporal causality, and auditability. This paper introduces the Cognitive Lattice, a Neuro-Symbolic architecture deployed within an enterprise intelligence engine. We propose a Hypergraph Schema based on “Hex-Tuples” that unifies probabilistic weights, temporal validity, and epistemic context into a single edge construct. Inference is conducted via a Monte Carlo Semantic Walker (MCSW) that deterministically traverses this lattice, achieving ≈6.4×token compression via Holographic Context Injection. Furthermore, we introduce an Autonomous Optimization Loop utilizing Fine-Tuned Low-Rank Adapters (LoRA) to perform synthetic reinforcement learning on the graph topology, effectively allowing the system to “self-heal” and optimize its own logic without human intervention.</p> |
| title | The Cognitive Lattice: A Neuro-Symbolic Hypergraph Architecture for Deterministic Reasoning and Autonomous Knowledge Topology |
| topic | neuro-symbolic AI knowledge graph hypergraph hex-tuple graph reasoning LLM + symbolic reasoning temporal knowledge graph retrieval-augmented generation (RAG) context compression enterprise AI symbolic reasoning graph-based inference knowledge ingestion explainable AI agentic AI semantic walker self-healing graph federated reasoning multi-tenant AI AI architecture |
| url | https://doi.org/10.5281/zenodo.17876645 |