Data

Tools

Indexes

Applications

Experiments

Open Source Science

ivb-replication

Replication code for the IVB model with Hierarchical Perceptive Memory and Adaptive Conditional Path

https://github.com/mmbakun/ivb-replication

Science Score: 57.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 2 DOI reference(s) in README
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Replication code for the IVB model with Hierarchical Perceptive Memory and Adaptive Conditional Path

Basic Info
  • Host: GitHub
  • Owner: mmbakun
  • License: other
  • Language: Python
  • Default Branch: main
  • Size: 12.7 KB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 8 months ago · Last pushed 8 months ago
Metadata Files
Readme License Citation

README.md

IVB: Multimodal Adaptive Model with Hierarchical Perceptive Memory and Adaptive Conditional Path

This repository contains replication code for the IVB model, including simplified implementations of the Hierarchical Perceptive Memory (HPA) and Adaptive Conditional Path (ACP) mechanisms.

🧠 Overview

IVB is an adaptive, multimodal AI architecture designed for efficient inference and energy optimization. The system features: - HPA – Context-aware hierarchical memory with short-, mid-, and long-term layers. - ACP – An adaptive computation strategy that selects execution pathways based on task complexity. - Multimodal Processing – Handles text, image, and audio inputs.

📁 Repository Contents

ivb-replication/ ├── hpa/ │ └── hpa_core.py # Minimal implementation of the HPA memory structure ├── acp/ │ └── acp_predictor.py # Implementation of the Adaptive Conditional Path algorithm ├── benchmarks/ │ ├── mm_qa40.py # Benchmark for multimodal question answering │ └── energy_edge.py # Benchmark for energy-related evaluation ├── models/ # Directory for model weights (optional) ├── run_experiments.sh # Script to run benchmark experiments ├── README.md # This file ├── CITATION.cff # Citation metadata file

🚀 Quick Start

bash git clone https://github.com/your-org/ivb-replication.git cd ivb-replication bash run_experiments.sh

📚 Citation

If you use this code in your research, please cite:

Mieczysław Bakun (2025). IVB: Multimodal Adaptive Model with Hierarchical Perceptive Memory and Adaptive Conditional Path. Zenodo. https://doi.org/10.5281/zenodo.15707361

The CITATION.cff file is included for citation managers and GitHub integration.

🔒 License

This replication code is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

✉ Contact

For commercial licensing or further collaboration:

📧 bakun@ivb-research.edu.pl
🌐 https://ivb-research.edu.pl

Owner

  • Name: ivb
  • Login: mmbakun
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "Jeśli używasz tego oprogramowania, proszę zacytuj poniższy wpis."
title: "IVB: Multimodal Adaptive Model with Hierarchical Perceptive Memory and Adaptive Conditional Path"
authors:
  - family-names: Bakun
    given-names: Mieczysław
    orcid: "https://orcid.org/0009-0007-5679-446X"
    affiliation: "IVB Research Institute"
date-released: 2025-06-20
doi: 10.5281/zenodo.15707361
license: CC-BY-4.0
repository-code: "https://doi.org/10.5281/zenodo.15707361"
version: "1.0.0"
keywords:
  - AI
  - adaptive inference
  - HPA
  - ACP
  - multimodal models
  - energy optimization

GitHub Events

Total
  • Push event: 4
Last Year
  • Push event: 4

Dependencies

requirements.txt pypi
  • numpy >=1.24
  • torch >=2.0.0
  • tqdm *
  • transformers >=4.38.0