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Model-to-Model Knowledge Transmission (M2KT): A Data-Free Framework for Cross-Model Understanding Transfer

Pratham Sorte

TL;DR

This work introduces Model-to-Model Knowledge Transmission (M2KT), a data-free framework for cross-model understanding transfer that replaces example-based supervision with knowledge packets containing concept embeddings, attention patterns, reasoning traces, and provenance metadata. A dedicated Concept Alignment Layer (CAL) in the student maps teacher concepts into the student latent space, while a composite loss enforces geometric, structural, and reasoning consistency alongside explicit safety constraints. The authors formalize concept manifolds, inter-model alignment, and an information-theoretic view, and provide algorithms for teacher-side packet generation and student-side ingestion. Preliminary experiments on symbolic reasoning show M2KT can reach about 87% of teacher performance with over 98% reduction in data usage, suggesting a viable path for data-free AI-to-AI knowledge transfer and modular, privacy-preserving model ecosystems. Overall, M2KT offers a principled, extensible approach to transferring abstract understanding across heterogeneous models without sharing training data or labels.

Abstract

Modern artificial intelligence systems depend heavily on large datasets for both training and transferring knowledge between models. Knowledge distillation, transfer learning, and dataset distillation have made such transfers more efficient, yet they remain fundamentally data-driven: a teacher must produce examples, logits, or gradients for a student to learn. In this work, we introduce Model-to-Model Knowledge Transmission (M2KT), a novel paradigm for data-free conceptual transfer between neural networks. M2KT enables models to exchange knowledge packets that encapsulate structured concept embeddings, abstraction graphs, reasoning traces, and provenance metadata. Unlike classical distillation, M2KT operates primarily in concept space rather than example space, and it does not require labeled datasets or teacher-generated outputs during transfer. We formalize the notion of concept manifolds, introduce an inter-model alignment mapping between teacher and student latent spaces, and derive a composite loss that enforces geometric, structural, and reasoning consistency together with explicit safety constraints. We further present algorithmic procedures for teacher-side packet generation and student-side ingestion and verification. Experiments on symbolic reasoning with large language models show that M2KT can achieve approximately 85 to 90 percent of teacher performance while reducing data usage by over 98 percent compared to standard knowledge distillation. This work establishes a theoretical and practical foundation for data-free AI-to-AI knowledge transfer and self-improving model ecosystems.

Model-to-Model Knowledge Transmission (M2KT): A Data-Free Framework for Cross-Model Understanding Transfer

TL;DR

This work introduces Model-to-Model Knowledge Transmission (M2KT), a data-free framework for cross-model understanding transfer that replaces example-based supervision with knowledge packets containing concept embeddings, attention patterns, reasoning traces, and provenance metadata. A dedicated Concept Alignment Layer (CAL) in the student maps teacher concepts into the student latent space, while a composite loss enforces geometric, structural, and reasoning consistency alongside explicit safety constraints. The authors formalize concept manifolds, inter-model alignment, and an information-theoretic view, and provide algorithms for teacher-side packet generation and student-side ingestion. Preliminary experiments on symbolic reasoning show M2KT can reach about 87% of teacher performance with over 98% reduction in data usage, suggesting a viable path for data-free AI-to-AI knowledge transfer and modular, privacy-preserving model ecosystems. Overall, M2KT offers a principled, extensible approach to transferring abstract understanding across heterogeneous models without sharing training data or labels.

Abstract

Modern artificial intelligence systems depend heavily on large datasets for both training and transferring knowledge between models. Knowledge distillation, transfer learning, and dataset distillation have made such transfers more efficient, yet they remain fundamentally data-driven: a teacher must produce examples, logits, or gradients for a student to learn. In this work, we introduce Model-to-Model Knowledge Transmission (M2KT), a novel paradigm for data-free conceptual transfer between neural networks. M2KT enables models to exchange knowledge packets that encapsulate structured concept embeddings, abstraction graphs, reasoning traces, and provenance metadata. Unlike classical distillation, M2KT operates primarily in concept space rather than example space, and it does not require labeled datasets or teacher-generated outputs during transfer. We formalize the notion of concept manifolds, introduce an inter-model alignment mapping between teacher and student latent spaces, and derive a composite loss that enforces geometric, structural, and reasoning consistency together with explicit safety constraints. We further present algorithmic procedures for teacher-side packet generation and student-side ingestion and verification. Experiments on symbolic reasoning with large language models show that M2KT can achieve approximately 85 to 90 percent of teacher performance while reducing data usage by over 98 percent compared to standard knowledge distillation. This work establishes a theoretical and practical foundation for data-free AI-to-AI knowledge transfer and self-improving model ecosystems.

Paper Structure

This paper contains 43 sections, 21 equations, 4 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Knowledge Distillation
  4. Dataset Distillation
  5. Transfer Learning and Parameter-Efficient Adaptation
  6. Transfer Learning and Parameter-Efficient Adaptation
  7. Federated Learning
  8. Meta-Learning and Machine Teaching
  9. Problem Setup and High-Level Overview
  10. Teacher and Student Models
  11. Concept Manifolds
  12. Goal of M2KT
  13. M2KT Framework and Architecture
  14. System Components
  15. Knowledge Packet Structure
  16. ...and 28 more sections

Figures (4)

  • Figure 1: High-level M2KT architecture. The teacher generates knowledge packets that are optionally validated by a broker and then ingested by the student via a Concept Alignment Layer, with a Verifier and Safety Auditor assessing the resulting behavior.
  • Figure 2: Schematic of a knowledge packet. Concept embeddings $C$, attention maps $A$, reasoning traces $R$, metadata $M$, and signature $\sigma$ collectively define the information transmitted from teacher to student.
  • Figure 3: TikZ-based view of the knowledge packet structure used in M2KT.
  • Figure 4: TikZ flowchart of the end-to-end M2KT pipeline, from teacher extraction to student verification and potential rollback.