Hi everyone,
I'd like to share a theoretical concept and research idea regarding multi-agent architectures, tentatively named "Knowledge Cross-Pollination". The core objective is to explore how output discrepancies between language models could be leveraged while addressing the primary risk of inter-LLM exchanges: the cascading propagation of errors.
### The Core Concept
Instead of using multi-model interactions for voting (Ensembling) or seeking consensus (Debate), could continuous interaction between different LLM architectures be driven by output discrepancies?
The general principle would follow an iterative loop:
Subject → Output Comparison → Gap Detection → Deterministic Verification → Targeted Queries → New Connections → Further Exploration.
Each model would retain its own weights and parameters, interacting strictly through a structured exchange protocol.
### Fundamental Premise: Textual Outputs vs. Knowledge
A crucial distinction to consider is that language models lack reliable internal introspection. An LLM generates plausible text with a similar apparent confidence for both verified facts and hallucinations.
Consequently, such a process does not detect differences in internal knowledge, but rather differences in textual outputs. This shifts the core challenge: the priority is not merely moving information around, but establishing a strict verification mechanism to ensure output gaps are not mistakenly interpreted as valid knowledge to be transmitted.
Reliability and verification must therefore be framed as a core condition of the exchange protocol, rather than a secondary refinement.
### Conceptual Workflow
1. **Output Generation:** Model A and Model B generate outputs on a given topic.
2. **Discrepancy Identification:** The system flags gaps, omissions, or contradictions between the outputs.
3. **Pre-Transmission Verification Layer:** Before any transfer occurs, the gap is evaluated through a validation gate (e.g., grounding against external sources/RAG, execution verification, or cross-checking via a model from a distinct architectural family).
4. **Contextual Transmission:** If validated, the gap is passed forward as structured metadata (including provenance, source proof, and a confidence score)—never as a raw assertion.
5. **Cross-Pollination:** The combination of validated elements (X + Y) triggers a targeted query to explore the newly highlighted blind spot.
### Positioning & Key Questions
Unlike classic consensus or debate approaches—which often risk "hallucination snowballing" when models exchange unverified statements—this approach treats verified output gaps as a signal to reveal blind spots and guide exploration.
Some open questions worth exploring around this idea:
1. What are the most practical and lightweight approaches to validate output gaps before transmission (e.g., hybrid RAG, code execution, multi-family checks)?
2. How could a "Net Knowledge Gain" metric be defined to measure whether valid information acquired outweighs residual noise introduced over time?
3. What data structures (e.g., JSON schemas, localized knowledge graphs) would best prevent context drift across continuous exchange loops?
4. Long-term, could such exchange protocols operate at the latent space representation level while maintaining auditability and verification?
I would be interested in hearing the perspective of researchers and developers on the theoretical feasibility of such a verification layer, which seems to be the primary technical bottleneck for this concept.