Semantic reinforcement for better understanding

Semantic reinforcement for better understanding

We help humans understand large language model outputs faster, more clearly, and with reduced cognitive load.

A post-generation semantic restructuring layer — no model changes required.

the product

A semantic interpretation reinforcement layer, not an interface.

Navia-X is not:

  • a conversational AI

  • a browser extension focused on convenience

  • a standalone interface competing with existing models

What this is not

What it is

Navia-X delivers a semantic interpretation reinforcement layer that runs alongside large language model interfaces.

It enables users to clarify unclear concepts in-context, while structuring those moments of misunderstanding as machine-readable semantic signals.

Current status

Live MVP · Actively iterating · Model-agnostic

how it works

1. In-Context Interpretation

Users highlight or hover over unclear words, phrases, or concepts during interaction.
Interpretation occurs directly within the original context, without leaving the primary interface.

Text with highlighted words and questions about a project, a robot, a mouse, code, and a variable.

Each interaction is abstracted into structured semantic signals:

  • No raw content dependency

  • No personal data required

  • No persistent user profiling

2. Semantic Structuring

Diagram of semantic feedback signals with highlighted text snippets: "the robot?", "the mouse??", "the code?" and words "variable" and "variable" with question marks. The diagram shows steps of abstraction, identification of robots, detection of lab objects, and clean code variables, leading to semantic signals and non-coded signals.

Structured understanding gaps are aggregated at a system level, forming a semantic feedback layer that reflects where human interpretation consistently breaks down.

3. Signal Aggregation

Diagram illustrating the aggregation of semantic feedback signals into system-level gaps, which are analyzed for robot identification gaps, lab context gaps, and variable implementations.

Illustrative diagrams only. The semantic layer operates independently of any interface.

feedback to models

The problem

Large language models see prompts and outputs.
They rarely see misunderstanding.

Our approach

Navia-X structures interpretation failures as:

  • non-privacy

  • non-content

  • semantic feedback signals

These signals describe how and where users struggle to understand model outputs — not what they asked or received.

What we don’t do

  • No claims of model retraining

  • No access to model weights

  • No black-box optimization

Positioning

Navia-X operates adjacent to models, not inside them.
Supportive, not competitive.
Designed to improve alignment and interpretability through real usage signals.

Diagram illustrating a process with human interaction inputs, a semantic feedback layer, and integration into a large language model.

explorations

Current focus

Semantic interpretation reinforcement — actively developed and iterated.

Exploring

Questions around identity, continuity, and reasoning in AI systems, approached as long-term research directions, not near-term product commitments.

This page reflects conceptual continuity, not a delivery roadmap.