Concept Memory typed judgment base model

This repository contains the task-fine-tuned Concept Memory checkpoint for criteria-conditioned Choice, ordinal Score, and Yes/No (Noul) judgments. It is not a language model pretrained from scratch. Its text encoder is sentence-transformers/all-mpnet-base-v2.

What is the problem?

Typed decision models can return structured outputs such as a Choice label and its probabilities. In a domain workflow, teams may also need to understand which reusable ideas are relevant, apply domain knowledge consistently, and adapt those mappings as policies change. A prediction by itself does not provide those capabilities, and a probability is not an explanation.

Concept Memory is designed to explore this gap. It supports three judgment types:

Choice: select one criterion, such as a support team.
Score: estimate an ordinal position among supplied criteria.
Noul (Yes/No): return a binary judgment.

What is the solution?

Theory

Concept Memory pairs a learned language representation with an optional, editable Concept Bank. A concept describes a reusable pattern, such as “integration failure,” and can link that pattern to outcomes such as “Technical.” This separates some domain knowledge from the model's learned weights, making the definitions and mappings easier to inspect, reuse, and adapt across tasks.

This is a different design emphasis from a decision-only interface: the model still returns a typed prediction, while the bank offers an additional path for relating relevant concepts to outcomes. This is a potential architectural benefit, not evidence of higher accuracy than Jev, Laya, or another model.

Technical design

The base model first predicts from the input state, question instructions, and criteria. When a Concept Bank is supplied, a second Choice question ranks the bank's concepts by relevance. The bank maps those concept signals to outcome votes. The current helper adjusts probabilities only when the bank's top outcome agrees with the base model's top outcome; when it does, it blends 20% raw model probabilities with 80% normalized bank votes. The bank is therefore constrained decision support, not an independent rule engine, and it cannot overturn the raw model's winner.

Concept rankings and declared mappings make part of the decision process inspectable. They are not causal explanations: relevance is a relative ranking, not calibrated evidence, and it does not prove what caused the raw prediction. The current implementation also does not independently detect each concept. Concept hierarchies, dependencies, interventions, and provenance tracking are future research directions, not capabilities of this checkpoint.

This architecture allows domain mappings to be changed without retraining the encoder, but new concepts must still be evaluated on representative examples. Accuracy, calibration, robustness, and latency should be compared on the same data and conditions before making performance claims.

How can you use it?

Run the standalone Choice example

The local artifacts/base_model/example.py script:

Downloads the Syigen/rgcm repository snapshot from Hugging Face.
Loads model.pt and constructs the model using its saved encoder settings.
Creates a Choice question with Billing, Technical, and Sales criteria.
Predicts a category for a sample Stripe integration problem, then prints the chosen criterion and probabilities.

From the project root, install the dependencies and run it:

pip install torch transformers huggingface_hub
python artifacts/base_model/example.py

The example demonstrates Choice. Score and Noul questions use the same typed question interface with the appropriate type and criteria. The checkpoint is a PyTorch artifact for this project's model class; it is not directly loadable with transformers.AutoModel.from_pretrained.

Try the linked examples

Hugging Face example.py is the hosted Python example file.
Open the Colab notebook to try the linked notebook.
Open the Concept Memory playground for interactive inference.

Add concepts

Concept Bank entries define a readable concept and the outcomes it supports or contradicts. For example:

{
  "id": "integration_failure",
  "name": "Integration failure",
  "description": "An API, SDK, or third-party integration is failing.",
  "supports": ["technical"],
  "contradicts": ["sales"]
}

Add concepts to the playground's Concept Bank field to see which ones rank as relevant and how their links contribute to the decision.

Files

model.pt: PyTorch state dictionary and training metadata.
config.json: encoder, task, and training run metadata.
example.py: standalone Python Choice example.

Training metadata

{
  "datasets": [
    "mteb/banking77",
    "nyu-mll/glue:sst2",
    "google/boolq"
  ],
  "task_types": [
    "choice",
    "score",
    "noul"
  ],
  "encoder": "sentence-transformers/all-mpnet-base-v2",
  "epochs": 50,
  "batch_size": 32,
  "seed": 42,
  "model_version": "base-20261003-171325Z",
  "generated_at": "2026-10-03T17:13:25Z"
}

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