qurashiubaid/konverge1.1

Consistency-Invariant Transformer (CIT)

A novel language model that learns through minimizing violation of theoretical invariants without any labeled data or human supervision.

Model Description

The Consistency-Invariant Transformer (CIT) implements a groundbreaking approach to self-supervised learning. Instead of learning from labeled data or reinforcement learning from human feedback, CIT learns by minimizing its violation of known theoretical invariants across multiple domains:

• Temporal Invariants: Logical consistency in time relations (before/after/while)
• Causal Invariants: Asymmetry and transitivity in cause-effect relations
• Mathematical Invariants: Algebraic identities and equation consistency
• Logical Invariants: Propositional logic validity
• Lexical Invariants: Semantic consistency under paraphrasing
• Factual Invariants: Entity property consistency

How It Works

The model optimizes the following objective:

Where:

• T_i are invariant operators that should equal 0 for perfect models
• f_θ is the model's output
• w_i are weights balancing different invariants

As training progresses, T_i[f_θ] → 0, meaning the model approaches optimal behavior without ever seeing labeled examples.

Intended Use

This model is intended for:

• Research on self-improving AI systems
• Studying how theoretical knowledge can guide learning
• Applications where labeled data is scarce but theoretical principles are known
• Education and demonstration of invariant-based learning

Training Data

The model is trained on synthetically generated data that includes patterns for:

• Temporal reasoning (event sequencing)
• Causal reasoning (cause-effect chains)
• Mathematical equations
• Logical statements
• Mixed reasoning patterns

Usage

from consistency_invariant import ConsistencyInvariantTransformer, ConsistencyInvariantConfig

# Load configuration
config = ConsistencyInvariantConfig.from_pretrained("username/consistency-invariant-transformer")

# Load model
model = ConsistencyInvariantTransformer(config)
model.load_state_dict(torch.load("pytorch_model.bin"))

# Generate text
prompt = torch.tensor([[config.bos_token_id, 3000, 3001, 3002]])
generated = model.generate(prompt, max_length=50, temperature=0.8)