Metric	Score
Composite Score	87.6
Exact Accuracy	69.4% ± 6.4
Adversarial Robustness	84.0% ± 8.0
Delegation Accuracy	100.0% ± 0.0
Delegation Rate	100.0% ± 0.0
Magnitude Sense (OoM±1)	100.0% ± 0.0
Catastrophic Errors	0.0% ± 0.0

Results: mean ± std over 3 seeds (42, 43, 44), 50 samples × 5 dimensions per seed.

🔑 Key Findings

Outperforms all 3B variants despite having half the parameters
Zero catastrophic errors — never produces absurd results
100% delegation — always routes complex operations to tools
Dream pruning acts as cognitive regularization for capacity-constrained models

🧠 Progressive Cognitive Architecture

A bio-inspired 4-phase training methodology:

Phase	Name	What happens
1	Foundation	Learn exact arithmetic via LoRA fine-tuning
2	Consolidation	SVD Dream Pruning (rank 16→8) compresses knowledge into intuition
3	Delegation	Learn complexity-aware routing: compute internally vs. delegate to tool
4	Orchestration	Full pipeline: intuit → route → tool → validate

Guiding Principle: Knowledge doesn't disappear — it collapses into attractors. Intuition is the compressed residue of experience.

🌙 Dream Pruning (SVD Low-Rank Factorization)

Instead of zeroing out small weights (magnitude pruning), Dream Pruning uses SVD decomposition to reduce the effective rank of LoRA matrices from 16 to 8. This preserves the principal directions ("logical connections") while discarding noise — analogous to memory consolidation during sleep.

W = U·Σ·V^T → W' = U[:,:k]·Σ[:k,:k]·V^T[:k,:] (k=8)

🔧 Training Configuration

Parameter	Value
Base Model	Qwen/Qwen2.5-1.5B
LoRA Rank	16 (→ 8 after SVD)
LoRA Alpha	32
LoRA Targets	q_proj, k_proj, v_proj, o_proj
Dropout	0.05
Training Data	~6,000 English arithmetic examples
Hardware	NVIDIA T4 16GB

🚀 Quick Start

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-1.5B", device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-1.5B")

# Note: adapters are in the lora_adapters/ subfolder
model = PeftModel.from_pretrained(
    base_model,
    "dexmac/progressive-cognitive-dream-lora-en",
    subfolder="lora_adapters"
)

messages = [{"role": "user", "content": "Solve: 342 * 67"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Expected output pattern:

Step 1 - Intuition: in the order of tens of thousands
Step 2 - Routing: DELEGATE (medium complexity)
Step 3 - Tool: 22914
Step 4 - Validation: result 22914 consistent with estimate → VALID

📊 Full Comparison

Model	Composite	Exact	Adversarial	Delegation	Magnitude	Safety
1.5B Dream (this)	87.6	69%	84%	100%	100%	100%
1.5B Flat	79.2	57%	81%	79%	100%	100%
3B Flat	78.5	60%	85%	79%	84%	100%
3B Dream	66.0	56%	34%	93%	100%	59%

🔗 Related Models

Flat LoRA (control) — Same data, no phases, no pruning
3B Dream — Same architecture on Qwen2.5-3B
3B Flat — 3B control
Italian Dream — Italian language variant
Results Dataset — Raw evaluation data
GitHub — Full source code and evaluation framework

📝 Citation

bibtex

@software{progressive_cognitive_2026,
  author = {Dex Mac},
  title = {Progressive Cognitive Architecture for LLMs},
  year = {2026},
  url = {https://github.com/dexmac221/progressive-cognitive},
  version = {1.0.0}
}

📄 License

Apache 2.0