Progressive Cognitive Architecture - Logic Specialist (English)

LoRA adapter specialized for compact logical reasoning on top of Qwen2.5-1.5B.

Summary

This adapter was trained as the logic specialist component of the Progressive Cognitive Architecture project. It targets logic-focused tasks such as syllogistic validity, conditional reasoning, boolean evaluation, and short-form symbolic transformations.

Observed Behavior

On the focused Socratic benchmark used in this project, the logic specialist consistently improved logical reasoning over the 1.5B base model.

  • 2-seed logic composite mean: 70.3%
  • 2-seed overall mean on the mixed benchmark: 43.9%
  • Strongest dimensions: syllogism validity, conditional validity, boolean evaluation
  • Weakest dimensions: negation and open-form compound logic transformations

These numbers come from the project evaluation artifacts available in the Progressive Cognitive results dataset and should be interpreted as research results rather than a production benchmark.

Intended Use

  • logic classification and validation
  • short logical inference tasks
  • use as a specialist in routed or multi-agent architectures

Limitations

  • not intended as a general-purpose chat model
  • weaker on arithmetic and tool-use than the math-oriented adapters
  • open-ended logical rewrites remain less reliable than binary validity judgments

Loading

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")

model = PeftModel.from_pretrained(
    base_model,
    "dexmac/progressive-cognitive-logic-specialist-en",
    subfolder="lora_adapters"
)

Related Repositories

License

Apache 2.0

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