minhaj1403/qwen3.5-0.8b-blind-spots

Blind Spots of Frontier Models

This dataset contains a collection of 10 diverse test cases designed to identify the "blind spots" of the Qwen3.5-0.8B-Base model.

1. Model Information

• Model Tested: Qwen/Qwen3.5-0.8B-Base
• Parameters: ~0.8B
• Type: Base Causal Language Model (Native Multimodal)

2. How the Model was Loaded

The model was loaded and tested in a Google Colab environment using a T4 GPU. The following code snippet shows the loading configuration:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Qwen/Qwen3.5-0.8B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

3. Analysis of Blind Spots

Through zero-shot testing, the following blind spots were identified where the model's statistical training overrode logical grounding.

3.1 Logic & Riddles

In the plane crash riddle, the model hallucinated a 2018 crash site and suggested burying survivors in a Canadian cemetery instead of recognizing the trick in the word "survivors."

3.2 Tokenization

The model struggled with character-level manipulation. When asked to reverse the word "PANDA", it failed and entered a repetitive generation loop.

3.3 Temporal Logic

Although the model correctly calculated that 72 hours = 3 days, it failed the reasoning step Tuesday + 3, producing Thursday instead of the correct answer.

3.4 Inverse Logic

The model could not follow a hypothetical logic chain that contradicts real-world knowledge (e.g., "a mouse is bigger than an elephant"). Instead, it defaulted to a hallucinated multiple-choice style answer.

3.5 Symbolic Mapping

Given the mapping A = 1, B = 2, C = 3, the model failed to convert CAB → 312, instead outputting the sequential pattern 123.

3.6 Constraint Violation

When instructed to write a story in exactly three sentences, the model generated an infinite loop of repetitive sentences about a cat named Luna, ignoring the constraint.

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4. Proposed Fine-Tuning Strategy

4.1 Dataset Type

I think the model is kind of following the CoT Format but it does not comprehend instructions properly. So I would suggest training or finetuning with instruction tuning datasetsn for the specific tasks I mentioned above.

4.2 Assembly Method

The dataset can be constructed using Instruction Distillation.

A larger teacher model (for example Qwen2.5-72B-Instruct) can generate correct reasoning traces for these specific failure categories. These teacher-generated traces can then be used as synthetic supervision for the smaller model.

4.3 Estimated Dataset Size

For a 0.8B parameter model, a curated dataset of approximately 15,000–25,000 instruction–reasoning pairs should be sufficient to improve logical reasoning ability without degrading base model performance.

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5. Dataset Structure

Each entry in dataset.jsonl follows this schema:

• input — The prompt provided to the model
• expected_output — The logically correct response
• model_output — The actual response generated by Qwen3.5-0.8B-Base