Some questions about text generation

Dear BAGEL Team,

First of all, I would like to sincerely commend you for your impressive work on the BAGEL model. We greatly appreciate the clarity of your paper and the open access to your codebase—it has been immensely helpful for our own research on multimodal generation.

We've been experimenting with BAGEL in our own benchmarking pipeline, particularly exploring the impact of Chain-of-Thought (CoT) prompting on text generation quality. While we observed some promising improvements, we also noticed a decline in overall text quality after introducing CoT.
| Prompt | BAGEL | BAGEL + CoT |
|------------------------------------------------------------------------|--------------------------------------------------|--------------------------------------------------|
| An agent leaps between rooftops amid explosions. Bold letters announce: "Urban Shadow: Agent in Action". |  |  |
|The tabletop blackboard sits on a bar counter, surrounded by bottles and dim ambient lighting. It can be seen "Happy Hour 5-7 PM" using playful bubble letters, decorative lines, and doodles of cocktail glasses on the tabletop blackboard. |  |  |

The key hyperparameters/settings we used are the same as [inference.ipynb](https://github.com/ByteDance-Seed/Bagel/blob/main/inference.ipynb).

We would be grateful if you could offer any insight or suggest possible solutions.

Start with inference.ipynb and reproduce the reported BAGEL versus BAGEL + CoT examples using the stated settings. Compare the outputs and identify whether the quality decline is reproducible; the issue does not define a concrete code change or acceptance test.

Written by the indexing model from the issue text.