Why need prefill for new generated text token?

Hi, thanks for your great work!

I have a question related to the code below:

```
if think:
gen_text = self.gen_text(gen_context, do_sample=do_sample, temperature=text_temperature, max_length=max_think_token_n)
gen_context = self.update_context_text(gen_text, gen_context)
output_list.append(gen_text)
```

_**For gen_text(decode), it will generate kv_cache, but you still run the update_context_text(prefill) to get kv_cache. If i want to accelerate it, can this step(update_context_text) be skipped? Why you design like this?**_

I also print the kv_cache tensor in gen_text and update_context_text. The results shows they have 0.0000035 mean_abs which is quite small, more likely caused by precision difference instead of accuracy problem.

The print log is below:

```
=== Summary ===

[past_key_cache_after_decode.pt]
tensor: shape=(3012, 4, 128) dtype=torch.bfloat16 device=cpu **mean=-0.0625086 std=5.32648 min=-208 max=55.5**

[past_key_values_after_prefill.pt]
tensor: shape=(3013, 4, 128) dtype=torch.bfloat16 device=cpu **mean=-0.0625121 std=5.32567 min=-208 max=55.5**
tensor (trimmed): shape=(3012, 4, 128) mean=-0.0625059 std=5.32648 min=-208 max=55.5
tensor (last_row): mean=-0.0812546 std=1.58288 min=-7.125 max=7.03125

=== Pairwise comparisons (same key across files) ===

[past_key_cache_after_decode.pt] vs [past_key_values_after_prefill.pt]
tensor: avg_abs_diff=0.00182607 max_abs_diff=0.785156
```

Start by locating the call sites for gen_text and update_context_text in the Python code shown, then trace how each produces and consumes kv_cache. Compare the cache shapes and values before and after the update step, and document or validate whether skipping it preserves the expected generation behavior.

Written by the indexing model from the issue text.