Add an append-only streaming path that feeds each audio chunk as a new
conversation turn, so nothing is ever recomputed: the KV grows by
append, no prefix is re-decoded and no audio is re-encoded. Per-step
cost becomes flat instead of growing with the segment (RTF 1.06 -> 0.26
on 62.6s of continuous speech), but accuracy requires ~8s chunks because
the audio encoder needs its 8s attention window, so text only appears
every 8s. The incremental mode stays the default.
新增追加式流式：每块音频作为一个新的对话轮追加，前缀不重算、旧音频不重
编码，单步成本不再随段落增长（连续 62.6s 语音 RTF 从 1.06 降到 0.26）。
但准确率要求 chunk≈8s，否则会被截断，所以出字粒度是 8s；增量式仍为默认。
Add native streaming ASR: 8s-aligned audio slices are cached in the KV
cache, only new audio is encoded, and the previous text is fed back as
an assistant prefix with token rollback. Text matches offline results.
Streaming keeps each step within the 2s budget for segments up to 20s.
新增原生流式解码：8s 对齐的音频切片存入 KV 复用，只编码新音频，并把
上一轮文本作为 assistant 前缀回喂（带 token 回滚）。转写结果与离线
一致；20s 以内的段落每步计算都在 2s 预算内。
Add a C++/Qt6/CMake CLI prototype that captures the default sink
monitor through libpulse and transcribes it in-process with Qwen3-ASR
via libllama/libmtmd. Add phase 0/1 verification docs.
新增实时字幕命令行原型：libpulse 采集默认输出 monitor，进程内用
libllama/libmtmd 跑 Qwen3-ASR 推理。附阶段 0/1 验证文档。
blumia