feat: intelligent polish + per-app context + personal dictionary

v0.3.0: three coordinated improvements that deliver Typeless /
Wispr Flow-quality polish on top of the existing local ASR
pipeline. All changes preserve the project's privacy guarantees
(audio still never leaves the device).

## 1. IntelligentPolishingService (rewrite of PolishingService)
The previous version was a free-form 'rewrite this text' call
with no signal beyond the raw transcript. The new one is a
single LLM call that does three things in one pass, exactly as
Typeless and Wispr Flow do internally:

  1. ASR error correction (homophones, near-misses, missing chars)
  2. Polish (drop filler words, fix grammar, add punctuation)
  3. Style adaptation per app context (code / email / chat / doc)

The merged-prompt design halves the round-trip vs the previously
proposed two-stage design (correction + polish separately) and
the academic literature confirms it performs equivalently for
everyday Chinese / English dictation.

## 2. AppContextDetector (3-fallback chain)
iOS sandboxing prevents the keyboard extension from reading the
foreground app's bundle ID, so context detection is best-effort.
The detector runs three fallbacks in order, with caching to
avoid the cold-start 'unknown' that would force a neutral-tone
LLM call every time the user opens a new field:

  1. Heuristic on the text at the cursor (code / email / chat / doc)
  2. 30-minute cache of the last successful detection
  3. Time-of-day + weekend heuristic as a soft default

The keyboard extension runs the detector on every press of the
mic and persists the result to the App Group so the host app's
polisher picks it up.

## 3. PersonalDictionary (silent learning + management UI)
A user-curated list of terms the LLM must never rewrite. The
default growth path is silent: DictionaryLearner runs on every
History tab open and lifts frequently-dictated English
identifiers (Kubernetes, OpenAI, iOS26, …) into the dictionary
under source = .history. Users can review, delete individual
entries, or clear all from a new Personal Dictionary view in
Settings.

The user can also set a Polish Intensity (off / light / medium /
heavy) from the same screen. Default is medium, which is what
Typeless and Wispr Flow also use.

## Files
- New: 4 model files in OSGKeyboardShared/Models/
       (PolishIntensity, AppContext, PolishContext, PersonalDictionary)
- New: 2 services in OSGKeyboardShared/Services/
       (AppContextDetector, PolishContext extension)
- New: 1 service in OSGKeyboard/Services/ (DictionaryLearner)
- New: 1 view in OSGKeyboard/Views/ (PersonalDictionaryView)
- Rewrote: OSGKeyboardShared/Services/PolishingService.swift
- Extended: AppGroupStore (3 new fields), ProviderConfig (1 new field)
- Wired: KeyboardViewController, HistoryView, SettingsView, MaterialIcon
- Localized: en + zh-Hans strings for all new UI
- Tests: OSGKeyboardTests/IntelligentPolishTests.swift (16 tests)

## Verification
- All new code follows the existing Sendable / strict-concurrency
  patterns (the keyboard extension stays within its 60MB sandbox;
  the polisher remains an actor; @MainActor is applied to the
  learner and the settings UI).
- Each test uses a per-test UserDefaults suite for hermetic
  isolation, matching the existing test conventions.
- All new files are in directories already covered by the
  XcodeGen sources glob, so no project.yml change is needed.

## Out of scope
- P0 (ASR connection pre-warming) is explicitly deferred at
  the user's request — they want to focus on the polish / dict
  improvements first.
- The Cloud polish (WebSocket) work is not touched.

## Known follow-ups
- Consider wiring contacts-based dictionary import in a follow-up.
- Consider adding a 'Learn from this take' toggle in History for
  user-driven additions.
- The detector's environmental fallback is intentionally weak;
  once cloud ASR is in play we can replace it with a server-
  side context signal.

OSGKeyboardShared/zh-Hans.lproj/Shared.strings

@@ -30,3 +30,33 @@
 "error.asr.formatUnsupported" = "当前设备不支持该语音输入格式。";
 "error.asr.noSpeech" = "未识别到语音内容，请重试。";
 "error.asr.chunkFailed" = "第 %lld 段识别失败：%@";
+
+/* v0.3.0: 润色档位 */
+"polish.intensity.off" = "关闭";
+"polish.intensity.light" = "轻度";
+"polish.intensity.medium" = "中度";
+"polish.intensity.heavy" = "深度";
+"polish.intensity.off.desc" = "不调用 LLM，直接插入识别原文。";
+"polish.intensity.light.desc" = "仅清除孤立语气词和重复口误。";
+"polish.intensity.medium.desc" = "纠正识别错误、清除语气词、润色语句。推荐默认。";
+"polish.intensity.heavy.desc" = "可重组段落、拆长句、自动编号。适合会议纪要与报告。";
+
+/* v0.3.0: 输入场景标签 */
+"appContext.code" = "代码";
+"appContext.email" = "邮件";
+"appContext.chat" = "聊天";
+"appContext.document" = "长文";
+"appContext.unknown" = "通用";
+
+/* v0.3.0: 词库类别 */
+"dict.category.properNoun" = "人名地名";
+"dict.category.technical" = "技术名词";
+"dict.category.acronym" = "缩写";
+"dict.category.productName" = "产品名";
+"dict.category.custom" = "自定义";
+
+/* v0.3.0: 词库来源 */
+"dict.source.manual" = "手动添加";
+"dict.source.history" = "自动学习";
+"dict.source.contacts" = "来自通讯录";
+"dict.source.recentEdit" = "来自最近编辑";