Strip model-aware branches from LLM configuration

Drop every hand-rolled provider table and per-model gating that had
accumulated in the model-handling layer:

  * normalize_model_name no longer auto-prefixes bare claude-* / gemini-*
    names. Users supply the full <provider>/<model> form. The function
    became literally model_name.strip(), so callers now inline that and
    the function is removed.
  * tool_choice="required" is gone everywhere. Thinking-mode endpoints
    (Anthropic, DeepSeek /beta) reject it; modern reasoning models don't
    need it; non-interactive runs already have
    _append_noninteractive_tool_required_message as the convergence
    backstop. model_supports_reasoning, model_known_to_registry, and
    _model_cost_entry were only used to gate this and follow it out.
  * Reasoning(effort=...) is now attached whenever
    STRIX_REASONING_EFFORT is non-none. litellm.drop_params=True absorbs
    it for non-reasoning models.
  * Warm-up's bare-name OpenAI 401 hint is removed (false-positive prone,
    relied on substring matching).
  * reset_tool_choice on SandboxAgent is no-op now (no tool_choice gets
    set) and is removed.
  * report/dedupe.py was still routing through stock MultiProvider, so
    non-OpenAI configs failed the dedupe LLM pass; switch it to
    StrixProvider.

Verified end-to-end against modern provider strings (openai/gpt-5.4,
anthropic/claude-opus-4-7, deepseek/deepseek-reasoner,
gemini/gemini-2.5-pro, groq/, xai/, mistral/, together_ai/, perplexity/,
openrouter/, litellm/ legacy form, and whitespace-padded input): 18/18
cases route correctly, env vars mirror via litellm.validate_environment,
and ModelSettings carries no tool_choice. mypy strict passes.

strix/core/runner.py

@@ -17,7 +17,6 @@ from strix.config import load_settings
 from strix.config.models import (
     StrixProvider,
     configure_sdk_model_defaults,
-    normalize_model_name,
     uses_chat_completions_tool_schema,
 )
 from strix.core.agents import AgentCoordinator
@@ -91,7 +90,7 @@ async def run_strix_scan(
 
     settings = load_settings()
     configure_sdk_model_defaults(settings)
-    resolved_model = normalize_model_name(model or settings.llm.model or "")
+    resolved_model = (model or settings.llm.model or "").strip()
     if not resolved_model:
         raise RuntimeError(
             "No LLM model configured. Set STRIX_LLM env or pass model= to run_strix_scan().",
@@ -154,10 +153,7 @@ async def run_strix_scan(
         is_whitebox = any(t.get("type") == "local_code" for t in targets)
         skills = list(scan_config.get("skills") or [])
         root_task = build_root_task(scan_config)
-        model_settings = make_model_settings(
-            settings.llm.reasoning_effort,
-            model_name=resolved_model,
-        )
+        model_settings = make_model_settings(settings.llm.reasoning_effort)
         run_config = RunConfig(
             model=resolved_model,
             model_provider=StrixProvider(),