fix(llm): stabilize JSON-object generation (force tool_choice + native response_format + robust planner parsing)

[ZhiXiao-Lin]

Problem

Users reported a3s-code's JSON-object generation is unstable. Root-cause audit found two gaps:

1. Structured output never used provider-native guarantees. The structured engine injected a synthetic emit_<schema> tool but only offered it (no tool_choice), and Strict/Json modes collapsed to Tool universally — so the model could reply with prose or malformed args. Pure parse-and-pray.
2. The planner / pre-analysis JSON path was fragile. llm_planner::extract_json was a naive first-{/last-} slice: no markdown-fence handling, no balanced extraction, no repair, hard-erroring on any fenced/prosey/brace-in-string output.

Fix (Tier 1 + Tier 2)

• LlmClient trait (non-breaking): additive native_structured_support(), complete_structured(), complete_streaming_structured() — all with default impls that reproduce current behavior, so every existing client/mock keeps working.
• structured.rs: capability-aware resolve_mode + StructuredDirective. Force tool_choice for Tool/Auto; request native response_format (Strict→json_schema+strict, Json→json_object) only on providers that support it, falling back to forced Tool mode otherwise (never silent degradation).
• Providers: Anthropic forces tool_choice; OpenAI sets tool_choice + response_format; Zhipu delegates to its inner OpenAI client. Request-building was extracted from request-execution so the structured path shares the exact HTTP/retry/parse code (complete_streaming left untouched).
• llm_planner.rs: the four parse paths now reuse the robust shared extractor, and pre_analyze gets one repair-retry (re-prompt for strict JSON) before falling back.
• generate_object.rs: stop pre-collapsing Strict/Json to Tool — the engine resolves per capability.

Tests

• Deep adversarial unit tests: capability/directive routing (5), provider wire-format tool_choice/response_format (Anthropic + OpenAI), adversarial extraction (CRLF/uppercase fences, prose+brace-in-string, single-quote rejection, tool-returns-text fallback), planner fence/prose/brace + repair-retry. 1811 lib tests green, cargo fmt + clippy clean.
• New #[ignore] real-LLM integration test (tests/test_structured_json_real_llm.rs) driven by .a3s/config.acl.

Real-LLM validation (against gpt-4o via the configured gateway)

Case	Result
forced tool_choice ×5 (stability)	✅ 5/5 valid objects, 0 repairs
native json_object	✅ valid object
pre_analyze (planner JSON)	✅ parsed, original request preserved
native json_schema (strict)	✅ handled cleanly (gateway rejected → graceful)

Run: A3S_CONFIG_FILE=/abs/.a3s/config.acl A3S_TEST_MODEL=openai/<tool-capable-model> cargo test -p a3s-code-core --test test_structured_json_real_llm -- --ignored --nocapture

Notes / follow-ups

• Strict (json_schema strict) is opt-in only; Auto/Tool never send response_format (avoids the OpenAI strict-subset 400 footgun).
• Follow-up: override complete_streaming_structured on the providers so the streaming structured path also forces the directive — today it uses the non-forcing default (the emit tool is still present, just not forced). Not a correctness bug; a reliability optimization for the streaming path.

[ZhiXiao-Lin]

Addressed the streaming follow-up in dfb7acc: providers now override complete_streaming_structured so the streaming structured path also forces tool_choice / native response_format (previously it used the non-forcing default). Validated against gpt-4o via the real gateway — streaming forced tool_choice yielded 8 partial-object callbacks and a valid final object; all 5 real-LLM integration cases pass. 1812 lib tests + fmt + clippy green.