[Ticket]: 5 — Design & Implement Mitigation Strategies (Days 9–10)

### Type
Feature

### Summary
We need to design and implement mitigation strategies **directly derived from the root causes** identified in Ticket 4, because the epic's goal is not to describe the problem but to reduce it. Expected result: implemented mitigation prototypes (decoding, prompt, evidence grounding, verification, abstention, combined), their experimental configurations, and initial mitigation results compared to baseline.

### Intent
We must answer "how can we reduce the hallucinations?" with working prototypes, each traceable to a Ticket 4 root cause, so Ticket 6 can compare them and pick the best trade-off.

### Source of truth (links)
- Epic #1062 — Deep Analysis & Mitigation of Qwen3.5-2B Hallucinations (this ticket is its Ticket 5).
- Requester-provided epic specification (2026-08-31), Ticket 5 — "Design & Implement Mitigation Strategies".
- Ticket 4 — Root Cause Matrix (what each mitigation must target).
- Ticket 3 — baseline metrics (comparison reference).
- `impl.md` — frozen deployment configuration (mitigations may alter sampling params / prompts, not serving infra).

### Current Behavior
- The model runs at baseline; no mitigation exists.

### Expected Behavior
- **Prototype 1 — Decoding optimization**: temperature, top-p, top-k, decoding strategy.
- **Prototype 2 — Prompt optimization**: uncertainty awareness, evidence-based answering, anti-fabrication instructions, abstention behavior.
- **Prototype 3 — Evidence grounding (RAG-style)**: Question → Evidence → Qwen3.5-2B → Grounded Answer.
- **Prototype 4 — Verification**: Answer → Claim extraction → Verification → Supported / Unsupported → Keep / Regenerate.
- **Prototype 5 — Abstention**: Enough evidence? → YES: answer; NO: abstain.
- **Prototype 6 — Combined**: the best combination of the above.
- Each prototype has an **experimental configuration** (parameters/prompt/flow) and **initial results** on a shared evaluation subset vs baseline.

### Acceptance Criteria
- [ ] Given the Root Cause Matrix (Ticket 4), when a mitigation is implemented, then it demonstrably targets at least one documented root cause.
- [ ] At least one prototype exists per strategy family (decoding, prompt, evidence, verification, abstention) plus a combined one.
- [ ] Each prototype's experimental configuration is recorded (reproducible).
- [ ] Initial results are reported for each prototype against the baseline (hallucination rate delta).
- [ ] Prototypes are compatible with the existing endpoint (no serving-infra change required, or a documented, approved exception).
- [ ] Error cases handled safely (abstain/regenerate loops bounded).
- [ ] Verification evidence is provided (prototype configs, initial results).

### Out of Scope
- Production rollout of any prototype (Ticket 7 recommendation; rollout is a follow-up).
- Model fine-tuning.
- Serving-infrastructure changes (vLLM source, engine profiles).

### Technical Context
- Endpoint: OpenAI-compatible; sampling knobs available (temperature, top_p, top_k, `enable_thinking`).
- Evidence grounding requires a retrieval step; for the benchmark, evidence can be supplied explicitly per case (ground-truth context) rather than building a retrieval system — keep the prototype minimal and testable.
- Verification can reuse the same model or a deterministic checker (string/assertion-based) for code claims; document the method.
- Abstention behavior must be measurable with the Ticket 3 "abstention accuracy" metric.

### Risks
- Mitigations reduce hallucinations at the cost of usefulness/quality. Mitigation: also track accuracy and abstention; evaluate trade-off in Ticket 6.
- Verification/regeneration loops add latency. Mitigation: bound iterations; measure latency.
- Prompt mitigations overfit the benchmark. Mitigation: keep prompts generic, validate on unseen data in Ticket 7.

### Test Plan
- Run each prototype on a shared evaluation subset (e.g. 50 cases) with the same seed as baseline.
- Report hallucination rate delta, factual accuracy, abstention rate, and latency per prototype.
- Re-run twice to confirm stability.

### Verification evidence
- Prototype code/configs checked into the repo (`tools/hallucination-benchmark/mitigations/` or equivalent).
- Initial results table (prototype → HR delta, accuracy, abstention, latency).
- Traceability: each prototype → root cause(s) from the Ticket 4 matrix.

### Human accountable owner
@stephane-segning

### AI Usage Declaration
Drafting the ticket, Proposing implementation

### Human verification completed
- [ ] I understood the intent
- [ ] I checked the source of truth
- [ ] I reviewed all AI-generated text/code
- [ ] I verified the implementation manually
- [ ] I verified the tests
- [ ] I checked for hallucinated assumptions
- [ ] I documented remaining risks
- [ ] I am the accountable owner and accept responsibility for this ticket.