eval_compare.md

DriftShield — eval snapshot (eval_compare.md)

This file is generated by scripts/generate_proof_artifacts.py for hackathon / README evidence. Baseline = two-step hand rollouts per task (opens case then submits a weak resolution). Trained column uses the deterministic scripted strong policy in inference.fallback_action (same trajectories as tests/test_driftshield.py), standing in for a GRPO-tuned LoRA until you attach a real adapter under outputs/.

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Baseline vs Trained — DriftShield

• Base model: Qwen/Qwen3-1.7B
• Adapter: inference.fallback_action (scripted strong baseline, not LoRA)
• Curriculum: driftshield
• Episodes: 1 per run per task

Component means (higher is better except penalty)

Metric	Baseline	Trained	Δ
Total (progress_score)	+0.347	+0.962	▲ +0.615
Investigation	+0.050	+1.000	▲ +0.950
Routing	+0.750	+1.000	▲ +0.250
Reply quality	+0.000	+1.000	▲ +1.000
Groundedness	+0.750	+1.000	▲ +0.250
Submission	+0.750	+1.000	▲ +0.250
Pass rate (total≥0.5)	+0.000	+1.000	▲ +1.000
Done rate	+1.000	+1.000	· +0.000
Penalty total (↓)	+0.280	+0.037	▼ -0.243
Turns (mean)	+2.500	+4.250	▲ +1.750