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    "headline": "The Critic Found the Safe Region Before the Equation Tightened It",
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    "dek": "A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.",
    "summary": "A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.",
    "body_text": "High-dimensional reach-avoid analysis is difficult for grid solvers, while physics-informed networks may settle in poor residual minima and reinforcement learning may violate the governing equation. The proposed schedule starts with temporal-difference actor-critic learning, then adds PDE and boundary losses gradually. Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems.",
    "why_it_matters": "A scheduled method let reinforcement learning shape the value function before progressively enforcing the governing reach-avoid PDE.",
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      "Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems."
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    "first_published_at": "2026-08-19T09:00:00.000-04:00",
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        "qualification": "Two case studies reached accuracy comparable to successfully trained PINNs while mitigating their reported failure mode; the paper does not yet establish scaling across broad safety-critical systems."
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    "tags": [
      "reach-avoid",
      "reinforcement learning",
      "PDE",
      "control"
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      "source_id": "source-2026-08-19-013",
      "title": "arXiv preprint 2608.17117",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.17117",
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      "published_at": "2026-08-16T20:00:00.000-04:00",
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    "title": "The Critic Found the Safe Region Before the Equation Tightened It",
    "publisher": "The Machine Press",
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