{
  "id": 4590,
  "url": "https://arxiv.org/abs/2605.10051v1",
  "title": "Guided Streaming Stochastic Interpolant Policy",
  "summary": "Inference-time guidance is essential for steering generative robot policies toward dynamic objectives without retraining, yet existing methods are largely confined to chunk-based architectures that exhibit high latency and lack the reactivity needed for test-time preference alignment or obstacle avoidance. In this work, we formally derive the optimal guidance term for Stochastic Interpolants (SI) by analyzing the value function's time evolution via the Backward Kolmogorov Equation, establishing ",
  "authors": "Puming Jiang, Meiyi Wang, Kelvin Lin, Ce Hao, Harold Soh",
  "category": "research",
  "topics": "regulation,safety-alignment,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T06:24:21.000Z",
  "fetched_at": "2026-07-14T16:31:08.354Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4590",
  "original_url": "https://arxiv.org/abs/2605.10051v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}