mlsanityData to 14 Oct 2026

UC-Search: Risk-Aware Test-Time Search for Delayed Constrained Time-Series Control

arXiv 2606.25274 ↗First posted 24 Jun 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Time-series deployments often need delayed feasible decisions, not only accurate forecasts. UC-Search is a trace-only retained-search layer for delayed constrained control: a frozen backbone emits forecasts or action scores, a hard-feasibility automaton rolls paths forward, and bounded search returns the first action of a feasible trajectory. The main claim is conditional: retained lookahead can improve delayed constrained decisions only when delayed feasible-set coupling, retained-prefix premises, and fail-closed release certificates hold. The promoted public endpoint is Phase128 certified M4 expanded40: validation selects Certificate-Constrained Retained Pareto Beam with λ=0.25, the held-out test has certificate/risk-active rates 1.0000/0.9642, and the weakest family remains above the unchanged 0.95 gate at 0.9516 on M4Weekly. The author-defined public 9-family suite remains an uncertified stress-test boundary. The paper reports a trace-only mechanism, one certified public endpoint, failed-route certificates, and deployment boundaries rather than a universal risk-control theorem.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 37 references that are in the map. First posted on arXiv 24 Jun 2026.

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15 of 37 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  4. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  5. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  6. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  7. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  8. 2019When to Trust Your Model: Model-Based Policy Optimizationcited by 773
  9. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  10. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  11. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  12. 2017Smart "Predict, then Optimize"cited by 339
3 more
  1. 2022Adaptive Conformal Predictions for Time Seriescited by 161
  2. 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
  3. 2023Monte Carlo Tree Search in the Presence of Transition Uncertaintycited by 4

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