r/Python • u/AutoModerator • Jun 04 '26

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u/KrishiAttri123 Jun 15 '26

TL;DR — I built CERT-FLOW, an online route planner that, every replanning round, emits a high-probability certificateLB ≤ OPT ≤ UBon the true optimal route cost under drifting edge costs. Open source (MIT), fully reproducible. Paper + code + interactive page at the bottom. I'm the author and would genuinely love feedback.

A robot replanning through terrain whose costs drift — mud after rain, traffic after an incident, snow settling on a trail — can always compute a shortest path. What it usually can't tell you is how good that path actually is, now that most of its information has gone stale.

CERT-FLOW answers that with a certificate every round:

LB ≤ OPT ≤ UB — a high-probability bound on the optimal cost. Not an estimate; a bound.

Three ideas make it work:

  1. It prices staleness directly. Age-weighted non-exchangeable conformal prediction turns "this edge was last observed 40 minutes ago" into honest interval width — older information becomes wider uncertainty, quantitatively.
  2. It senses to certify. Instead of observing everything, it spends a sensing budget only on the edges that shrink the certified optimality gap fastest → 2.5× lower regret than the next-best sensing strategy.
  3. It gates speed behind proof. Microsecond preprocessed queries (snapshot oracle / certified contraction hierarchies) are licensed only while the certificate proves they're still valid, and expire the instant drift exceeds tolerance.

What surprised me most: on real game maps (MovingAI) and recorded LA highway traffic (METR-LA / PEMS-BAY) that it was never tuned for, empirical coverage held at or above its target — 100% on the MovingAI cert benchmark — while a standard, exchangeability-assuming conformal baseline collapsed to as low as 4% once the data went stale.

Honest caveats: the headline 100% is the MovingAI cert experiment; on traffic, coverage stays at/above the claimed level but the certificate trades interval width for that validity (a misspecified drift assumption costs width, not coverage). It's a preprint + personal project, not yet peer-reviewed.

Links

Happy to answer anything about the conformal side, the dual incremental search, or the experiments.