Research, insights, and ideas worth exploring. Explore research, benchmarks, and practical guides on adaptive optimization, experimentation, and the technologies shaping better decision-making.
The regret gap: benchmarking qbrix against A/B testing
A 60-run simulation pitting qbrix's auto meta-bandit against a traditional fixed-split A/B test. Across 13,944 users per run, qbrix delivered 5.2% more conversions and 34% less regret — with non-overlapping confidence intervals. Here is what those numbers look like once you translate them into dollars.
When to use a multi-armed bandit instead of an A/B test
A practical decision guide, not a verdict. Four questions settle almost every case — does the decision repeat, how fast does the reward land, do you have the traffic, and do you need a defensible causal number — plus the five scenarios teams actually run into and the one where neither method is the right answer.
The Hidden Costs of A/B Testing at Scale
Why traditional A/B testing breaks down under real-world conditions — cross-test contamination, temporal drift, and the statistical burden of parallel experiments — and how adaptive algorithms offer a fundamentally better model.
A Developer's Guide to Multi-Armed Bandits
What multi-armed bandits are, how the core algorithms work — from Epsilon-Greedy to Thompson Sampling to contextual and adversarial policies — with Python implementations and practical guidance on when to use each.
Distributed Bandits in Production: Theory, Architecture, and Trade-offs
What happens when you take a multi-armed bandit — designed for a single agent pulling one lever at a time — and run it across a fleet of servers? A deep dive into staleness, delayed rewards, and the engineering that makes distributed bandits work.