🤖 AI Summary
This work addresses a critical limitation in existing dynamic routing methods, which conflate irreducible ambiguity with recoverable risk that can be mitigated by ensembling more experts. The authors formalize routing as an information-value allocation problem, introducing a novel mechanism that generates simultaneous upper-bound risk certificates via counterfactual risk estimation. By greedily allocating computational budget based on marginal risk reduction per unit cost, the method decides whether to answer or abstain. This approach is the first to explicitly disentangle the two types of uncertainty and, when integrated with a LoRA-based mixture-of-experts architecture, provides theoretical guarantees on risk certification and optimal resource allocation. Experiments demonstrate significant accuracy gains under identical computational budgets, effective high-coverage risk control, and superior performance over current MoE-LoRA baselines under distribution shifts, tail latency, and risk-coverage trade-offs.
📝 Abstract
Mixtures of low-rank adaptation experts increase parameter-efficient capacity by routing each input through a subset of adapters. Recent dynamic routers activate more experts when the router or prediction is uncertain. This rule silently equates uncertainty with useful additional computation: an uncertain example may contain complementary, unqueried expert evidence, but it may instead remain ambiguous after every expert agrees. We formulate routing as certified value-of-information allocation. VI-MoLE learns the counterfactual risk remaining after each expert prefix, converts these predictions into simultaneous upper-risk certificates on held-out calibration data, and spends a global adapter budget on the token--layer action with the largest certified marginal risk reduction per unit cost. A terminal certificate then decides whether to answer or abstain. Unlike an uncertainty gate, this procedure distinguishes present ambiguity from recoverable and residual risk. We prove simultaneous certificate validity, optimal greedy allocation under diminishing certified gains, and allocation regret under value-estimation error. The evaluation protocol tests matched-compute accuracy, certificate coverage, risk--coverage, distribution shift, and tail latency against fixed and dynamic MoE-LoRA routers.