arXiv Machine Learning
Tail-Influence Sampling for CVaR Policy Evaluation
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Speculative Evaluation of Stochastic LLMs
The paper introduces Speculative Evaluation, a method to reduce variance in evaluating stochastic large language models (LLMs) under a fixed rollout budget. It employs a Hierarchical Bayesian Neyman (HBN) policy that first runs a short uniform pilot, then pools task-level success counts via a hierarchical Bayesian model to compute posterior expectations of task-level sampling variances. Using these expectations, the method applies exact positive-integer Neyman allocation to allocate rollouts, and an asynchronous variant (HBN-async) speculatively executes continuations from partial pilot feedback to mitigate synchronization overhead. Across six checkpoints and 18 benchmark groups, Speculative Evaluation achieves 12.8%-33.6% lower variance compared to uniform allocation, outperforming empirical and independent Bayesian baselines, and demonstrates practical benefits in real-generation experiments.
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When Does q-error Predict Plan Regret? Three Regimes of Cardinality-Estimation Error
arXiv:2606. 15600v1 Announce Type: cross Abstract: Cardinality-estimation (CE) research ranks estimators by q-error, yet it is well known that q-error is an imperfect proxy for query-plan quality.
arXiv AI
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Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation
arXiv:2609. 29140v1 Announce Type: new Abstract: Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty.
arXiv Machine Learning
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BudgetBench: A Budget-Tiered Protocol and Pilot Harness for Memory Strategy Evaluation in Local Large Language Model Agents
arXiv:2609.13149v1 Announce Type: new Abstract: For local large language model agents, active context is a scarce resource: memory capacity, prefill latency, cache growth, and service objectives all...
arXiv Machine Learning
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Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR
arXiv:2607. 26253v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all responses correct or all incorrect) whose zero reward variance yields no policy-gradient signal.
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Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation
Repeated evaluation can estimate a benchmark score accurately while still requiring replication to certify narrow uncertainty. We characterize that requirement on a fixed grid of $M$ tasks with $L$ binary paths per task under the hard budget $(M+t)K$, where each path costs at most $K$ responses or episodes.