David J. Lee | Searcharxiv

arXiv2026

LHAW: Controllable Underspecification for Long-Horizon Tasks

Long-horizon workflow agents that operate effectively over extended periods are essential for truly autonomous systems. Their reliable execution critically depends on the ability to reason through ambiguous situations in which clarification seeking is necessary to ensure correct task execution. However, progress is limited by the lack of scalable, task-agnostic frameworks for systematically curating and measuring the impact of ambiguity across custom workflows. We address this gap by introducing LHAW (Long-Horizon Augmented Workflows), a modular, dataset-agnostic synthetic pipeline that transforms any well-specified task into controllable underspecified variants by systematically removing information across four dimensions - Goals, Constraints, Inputs, and Context - at configurable severity levels. Unlike approaches that rely on LLM predictions of ambiguity, LHAW validates variants through empirical agent trials, classifying them as outcome-critical, divergent, or benign based on observed terminal state divergence. We release 285 task variants from TheAgentCompany, SWE-Bench Pro and MCP-Atlas according to our taxonomy alongside formal analysis measuring how current agents detect, reason about, and resolve underspecification across ambiguous settings. LHAW provides the first systematic framework for cost-sensitive evaluation of agent clarification behavior in long-horizon settings, enabling development of reliable autonomous systems.

cs.CL↗

arXiv2021

Telescoping Filter: A Practical Adaptive Filter

Filters are fast, small and approximate set membership data structures. They are often used to filter out expensive accesses to a remote set S for negative queries (that is, a query x not in S). Filters have one-sided errors: on a negative query, a filter may say "present" with a tunable false-positve probability of epsilon. Correctness is traded for space: filters only use log (1/ε) + O(1) bits per element. The false-positive guarantees of most filters, however, hold only for a single query. In particular, if x is a false positive of a filter, a subsequent query to x is a false positive with probability 1, not epsilon. With this in mind, recent work has introduced the notion of an adaptive filter. A filter is adaptive if each query has false positive epsilon, regardless of what queries were made in the past. This requires "fixing" false positives as they occur. Adaptive filters not only provide strong false positive guarantees in adversarial environments but also improve performance on query practical workloads by eliminating repeated false positives. Existing work on adaptive filters falls into two categories. First, there are practical filters based on cuckoo filters that attempt to fix false positives heuristically, without meeting the adaptivity guarantee. Meanwhile, the broom filter is a very complex adaptive filter that meets the optimal theoretical bounds. In this paper, we bridge this gap by designing a practical, provably adaptive filter: the telescoping adaptive filter. We provide theoretical false-positive and space guarantees of our filter, along with empirical results where we compare its false positive performance against state-of-the-art filters. We also test the throughput of our filters, showing that they achieve comparable performance to similar non-adaptive filters.

cs.DS↗