CADAQUES is an open-source Python framework built on one architectural principle: cost is a first-class primitive of the discovery loop, which separates the loop into two structural protocols, an Oracle that answers queries and a Driver that proposes them, and charges both evaluations and decisions against a common vector-valued budget.
Abstract
Autonomous discovery systems couple a resource that answers queries (a simulator, instrument, or analytic model) to an algorithm that selects what to query next. Most software frameworks for this loop inherit the control structure of numerical optimization: campaigns run for a fixed number of iterations, query costs are absent from the programming interface, and decision-making is treated as free. In practice, queries may differ in cost by orders of magnitude, and planners built on large language models or expensive surrogates consume resources of their own. Here we present CADAQUES, an open-source Python framework built on one architectural principle: cost is a first-class primitive of the discovery loop. CADAQUES separates the loop into two structural protocols, an Oracle that answers queries and a Driver that proposes them, and charges both evaluations and decisions against a common vector-valued budget spanning wall time, CPU hours, monetary cost, and language model tokens. An append-only ledger records, for each transaction, the cost declared before execution and the cost settled afterwards, making their discrepancy an observable property of the campaign. We evaluate the architecture by locating the critical temperature of the two-dimensional Ising model from noisy finite-size estimates against the exact thermodynamic-limit reference. In this noisy setting, strategies that concentrate around the best observed result can be misled by noise-induced peaks, whereas a schedule that explores with cheap low-fidelity queries and refines with higher-fidelity ones yields lower and less variable errors than high fidelity throughout, at the studied budget scale. Metering adds tens of microseconds per iteration, three orders of magnitude below the cheapest oracle query. The framework is MIT-licensed and archived at Zenodo (doi:10.5281/zenodo.21293589).
AgentWeave is introduced, a deterministic pre-inference routing layer that constructs a bounded model-visible action space using eligibility, requirement, capability, and routing signals and shows lower mean local-model latency.
Saurav Singla, A. Singla, Advik Gupta et al.· 0 citations
Query planners are typically expected to produce optimized plans quickly, leading many researchers (including the authors of this paper) and practitioners to design systems that assume query planning is a low-cost operation. Using a lightweight agentic search, we show that this assumption does not always hold. Across seven DBMSes, including four commercial systems, we find at least one query per system that takes more than three minutes to plan. In addition to being slow to plan, such queries risk tying up database resources without performing useful work, creating a potential denial-of-service vector. We analyze the queries our search uncovers and compare how the seven systems respond to each pattern. We find that although the queries triggering slow planning are largely DBMS-specific, recurring pathologies involving correlated subqueries, CTE expansion, repeated subquery expressions, disjunctive joins, and constant folding affect multiple systems. We release our uncovered queries along with a curated suite of parameterized query pathologies that researchers and database engineers can use to test planner robustness. Overall, our results show that query planning cannot always be treated as a predictably inexpensive operation and that its latency and robustness deserve further attention from both database researchers and engineers.
Geoffrey X. Yu, Ryan Marcus, Tim Kraska· 0 citations
Query optimization remains a critical challenge in relational database management systems (RDBMSs). Traditional Cost-Based Optimization (CBOs) depend on static cardinality estimation and rigid heuristics that break down under skewed data distributions, complex join structures, and heterogeneous workloads. While learned query optimization methods offer improved cost estimation, they remain vulnerable to schema drift and unseen query templates and lack the interpretability required by database administrators. This paper presents LLM-QOpt++, a novel hybrid, confidence-aware query optimization framework that unifies traditional CBO estimation, machine learning–based cost prediction, and large language model (LLM) reasoning within a single adaptive pipeline. Key components include operator-level cost decomposition; an LLM-based query reasoning advisor that detects structured query language (SQL) anti-patterns and generates context-aware rewrite suggestions; a confidenceaware decision mechanism for adaptive strategy selection; a failed-plan learning memory (FPLM) that suppresses historically inefficient execution plans; and an explainability-guided layer leveraging SHapley Additive exPlanations (SHAP) attribution, attention analysis, and natural language reasoning. A dynamic hybrid fusion mechanism blends CBO, ML, and LLM signals according to per-query confidence estimates. Evaluated on TPC-H (SF100) and TPCDS (SF300), LLM-QOpt++ achieves a 38.4% reduction in mean absolute cost estimation error over the best learned baseline (95% CI: [35.1%, 41.9%]; p < 0.001), a 2.37_ geometric mean speedup over PostgreSQL’s native optimizer (95% CI: [2.21_, 2.53_]; p < 0.001), and an F1-score of 0.89 in strategy classification. By integrating statistical, learned, and LLM-based reasoning under a confidence-aware architecture, LLM-QOpt++ delivers query optimization that is simultaneously more accurate and more interpretable than existing baselines.
This work operationalizes constructive specification with constructive specification, which builds hierarchical capability representations from limited profiling over diverse benchmarks, using an optimism-guided profiler that prioritizes informative regions and prunes low-utility areas with guarantees, and enables plug-and-play identification of lightweight agents.
Jian-Dong Liu, Zi-Chen Zhao, Haodong Sun et al.· Proceedings of the 32nd ACM...· 1 citation
