CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery

• URL: http://arxiv.org/abs/2607.16127v1
• Date: Fri, 17 Jul 2026 17:04:20 GMT
• Title: CADAQUES: A Cost-Aware Dual Architecture for Query-Efficient Autonomous Discovery
• Abstract summary: CADAQUES is a first-class primitive of the discovery loop built on one architectural principle.<n>It charges both evaluations and decisions against a common vector-valued budget spanning wall time, CPU hours, monetary cost, and language model tokens.<n>We evaluate the architecture by locating the critical temperature of the two-dimensional Ising model from noisy finite-size estimates.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

Related papers

• Efficient Swing Computation for Retrieval in Large-Scale Recommender Systems [12.932134727922245]
  We present ASC and $K$ASC, two novel and efficient algorithms for approximate and top$K$ Swing queries.<n>These algorithms provide rigorous theoretical guarantees in probabilistic relative and additive errors of Swing values.
  arXiv  Detail & Related papers  (2026-09-15T08:42:59Z)
• Pandora's AI Model Routing Box: Efficient Allocation with Costly Value Estimation [64.51088225051959]
  Heterogeneous AI systems can improve quality and efficiency by routing queries to the specialist who can answer most effectively at the lowest cost.<n>We formalize this tradeoff as an instance of Pandora's Box, the classical problem of optimal search with costly inspection.<n>Under a Gaussian signal model, the resulting policies have closed-form value-of-information expressions that determine, for each specialist and input, whether refining the value estimate is worth its cost.
  arXiv  Detail & Related papers  (2026-08-20T17:54:37Z)
• Cost-Aware Multi-Objective Bandits: Theory and Application to Budgeted LLM Configuration Evaluation [23.566982511686096]
  We formulate large language model (LLM) configuration evaluation as a cost-aware multi-objective bandit problem.<n>For online configuration selection, we propose a hyper-based UCB algorithm that optimize an optimistic hypervolume-per-cost index.<n>Experiments on LLM configuration evaluation tasks demonstrate that the proposed framework enables efficient online decision-making.
  arXiv  Detail & Related papers  (2026-08-05T01:21:02Z)
• Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design [0.7500610196128554]
  We show that a cheap probabilistic surrogate can triage the generator's output, and what such a surrogate must do well.<n>A cross-factorial benchmark of surrogate performance spanning mechanical, electronic, and vibrational properties identifies pretrained ORB embeddings with a Gaussian process as the most reliable combination.<n>The complete pipeline is released as open-source software.
  arXiv  Detail & Related papers  (2026-06-26T20:10:01Z)
• Instance-Optimal Estimation with Multiple LLM Judges on a Budget [84.31744861038106]
  We formalize this question as *budgeted heteroskedastic multi-judge estimation*.<n>Given $K$ prompt-response pairs, $J$ judges with known costs, and unknown query-judge variances, the goal is to estimate a bounded score vector while minimizing an $ell_p$-error.<n>We prove that EST-IVWE matches the oracle IVWE rate up to lower-order terms in the budget.
  arXiv  Detail & Related papers  (2026-05-22T08:26:08Z)
• ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
  $textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
  arXiv  Detail & Related papers  (2026-05-11T16:20:51Z)
• Matroid Algorithms Under Size-Sensitive Independence Oracles [19.61677355524952]
  Matroid algorithms assume that each independence query can be answered in constant time, regardless of the size of the queried set.<n>In particular, for natural and widely studied classes such as graphic matroids, even a single independence query can require work linear in the size of the set.<n>We introduce a size-sensitive cost model where the cost of a query $Q$ scales with $|Q|$.
  arXiv  Detail & Related papers  (2026-04-30T20:26:35Z)
• Learning Query-Aware Budget-Tier Routing for Runtime Agent Memory [56.0946692457838]
  BudgetMem is a runtime agent memory framework for explicit, query-aware performance-cost control.<n>A lightweight router performs budget-tier routing across modules to balance task performance and memory construction cost.<n>Across LoCoMo, LongMemEval, and HotpotQA, BudgetMem surpasses strong baselines when performance is prioritized.
  arXiv  Detail & Related papers  (2026-02-05T18:57:09Z)
• Rerank Before You Reason: Analyzing Reranking Tradeoffs through Effective Token Cost in Deep Search Agents [50.212640395029744]
  We study how to allocate reasoning budget in deep search pipelines.<n>Using the BrowseComp-Plus benchmark, we analyze tradeoffs between model scale, reasoning effort, reranking depth, and total token cost.
  arXiv  Detail & Related papers  (2026-01-20T18:38:35Z)
• Budget-aware Query Tuning: An AutoML Perspective [14.561951257365953]
  Modern database systems rely on cost-based querys to come up with good execution plans for input queries. We show that by varying the costunit values one can obtain query plans that significantly outperform the default query plans.
  arXiv  Detail & Related papers  (2024-03-29T20:19:36Z)
• EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models [6.109188517569139]
  We study how to maximize the re-ranking performance given a budget. We propose a suite of budget-constrained methods to perform text re-ranking.
  arXiv  Detail & Related papers  (2024-02-16T18:03:42Z)
• Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets [93.79297053429447]
  Existing methods often perform an independent architecture search for each target budget. We propose a general architecture generator that automatically produces effective architectures for an arbitrary budget merely via model inference. Extensive experiments on three platforms (i.e., mobile, CPU, and GPU) show the superiority of the proposed method over existing NAS methods.
  arXiv  Detail & Related papers  (2021-02-27T13:59:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.