{"total":25,"items":[{"citing_arxiv_id":"2606.23661","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Prime-Power Rarefaction and a Density-One Lower Bound for Erd\\H{o}s Problem 400","primary_cat":"math.NT","submitted_at":"2026-06-22T17:47:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proves density-one lower bound g_k(n) ≥ (3(k-1)/log 12 - ε) log n for almost all n and pointwise upper bound g_k(n) ≤ (k-1)log2 n + log2 log n + O_k(1).","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21283","ref_index":36,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Reducing measurement error with adaptivity","primary_cat":"quant-ph","submitted_at":"2026-06-19T10:06:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Adaptive feed-forward circuits outperform non-adaptive parallel schemes in mitigating error from noisy two-outcome qubit measurements, with advantage appearing at three uses and growing unbounded with more uses.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20346","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Effective discrete-modulated continuous variable QKD under general attacks","primary_cat":"quant-ph","submitted_at":"2026-06-18T15:15:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Finite-size security proof for discrete-modulated CV-QKD under general attacks using dimension reduction and entropy accumulation yields positive rates at block sizes of order 10^8.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17316","ref_index":37,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Approximation Preserving Coresets","primary_cat":"cs.DS","submitted_at":"2026-06-15T21:48:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces approximation-preserving coresets that guarantee cost preservation for near-optimal solutions and proves that even tiny approximation-factor distortion forbids coresets of that size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13244","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection","primary_cat":"quant-ph","submitted_at":"2026-06-11T11:56:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01522","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Type-Error Ablation and AI Coding Agents","primary_cat":"cs.PL","submitted_at":"2026-06-01T01:09:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Ablation experiment in Shplait finds that detailed type error messages improve AI agents' type-error repair rates over minimal messages or dynamic errors, with type systems adding further benefit.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27168","ref_index":66,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Grounding Text Embeddings in Stakeholder Associations","primary_cat":"cs.CL","submitted_at":"2026-05-26T15:24:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The Stakeholder Grounding Exercise shows neural text embeddings are 19-26pp less reliable than human experts at capturing semantic distinctions, with misalignment strongly correlated to poorer clustering performance (ρ=0.9), replicated across Danish policy and US AI domains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.25066","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity","primary_cat":"quant-ph","submitted_at":"2026-05-24T13:21:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12137","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NPAP: Network Partitioning and Aggregation Package for Python","primary_cat":"cs.SI","submitted_at":"2026-05-12T13:57:40+00:00","verdict":"ACCEPT","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"NPAP is a Python package built on NetworkX that supplies 13 partitioning strategies and two aggregation profiles for network graph reduction via a strategy pattern allowing custom extensions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09870","ref_index":179,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions","primary_cat":"cs.LG","submitted_at":"2026-05-11T01:54:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"tation, for use later in Theorem 5.13. Proposition 5.1(Convergence rate (van der Vaart, 2000; Hoeffding, 1963)).For the estimation error between the intervention effectˆei→jestimated fromMintervention samples and the true valuee∗ i→j, when 10 the variance ofXj|do(Xi =x)is bounded byσ2: |ˆei→j−e∗ i→j|=Op(M−1/2)(7) More precisely, by Hoeffding's inequality (Hoeffding, 1963): P ( |ˆei→j−e∗ i→j|>ϵ ) ≤2 exp ( −Mϵ2 2σ2 ) (8) (Proof: see Appendix A.) Corollary 5.2(Confidence interval (Wasserman, 2006)).The confidence interval for the intervention effect at confidence level1−αis given byˆei→j±zα/2·ˆσ/ √ M. 5.2 Sample Complexity We record the sample complexity for recovering all pairwise causal effects from simulator-generated interven-"},{"citing_arxiv_id":"2605.08242","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Explainable Unsupervised-to-Supervised Machine Learning Framework for Dietary Pattern Discovery Using UK National Dietary Survey Data","primary_cat":"q-bio.QM","submitted_at":"2026-05-07T09:05:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"An unsupervised-to-supervised ML pipeline on UK NDNS data discovers four dietary patterns, reproduces them with macro-F1 0.963 using a surrogate classifier, and interprets them via SHAP for potential clinical use.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"novel methods, including machine learning and probabilistic approaches, may help characterise dietary complexity in greater depth than traditional approaches alone [4]. Clustering is widely used for exploratory pattern discovery. K-means provides a simple centroid- based baseline [12], Gaussian Mixture Models allow probabilistic assignments [13-14], and hierarchical methods can reveal nested structure [15]. Because different algorithms impose different assumptions, comparing multiple methods and cluster numbers is preferable to relying on a single run [16-17]. Internal validation measures such as silhouette score, Davies-Bouldin index and Calinski- Harabasz index provide quantitative evidence [18-20], although final selection also requires"},{"citing_arxiv_id":"2604.21791","ref_index":172,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Rigorous Security Proofs for Practical Quantum Key Distribution","primary_cat":"quant-ph","submitted_at":"2026-04-23T15:48:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Rigorous security proofs for variable-length QKD, phase-error bounding with imperfect detectors, marginal-constrained entropy accumulation, and authentication reductions place practical QKD on firmer mathematical ground.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13493","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Low-Degree Fourier Threshold for Random Boolean Functions","primary_cat":"math.PR","submitted_at":"2026-04-15T05:26:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"For a uniform random Boolean function on p bits, its low-degree Fourier coefficients uniquely determine it with high probability precisely when d exceeds p/2 by an O(sqrt(p log p)) window.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08692","ref_index":53,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Arqon: A suite of control applications enabling a reliable quantum network","primary_cat":"quant-ph","submitted_at":"2026-04-09T18:25:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Arqon delivers reliable quantum network service via admission control and scheduling that satisfies defined reliability requirements for accepted demands in static topologies, with O(k^3) and O(N^3) complexity.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"plexity analysis of the Network Scheduler with thorough numeric evaluations that supply concrete time values for computation times in a variety of parameter regimes. In all simulations, the demands for service submitted by pairs of end nodes are based on the real minimum fidelity requirements of BQC (2, 6, or 10 qubit) [32, 53], e91 based QKD [1], teleportation [53], or any of these applications pre-pended by purification following the DEJMPS proto- col [35] of the bipartite entangled pairs. The other pa- rameters of each demand, such as the number of pairs and the window duration are set based on the application and the network capabilities database for that simulation [6]. Lists of the applications simulated in each simulation are"},{"citing_arxiv_id":"2604.04611","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns","primary_cat":"cs.LG","submitted_at":"2026-04-06T11:54:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"S2-WEF detects dynamic free-riders in federated learning by simulating attack WEF patterns from prior global models, combining them with mutual deviation scores, and using two-dimensional clustering without proxy data or pre-training.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.04328","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Soft Tournament Equilibrium","primary_cat":"cs.AI","submitted_at":"2026-04-06T00:40:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"STE is a differentiable method to compute continuous analogues of the Top Cycle and Uncovered Set from pairwise comparison data for stable set-valued evaluation of cyclic agent interactions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Forc̸=a, define the soft violation of the claim \"ccoversa\" as vτ(c, a) = smaxγc {Dτ(a, b)(1−D τ(c, b)) :b∈ A \\ {a, c}} \u0001 ,(12) with the convention that the maximum over an empty set is zero. The term inside the maximum is large whenahas strong evidence of beatingbandclacks such evidence. The soft cover score is coverτ(c, a) =D τ(c, a) 1−v τ(c, a) \u0001 ,cover τ(a, a) = 0.(13) This score lies in[0, 1]and converges to the hard covering indicator under the strict-margin, zero-temperature limit. 4.4.2 Soft Uncovered-Set Membership Score An agent belongs to the Uncovered Set if no other agent covers it. We first aggregate the evidence that some coverer exists: qτ(a) = smaxγc {coverτ(c, a) :c∈ A, c̸=a} \u0001 .(14) The soft Uncovered-Set membership score is then"},{"citing_arxiv_id":"2604.13050","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning","primary_cat":"cs.DB","submitted_at":"2026-03-17T01:29:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A framework applies frequent itemset mining with the negFIN algorithm and unsupervised learning to identify cities sharing co-occurring land use patterns from Copernicus Urban Atlas data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.05024","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Model-Free Assessment of Simulator Fidelity via Quantile Curves","primary_cat":"stat.ME","submitted_at":"2025-12-04T17:39:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A model-free method builds confidence sets for latent parameters to proxy sim-to-real discrepancies and estimates the quantile function of that proxy to produce a distribution-level fidelity profile for simulators.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2508.10997","ref_index":115,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Reliable high-accuracy error mitigation for utility-scale quantum circuits","primary_cat":"quant-ph","submitted_at":"2025-08-14T18:02:44+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"QESEM is a characterization-based error mitigation technique that achieves unbiased estimates with substantially reduced runtime cost compared to probabilistic error cancellation while outperforming zero-noise extrapolation on utility-scale circuits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.08599","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback","primary_cat":"cs.IT","submitted_at":"2025-07-11T13:47:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Two strategies are introduced for transmission over unknown binary erasure channels: a two-phase method achieving O(T^{2/3}) regret with one query and a windowing method achieving O(sqrt(T)) regret with O(log T) queries.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2507.01533","ref_index":18,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Consistency of Learned Sparse Grid Quadrature Rules using NeuralODEs","primary_cat":"math.NA","submitted_at":"2025-07-02T09:37:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proves PAC consistency and explicit convergence rates for learned transport integrated (LtI) quadrature using neural ODE flows for general targets and empirical quantile maps for product targets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.12582","ref_index":98,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Modular Quantum Network Architecture for Integrating Network Scheduling with Local Program Execution","primary_cat":"quant-ph","submitted_at":"2025-03-16T17:24:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The paper introduces a modular, hardware-agnostic architecture using entanglement packets for scheduling network operations in quantum networks to enable end-to-end entanglement generation integrated with local program execution, demonstrated via simulation on a 6-node star topology.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.14493","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"An Improved Pseudopolynomial Time Algorithm for Subset Sum","primary_cat":"cs.DS","submitted_at":"2024-02-22T12:38:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The authors give an Õ(n + √(wt))-time algorithm for Subset Sum.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1906.10179","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDE","primary_cat":"stat.ME","submitted_at":"2019-06-24T19:04:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Unifying framework for CTree, MOB and GUIDE shows model scores without dichotomization yield higher power for covariate selection than residuals or dichotomized scores in many scenarios.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1906.10495","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Approximating Unitary Preparations of Orthogonal Black Box States","primary_cat":"cs.CC","submitted_at":"2019-06-23T01:21:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"For two orthogonal black-box n-qubit states, a poly(n, 1/ε)-size approximating unitary exists that maps basis states to them while resetting all auxiliaries on every input.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}