{"total":16,"items":[{"citing_arxiv_id":"2606.26990","ref_index":15,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Decision-Aligned Evaluation of Uncertainty Quantification","primary_cat":"cs.LG","submitted_at":"2026-06-25T13:05:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces decision-alignment to evaluate uncertainty metrics against downstream decision utilities and proposes prior-weighted proper scoring rules that align better in benchmarks and case studies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.23614","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Log-concavity and tunneling: adiabatic quantum optimization for convex functions (with a spike)","primary_cat":"quant-ph","submitted_at":"2026-06-22T17:12:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Establishes discrete log-concavity of ground states for convex potentials and extends Reichardt's HWS tunneling analysis to quadratic spikes via new spectral gap bounds.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19148","ref_index":128,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Fast Computation of Free-Support Wasserstein Medians","primary_cat":"stat.CO","submitted_at":"2026-06-17T14:50:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12268","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Impossibility of Eliciting Latent Knowledge","primary_cat":"cs.AI","submitted_at":"2026-06-10T16:11:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proves that no behavior-dependent feedback training strategy can guarantee an honest agent for latent knowledge even with perfect training feedback.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.09391","ref_index":38,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Kling-Gupta linear regression","primary_cat":"math.ST","submitted_at":"2026-06-08T12:06:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Kling-Gupta linear regression scales the OLS coefficient vector by a variance-inflation factor based on sample moments, preserves response variance on the training set, and converges almost surely to explicit population limits while maximizing KGE but not NSE.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.01218","ref_index":25,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Finite-Resolution Information from Collision Statistics","primary_cat":"cs.IT","submitted_at":"2026-05-31T13:11:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Collision statistics yield finite-resolution approximations to Shannon entropy and mutual information via Rényi entropies, with derived interpolation-error bounds that separate deterministic approximation error from finite-sample estimation error.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00233","ref_index":84,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Density Evolution: A Multiscale View of Density Estimation","primary_cat":"math.ST","submitted_at":"2026-05-29T18:08:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27168","ref_index":46,"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":"2605.26909","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Nonmonotone Descent Method for Optimization Problems Defined by Upper-$\\mathcal{C}^2 $ Functions over Submanifolds","primary_cat":"math.OC","submitted_at":"2026-05-26T12:06:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A nonmonotone subgradient algorithm is developed for upper-C^2 optimization on submanifolds with stationarity and KL-based convergence guarantees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23596","ref_index":181,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Multi-layered model-based characterisation of the local-Universe galaxy data from the GAMA survey","primary_cat":"astro-ph.GA","submitted_at":"2026-05-22T13:05:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"MtFAD plus MOBSynC on GAMA data yields eight simple clusters that merge into red and blue sequences containing substructure tied to mass quenching, environmental quenching, morphology and environment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12830","ref_index":45,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach","primary_cat":"stat.ME","submitted_at":"2026-05-12T23:54:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07671","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting","primary_cat":"cs.GT","submitted_at":"2026-05-08T12:42:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Our framework complements Lizzeri's by studying what happens when the intermediary canmisreport(not just withhold), disciplined by a proper scoring mechanism. 1.6 Related Literature Proper scoring rules and elicitation theory.The characterization of strictly proper scoring rules originates with de Finetti [17], Brier [13], McCarthy [44], and Savage [55]. The definitive modern treatment is Gneiting and Raftery [27]. Schervish [56] provides the general characterization linking properness to convex functions. Lambert et al. [37] uses conjugate duality to characterize elicitable properties of probability distributions. The connection between proper scoring rules and convex analysis is further developed by Abernethy and Frongillo [1] and the information-elicitation litera-"},{"citing_arxiv_id":"2605.07096","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Query-efficient model evaluation using cached responses","primary_cat":"cs.LG","submitted_at":"2026-05-08T01:24:06+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00618","ref_index":58,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus","primary_cat":"cs.CL","submitted_at":"2026-05-01T12:41:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Machine translation preserves embedding similarity structure for ten languages but distorts it for four in the Manifesto Corpus, via a new non-inferiority testing framework.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"changes resulting in an alteration or loss of meaning. Those include not only mistranslation, but also under-translation, i.e., omission of words or phrases; over-translation, i.e., addition of content not present in the source; syntactic modifications that affect meaning; and logical inconsistencies [25]. More subtle shifts include failure to convey the sentiment polarity of the source [58]. Such shifts are disproportion- ately frequent for low-resource languages [1]. Indeed, NMT research reports that for languages with scant training data, critical translation errors (omissions, mistranslations) are far more prevalent, often re- quiring extensive post-editing [58]. In such cases, vital information (like negation or modality) may be mistranslated, directly affecting"},{"citing_arxiv_id":"2605.00069","ref_index":91,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Soft-MSM: Differentiable Context-Aware Elastic Alignment for Time Series","primary_cat":"cs.LG","submitted_at":"2026-04-30T11:01:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Soft-MSM is a smooth, gradient-enabled version of the context-aware MSM distance for time series alignment that outperforms Soft-DTW alternatives in clustering and nearest-centroid classification.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18144","ref_index":105,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Self-referentiality and asymmetric knowledge flows between journals. The case of economics","primary_cat":"econ.GN","submitted_at":"2026-04-20T12:07:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Economics shows declining overall self-referentiality after 2008 but persistent high closure in CORE and Finance clusters, with CORE acting as a net knowledge exporter in a hierarchical journal system.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}