{"total":14,"items":[{"citing_arxiv_id":"2606.31455","ref_index":6,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Discrete time-multidimensional renewal theory and applications","primary_cat":"math.PR","submitted_at":"2026-06-30T10:27:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces multi-time renewal chains via multi-index convolution and power series, with FFT-based computation, asymptotic theorems under proportional growth, and a nonparametric MLE for fixed-horizon censored data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.31110","ref_index":208,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Explaining Machine Learning and Memorization with Statistical Mechanics","primary_cat":"cs.LG","submitted_at":"2026-06-30T04:15:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Thesis uses statistical mechanics to study DAM and RBM models for understanding memorization, low-dimensional learning, and adversarial robustness in neural networks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17593","ref_index":22,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Why Model Credibility Isn't Enough: -Rethinking Trust in Simulation Architectures","primary_cat":"cs.SE","submitted_at":"2026-06-16T06:56:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"This paper reviews methods for assessing credibility of simulation architectures made from multiple models, comparing sensitivity analysis, expert judgment, AI explainability, and network techniques on rigor, generalization, and resource requirements.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07516","ref_index":11,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Counterintuitive problems in discrete probability","primary_cat":"math.PR","submitted_at":"2026-06-05T17:59:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"A curated dataset of counterintuitive discrete probability problems with human solutions, built to benchmark LLM reasoning on bias-prone tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04561","ref_index":7,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"A GPH-Filtered Hannan--Rissanen Information Criterion for ARFIMA Order Selection","primary_cat":"math.ST","submitted_at":"2026-06-03T07:49:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A consistent two-stage GPH-filtered Hannan-Rissanen generalized information criterion for selecting finite AR and MA orders in ARFIMA models with growing candidate sets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.24682","ref_index":68,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Scalable High-Dimensional Bayesian Field Reconstruction with Finite Elements: Application to 3D Porous Media Flow","primary_cat":"cs.CE","submitted_at":"2026-05-23T17:38:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A finite-element variational inference method delivers full-covariance Bayesian field reconstruction at dimensions exceeding 400,000 for 3D porous media flow using sparse precision parameterization from SPDE priors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06995","ref_index":32,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Partitioning Neural Co-Variability","primary_cat":"q-bio.QM","submitted_at":"2026-05-07T22:18:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The PMNLV model extends single-neuron overdispersion to populations via matrix-normal gain priors, showing shared co-variability highest in V1 and declining along the mouse visual hierarchy.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"overΘ ={f(X),U,V}, p(Y|Θ) = Z p(Y|N,Θ) p(N|U,V)dN.(3.2) The nonlinear link g(·) and the non-conjugacy between the Poisson likelihood and the matrix-normal prior, however, render this integral intractable. Integrals from this model type are often computed using the Laplace approximation [9, 30, 31], but this approach loses approximation accuracy in high-dimensions [32, 33]. We therefore propose two complementary estimation algorithms. The first (Section 4) places no structural assumptions on U or V and recovers dense factor estimates via variational EM (VEM). The second restricts the Kronecker factors to a parametric kernel family, trading generality for scalability via composite likelihood (Section 5). 4 Variational Expectation-Maximization (VEM)"},{"citing_arxiv_id":"2606.00048","ref_index":4,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"The Invisible Coalition Partner: How LLMs Vote When Democracy Gets Concrete","primary_cat":"cs.CY","submitted_at":"2026-05-03T08:48:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"LLMs display left bias on abstract policy questions but align with centrist parties and exhibit change-aversion on real Swiss federal referenda.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06090","ref_index":42,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Posterior Predictive Checks for Gravitational-wave Populations: Limitations and Improvements","primary_cat":"gr-qc","submitted_at":"2026-04-07T17:03:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Maximum-likelihood-based posterior predictive checks detect model misspecification better than event-level versions for uncertain spin tilts, but current detector sensitivity limits their power; the Gaussian Component Spins model underpredicts high spin magnitudes and overpredicts anti-aligned tilts","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"Mapelli, Proc. Int. Sch. Phys. Fermi200, 87 (2020), arXiv:1809.09130 [astro-ph.HE]. [39] J. Fuller, A. L. Piro, and A. S. Jermyn, Mon. Not. Roy. Astron. Soc.485, 3661 (2019), arXiv:1902.08227. [40] J. Fuller and L. Ma, Astrophys. J. Lett.881, L1 (2019), arXiv:1907.03714 [astro-ph.SR]. [41] V. Kalogera, Astrophys. J.541, 319 (2000), arXiv:astro- ph/9911417. [42] D. Gerosa, E. Berti, R. O'Shaughnessy, K. Belczynski, M. Kesden, D. Wysocki, and W. Gladysz, Phys. Rev. D 98, 084036 (2018), arXiv:1808.02491 [astro-ph.HE]. [43] N. Steinle and M. Kesden, Phys. Rev. D103, 063032 (2021), arXiv:2010.00078 [astro-ph.HE]. [44] D. Wysocki, D. Gerosa, R. O'Shaughnessy, K. Bel- czynski, W. Gladysz, E. Berti, M. Kesden, and D."},{"citing_arxiv_id":"2604.02522","ref_index":93,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Opal: Private Memory for Personal AI","primary_cat":"cs.CR","submitted_at":"2026-04-02T21:23:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"attacks substantially harder. In our deployment (§6), all enclaves run in TDX-backed CVMs, and EnclaveEmb and EnclaveLLM use NVIDIA B200 GPUs in confidential-computing mode. In particular, TDX removes the host's direct software visibility into TD-private guest page tables and memory, so classic guest-PTE accessed-bit oracles do not directly apply to TD-private memory [ 93, 94, 96, 202]. On the GPU side, confidential-computing mode removes man- agement, debugging, and profiling observability [ 150]. Deployed platforms further mitigate residual channels operationally [19, 205, 215]. 3 Disk Data ORAM ANN ORAM LLM Model Embedding Model Client Opal Controller Knowledge Graph ANN Metadata ORAM Clients 31 8 00 2 34 4"},{"citing_arxiv_id":"2604.16393","ref_index":10,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"How Do Developers Interact with AI? An Exploratory Study on Modeling Developer Programming Behavior","primary_cat":"cs.SE","submitted_at":"2026-03-28T14:37:19+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Developers using AI assistants exhibit more stable emotions and greater focus on code creation, evaluation, and verification, captured in a new four-dimensional S-IASE model from retrospective labeling of screen recordings, surveys, and interviews.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2603.24501","ref_index":5,"ref_count":2,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Efficiency for Experts, Visibility for Newcomers: A Case Study of Label-Code Alignment in Kubernetes","primary_cat":"cs.SE","submitted_at":"2026-03-25T16:42:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Case study of 18,020 Kubernetes PRs shows label-diff congruence is prevalent and stable, with higher congruence linked to fewer review participants among core developers and more among one-time contributors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2601.21293","ref_index":38,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning","primary_cat":"cs.LG","submitted_at":"2026-01-29T05:46:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PG-TMT couples a physics-aligned tri-branch encoder with EVT-calibrated decision rules to achieve higher PR-AUC and shorter detection times at controlled false-alarm rates across multiple bearing datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2405.09570","ref_index":1,"ref_count":1,"confidence":0.5,"is_internal_anchor":false,"paper_title":"FunnelNet: An End-to-End Deep Learning Framework to Monitor Digital Heart Murmur in Real-Time","primary_cat":"eess.SP","submitted_at":"2024-05-10T03:12:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"FunnelNet is a ~5.4k-parameter CNN that detects heart murmurs from PCG signals at 85% accuracy and deploys in real time on edge hardware via TinyML.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}