{"total":16,"items":[{"citing_arxiv_id":"2606.13556","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation","primary_cat":"cs.AI","submitted_at":"2026-06-11T16:38:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A proposed Bayesian architecture that initializes physiological set-point priors from GWAS-derived genomic anchors and decays them toward empirical baselines as data accrue.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13024","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts","primary_cat":"cs.LG","submitted_at":"2026-06-11T07:57:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CausalMoE is a multimodal foundation model with pattern-routed heterogeneous experts and LLM/VLM integration that claims new SOTA performance on supervised and few-shot Granger causal discovery benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10592","ref_index":100,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting","primary_cat":"cs.LG","submitted_at":"2026-06-09T08:56:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11251","ref_index":8,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems","primary_cat":"cs.LG","submitted_at":"2026-06-08T15:23:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21316","ref_index":12,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bitcoin's Power Law: Weak Structure, Strong Forecasts","primary_cat":"stat.AP","submitted_at":"2026-05-20T15:46:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bitcoin price power law is rejected on distributional series and not robust to time-origin shifts, but dominates medium-horizon forecasts against baselines because it avoids committing to specific wave shapes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13417","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"State-resolved multimodal contributions to stratospheric polar vortex predictability","primary_cat":"physics.ao-ph","submitted_at":"2026-05-13T12:13:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Stratospheric polar vortex predictability is multimodal, with short-term forecasts dominated by persistence of the leading state and extended forecasts arising from higher-order stratospheric structures plus tropospheric variability.","context_count":1,"top_context_role":"dataset","top_context_polarity":"use_dataset","context_text":"through cross-level coupling. This multimodal organization is strongly state dependent, with enhanced contributions from higher-order states during SSW winters. 2 Data and Methods 2.1 Data and preprocessing We analyze geopotential height fields from the ERA5 reanalysis at 10 hPa and 500 hPa, representing the stratospheric and tropospheric circulation, respectively[30]. The analysis domain is the Arctic polar cap (65 ◦-90◦N) on a 2.5 ◦ ×2.5 ◦ grid, and the study period spans 1948-2023. Daily mean fields are computed from 6-hourly data. To isolate subsea- sonal variability, a multi-year daily climatology is removed at each grid point, yielding 2 deseasonalized anomaly fields. We denote the anomaly fields at timetasx S(t) and"},{"citing_arxiv_id":"2605.09870","ref_index":173,"ref_count":1,"confidence":0.88,"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":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07267","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning","primary_cat":"cs.LG","submitted_at":"2026-05-08T05:31:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"This limits their usefulness for intervention reasoning, pathway-level explanation, and reliable decision support [13, 35]. Causal representation learning and individual treatment effect estimation provide complementary tools for counterfactual reasoning from obser- vational data [ 12, 27], while temporal causal discovery methods model lagged dependencies in time series [7, 20]. Yet many such methods remain cohort-level: they estimate a single structure or treatment-response function and therefore struggle to represent heterogeneity in physiology, behavior, comorbidity, and treatment response. The core challenge is to learn causal structure at the level at which healthcare decisions are made: the individual patient, at the current point in time."},{"citing_arxiv_id":"2605.03045","ref_index":268,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations","primary_cat":"cs.LG","submitted_at":"2026-05-04T18:12:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00398","ref_index":47,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data","primary_cat":"cs.LG","submitted_at":"2026-05-01T04:40:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25096","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Dynamics of Delusion: Modeling Bidirectional False Belief Amplification in Human-Chatbot Dialogue","primary_cat":"cs.CL","submitted_at":"2026-04-28T01:00:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A latent state model of real chat logs shows chatbots sustain delusional beliefs longer than humans initiate them, forming feedback loops where chatbot self-influence dominates over time.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.19491","ref_index":41,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics","primary_cat":"physics.chem-ph","submitted_at":"2026-04-21T14:11:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"for solvent.Proceedings of the National Academy of Sciences2007,104, 10430- 10434, Publisher: Proceedings of the Na- tional Academy of Sciences. (40) Zhou, R.; Huang, X.; Margulis, C. J.; Berne, B. J. Hydrophobic Collapse in Mul- tidomain Protein Folding.Science2004, 305, 1605-1609, Publisher: American As- sociation for the Advancement of Science. (41) Athawale, M. V.; Goel, G.; Ghosh, T.; Truskett, T. M.; Garde, S. Effects of lengthscales and attractions on the col- lapse of hydrophobic polymers in water. Proceedings of the National Academy of Sciences2007,104, 733-738. (42) Saura, P.; Riepl, D.; Frey, D. M.; Wik- str¨ om, M.; Kaila, V. R. I. Electric fields control water-gated proton transfer in cy-"},{"citing_arxiv_id":"2604.12563","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Latent community paths in VAR-type models via dynamic directed spectral co-clustering","primary_cat":"stat.ME","submitted_at":"2026-04-14T10:45:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Dynamic directed spectral co-clustering on degree-corrected stochastic co-blockmodels embedded in VAR-type models uncovers latent community paths, with non-asymptotic misclassification bounds and applications to U.S. payrolls and global stock volatilities.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The analysis of high-dimensional vector autoregressive (VAR) models is often constrained by the curse of dimensionality. Even when regularization techniques yield sparse estimates (e.g., Basu and Michailidis, 2015; Brownlees et al., 2018; Barigozzi et al., 2024), direct inspection of individual coefficients-each representing predictive influence in the sense of Granger (1969)-rarely provides a coherent account of system-wide dependence. In many macroeconomic and financial applications, the main question is not simply which coefficients are nonzero, but how the system organizes itself into interpretable groups and how that organization changes across seasons or dependence horizons. To address this, we study a dynamic network framework forlatent community paths: ordered"},{"citing_arxiv_id":"2604.10371","ref_index":10,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Structural Gating and Effect-aligned Lag-resolved Temporal Causal Discovery Framework with Application to Heat-Pollution Extremes","primary_cat":"cs.LG","submitted_at":"2026-04-11T22:50:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"SGED-TCD is a lag-resolved causal discovery framework that uses structural gating and perturbation-effect alignment to infer interpretable weighted causal networks from complex time series, shown on heat-pollution extremes in China.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Figure 2: Standardized time series of major large-scale teleconnection indices. Figure 3: Lagged correlations between teleconnection indices and EC near-surface tem- perature (T2m). season stratification introduced in Section 2. Monthly time series are used throughout in order to emphasize large-scale variability while suppressing high-frequency fluctuations that are less relevant to teleconnection processes [10, 17]. Prior to model fitting, all variables listed in Table 1 are standardized to zero mean and unit variance. For variables with pronounced seasonality (e.g., T2m, O3, and PM2.5), the climatological monthly mean is removed 12 Figure 4: Seasonal cycles of key meteorological mediators over EC and NC. before standardization to reduce seasonal leakage and to ensure that the"},{"citing_arxiv_id":"2604.03274","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Financial Dynamics and Interconnected Risk of Liquid Restaking","primary_cat":"q-fin.GN","submitted_at":"2026-03-23T10:58:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Renzo liquid restaking revenue is primarily predicted by EigenLayer value locked, token yield, and multi-blockchain expansion, with current bridge risks not imposing systemic threats to the restaking ecosystem.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2409.15835","ref_index":103,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Measures and Models of Brain-Heart Interactions","primary_cat":"q-bio.OT","submitted_at":"2024-09-24T08:03:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":1.0,"formal_verification":"none","one_line_summary":"The paper reviews existing signal processing strategies for brain-heart interactions, their usability in biomarker development, and key challenges for future work.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Most used approaches rely on Granger-causality-based and entropy-based techniques quantifying the directed information transfer between signals and implemented via linear model-based or nonlinear model-free estimators [99], mutual nonlinear predictions detecting asymmetric relations in pairs of signals [91], [100], [101], and synthetic causal models of the underlying generative neural dynamics, among other connectivity measures [102]. Granger causality is a statistical method that aims to determine whether a time series is useful in forecasting another [103]. Therefore, Granger causality (GC) measures are candidate tools for assessing directional interactions between time series. In brief, the method is performed considering a N-dimensional stochastic process X = [X1, …, XN] and implementing a prediction model to estimate the information transferred from the scalar process Xi to the process Xj (i,j ∈ 1,."}],"limit":50,"offset":0}