{"total":22,"items":[{"citing_arxiv_id":"2606.23492","ref_index":65,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Continuous Hidden Markov Models for Equity Returns: Heavy-Tail Emission Families and Regime-Conditional Value-at-Risk","primary_cat":"q-fin.ST","submitted_at":"2026-06-22T15:39:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Heavy-tailed continuous HMMs recover volatility clustering and produce regime-conditional VaR that passes joint conditional coverage tests on US equity data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.00664","ref_index":52,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models","primary_cat":"cs.RO","submitted_at":"2026-05-30T10:41:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SKIP achieves 4.16x faster dense video rollouts for robot world models by synthesizing only multimodal-identified keyframes and interpolating the rest, preserving policy training effectiveness with minimal success rate drops.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.29290","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SWORD: Spectral Wasserstein Online Regime Detection in Dynamic Networks","primary_cat":"cs.CG","submitted_at":"2026-05-28T03:13:14+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SWORD detects change points in dynamic graphs by averaging Chebyshev moments of the normalized Laplacian over two time windows and using L1 distance, improving mean F1 from 0.27 to 0.79 over prior spectral methods on real benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27914","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Does Capability Transfer to Subjective Behavior -- and Would Our Instruments Tell Us? A Self-Evolving, Trust-by-Construction Evaluation Paradigm","primary_cat":"cs.CL","submitted_at":"2026-05-27T03:41:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Self-evolving rubric with anti-gaming fitness reveals that objective capability scaling fails to transfer to subjective LLM behaviors, with advice-restraint as the universal lowest dimension that can regress.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23887","ref_index":2,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces","primary_cat":"cs.DB","submitted_at":"2026-05-22T17:47:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"CHRONOS is a three-layer system for evolving data marketplaces that applies neural-ODE temporal decay, changepoint-aware Shapley valuation, and EXP3-IX private coordination to achieve 0.937 recall, 2.74 qps, 161 ms latency, and epsilon 4.25 at delta 10^-6.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23419","ref_index":25,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Generalized Stochastic Approximation of the Log-Likelihood Ratio for Robust Sequential Change-Point Detection","primary_cat":"stat.ME","submitted_at":"2026-05-22T09:28:42+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":"2605.19231","ref_index":51,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DeRegiME: Deep Regime Mixtures for Probabilistic Forecasting under Distribution Shift","primary_cat":"cs.LG","submitted_at":"2026-05-19T01:04:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DeRegiME uses a sparse variational GP with nonstationary regime-mixing kernel to decompose forecasts into mean, residual regimes, and noise for improved probabilistic forecasting under distribution shift.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19151","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use","primary_cat":"cs.AI","submitted_at":"2026-05-18T22:11:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Trust calibration in agentic tool use is cast as preferential Bayesian optimization over a latent human risk-tolerance function observed through binary approve/deny feedback with a probit likelihood.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17117","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Geometric Observables for Financial Regime Detection","primary_cat":"q-fin.ST","submitted_at":"2026-05-16T18:49:00+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":"2605.16170","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"BAPR: Bayesian amnesic piecewise-robust reinforcement learning for non-stationary continuous control","primary_cat":"cs.LG","submitted_at":"2026-05-15T16:49:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"full","one_line_summary":"BAPR combines Bayesian change detection with robust RL, proves the core operator is a contraction via Lean 4, and adapts conservatism after detected regime shifts in continuous control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13551","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Mixed neural posterior estimation for simulators with discrete and continuous parameters","primary_cat":"cs.LG","submitted_at":"2026-05-13T13:57:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Extends NPE to mixed discrete-continuous parameter spaces via a factorized inference network combining an autoregressive classifier and generative model, trained jointly to yield accurate calibrated posteriors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18842","ref_index":126,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Safe Continual Reinforcement Learning under Nonstationarity via Adaptive Safety Constraints","primary_cat":"cs.LG","submitted_at":"2026-05-13T04:10:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LILAC+ combines context-based, adaptation-speed, and budget-to-state safety constraints to reduce violations in continual RL under nonstationary conditions, demonstrated in simulated driving tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10562","ref_index":33,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Data-driven moving-window Bayesian inference for transient CO2-temperature network models of buildings","primary_cat":"math.NA","submitted_at":"2026-05-11T13:35:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A moving-window Bayesian inference procedure jointly estimates thermal parameters, airflow, occupancy trajectories, and sensor noise in a coupled CO2-temperature RC network model for buildings, achieving accurate trajectory reconstruction and low forecast errors on synthetic and physical validation.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Finally, the moving-window Markov-chain Monte-Carlo configuration, while robust, can be computationally demand- ing for real-time deployment, especially when scaling to larger buildings or shorter update intervals. Based on the above, future research could focus on: (i) incorporating regime-change handling directly in the estimator, for example via change-point models [33]; (ii) exploring more computationally efficient inference alternatives to support the use of our model in real- time, building scale, digital-twin applications; (iii) integrating richer physics into the model, to reduce structural mismatches while still maintaining computational efficiency. Acknowledgments The authors acknowledge the ACD (Academic Committee for Design) of the Depart-"},{"citing_arxiv_id":"2605.09471","ref_index":109,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Statistical Cost of Adaptation in Multi-Source Transfer Learning","primary_cat":"math.ST","submitted_at":"2026-05-10T10:56:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07852","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"CHASM: Online Changepoint Detection in Temporal and Cross-Variable Dependence","primary_cat":"stat.ME","submitted_at":"2026-05-08T15:15:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CHASM detects changes in temporal and cross-variable dependence in multivariate time series by monitoring the truncated eigenvalue sequence of a recursively estimated DMD operator, using optimal assignment and augmented monitoring for complex values.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"assumptions and generalise across domains without relying on labelled data, which are rarely available. 1.1 Related work Online changepoint detection has received considerable attention since the cumulative sum (CUSUM) method [51]. Extensions cover multivariate and high-dimensional settings via non- parametric methods [47], Gaussian models [17, 18], and probabilistic approaches [1, 36]. While these provide strong baselines, they target distributional rather than dynamical changes. More recently, a related method [2] constructs a subspace-based CUSUM statistic to detect changes in dynamics. Changepoint detection in VAR processes focuses on the transition matrix. Offline methods locate changepoints via low-rank and sparse decompositions [5, 6] or likelihood-ratio statistics over"},{"citing_arxiv_id":"2605.06612","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Online Bayesian Calibration under Gradual and Abrupt System Changes","primary_cat":"cs.LG","submitted_at":"2026-05-07T17:29:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03326","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Sequential Bayesian Monitoring for Recoverable and Drifting Processes","primary_cat":"stat.CO","submitted_at":"2026-05-05T03:33:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Bayesian procedures are derived to compute the posterior probability that a recoverable process is currently in control or that a drifting latent parameter lies in an acceptable region.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"specified from prior knowledge, estimated from historical data, or included in the state vector if sequential parameter learning is required. The transition density may include both gradual drift and abrupt jumps. As before, the observation yt is modelled through a measurement density p(yt |θ t; γ). This gives the state-space formulation θt |θ t−1 ∼p(θ t |θ t−1;γ), yt |θ t ∼p(y t |θ t;γ). (7) We now give two simple examples which illustrate the formulation. Example 1: Monitoring a proportion.Consider the case of monitoring the proportion of failures in a manufacturing process, as in Section 3.1. Let θt represent the proportion of faulty items at time t. If a batch of N items is sampled at each time point, then the number of defective items yt follows a Binomial(N, θt) distribution."},{"citing_arxiv_id":"2605.01003","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pi-Change: A Prior-Informed Multiple Change Point Detection Algorithm","primary_cat":"stat.ME","submitted_at":"2026-05-01T18:11:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Pi-Change extends the PELT framework for multiple change point detection by incorporating prior information on locations through a time-varying penalty that preserves dynamic programming efficiency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20949","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Early Detection of Latent Microstructure Regimes in Limit Order Books","primary_cat":"cs.LG","submitted_at":"2026-04-22T17:47:52+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A three-regime causal model with a latent build-up phase enables a MAX-aggregation trigger detector to deliver positive expected lead time before observable stress in limit order books.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.06438","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Cost-sensitive retraining via posterior learning debt","primary_cat":"stat.AP","submitted_at":"2026-04-07T20:27:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Posterior learning debt enables cost-sensitive retraining decisions that outperform calendar-based and CUSUM methods in synthetic Bayesian simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.09758","ref_index":20,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Path Signature Framework for Detecting Creative Fatigue in Digital Advertising","primary_cat":"stat.AP","submitted_at":"2025-09-11T17:46:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The paper introduces a path signature-based method for detecting creative fatigue in digital ads using geometric change detection on performance paths.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1906.10372","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Dynamic time series clustering via volatility change-points","primary_cat":"stat.ME","submitted_at":"2019-06-25T08:18:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A Bayesian method clusters time series by similarity in the timing of their most recent volatility change-points via a metric on posterior distributions, demonstrated on S&P 500 returns.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}