{"total":11,"items":[{"citing_arxiv_id":"2606.24283","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Investigating causality between principal components in protein dynamics","primary_cat":"physics.chem-ph","submitted_at":"2026-06-23T08:04:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Applies causal inference to PCs from MD trajectories of two proteins to construct directed influence networks complementary to PCA and TICA.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21754","ref_index":6,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Embedded Polygon Symbolic Transfer Entropy (EPSTE): A Geometric Token and Deep Learning Approach to Estimating Transfer Entropy in Neuroimaging Time Series","primary_cat":"cs.IT","submitted_at":"2026-06-19T21:13:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EPSTE decomposes MEG time series into geometric symbolic tokens and uses an attention RNN to predict surrogate-validated transfer entropy, recovering directed structure more accurately than a standard symbolic baseline on AAL90-parcellated data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.16403","ref_index":155,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Co-evolution of bar and spiral arms in TNG50 simulations using Information Theory","primary_cat":"astro-ph.GA","submitted_at":"2026-06-15T08:42:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Information-theoretic analysis of TNG50 simulations finds high mutual information (0.4-0.8) between bar and spiral parameters and comparable transfer entropy (0.33-0.42) in both directions, indicating mutual co-regulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19795","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Function, Complexity and Thermodynamics in Adaptive and Intelligent Soft Matter Systems: An Information-Theoretical Formulation","primary_cat":"cond-mat.soft","submitted_at":"2026-05-19T12:58:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Soft matter systems are modeled as information channels of increasing complexity, yielding a heuristic thermodynamic ceiling on information processing performance and a performance gap to biology attributed to per-element energy scales.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17334","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Causal Anomaly Detection for Lithium-Ion Battery Degradation","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2026-05-17T08:53:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06346","ref_index":7,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Prediction and Empowerment: A Theory of Agency through Bridge Interfaces","primary_cat":"cs.AI","submitted_at":"2026-05-07T14:30:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"In deterministic partially observable worlds, perfect prediction requires either identifying the relevant hidden quotient or achieving overwrite control, while high empowerment alone is insufficient.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"t,κX t ), Xt+1 =f t(Xt,Ut,Λ,Ξ t), ϕ X t+1 =ℓX t+1(Xt+1,Λ,Ξ t+1),(4) Ot+1 =h t+1(Xt+1,ϕS t+1,ϕX t+1,Ξ t+1), M t+1 =g t(Mt,At,Ot+1).(5) The transcript is TT = (O0,A 0,O 1,A 1,...,AT−1,OT ).(6) For every deterministic policy, and for every randomized policy conditional onΣ =s, there are deterministic maps TT =G π T,s(Z), X T =F π T,s(Z), M T =S π T,s(Z).(7) 2 When the seed is irrelevant or the policy is deterministic, the subscriptsis omitted. Definition 1(Target quotient and losslessness).For a familyR of deterministic targets R :Z →YR, definez∼R z′iff R(z) = R(z′)for all R∈R. Let QR = qR(Z)be the quotient. A transcript is lossless forQ ifH( Q|TT ) = 0. It is microstate-lossless ifQ =Z onsupp(p 0)."},{"citing_arxiv_id":"2605.00398","ref_index":33,"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":"2604.23716","ref_index":40,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Information-Theoretic Measures in AI: A Practical Decision Guide","primary_cat":"cs.AI","submitted_at":"2026-04-26T14:00:22+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"in Bayesian neural networks [13], and regularizes maximum-entropy policies in rein- forcement learning. Cross-entropy is the default training loss for classification. Mutual information underpins self-supervised representation learning, feature selection, and the Information Bottleneck principle [47]. Transfer entropy reveals directed infor- mation flow in dynamical systems and multi-agent environments [40]. A second, more specialized family of measures (integrated information Phi, effective infor- mation EI, and autonomy) has emerged from computational neuroscience and is finding application in the analysis of evolved artificial agents and complex adaptive systems [3, 18]. Despite this breadth of adoption, measure selection is often decoupled from esti-"},{"citing_arxiv_id":"2604.19491","ref_index":50,"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":"461-464, Publisher: American Physical Society. (49) Del Tatto, V.; Fortunato, G.; Bueti, D.; Laio, A. Robust inference of causal- ity in high-dimensional dynamical pro- cesses from the Information Imbalance of distance ranks.Proceedings of the Na- tional Academy of Sciences2024,121, e2317256121, Number: 19 Publisher: Pro- ceedings of the National Academy of Sci- ences. (50) Runge, J. Causal network reconstruction from time series: From theoretical as- sumptions to practical estimation.Chaos 2018,28, 075310. (51) Spirtes, P.; Glymour, C.; Scheines, R. In Causation, Prediction, and Search, 2nd 13 ed.; Bach, F., Ed.; Adaptive Computation and Machine Learning series; MIT Press: Cambridge, MA, USA, 2001. (52) Zanotti, J."},{"citing_arxiv_id":"2604.10371","ref_index":19,"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":"A key design choice in teleconnection-oriented temporal causal discovery is the specification of the maximum lag windowL[9, 17, 18]. In the main experiments, we adopt L= 12months,(10) which is sufficiently long to capture seasonal memory, delayed ocean- atmosphere coupling, and the circulation adjustment timescales commonly associated with ENSO, North Atlantic variability, and mid-latitude wave propagation [19]. The choice of a 12-month lag window is physically motivated. Remote SST anomalies and large-scale circulation modes may affect East Asian cli- mate and air-quality-relevant meteorology with lead times ranging from sev- eral weeks to multiple seasons [19]. A substantially shorter lag window may truncate meaningful delayed pathways, whereas an excessively long lag win-"},{"citing_arxiv_id":"2409.15835","ref_index":115,"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":"In epilepsy, GC revealed a dominance of the brain-to-heart causality, over the heart-to-brain counterpart, suggesting that the central control on autonomic dynamics during the ictal phase of the seizures [114]. The concept of statistical causality has been formalized also in terms of entropy-based methods such as the Transfer Entropy (TE), which provides a model-free probabilistic tool to assess the information transfer between two time series [115]. While standard mutual information approaches fail in distinguishing the directed information exchange between processes, information transfer estimates can distinguish the driving and responding elements, and detect potential asymmetries on the interactions [115], [116]. TE has been proposed as an alternative measure of effective connectivity [117], used as well to describe brain-heart interplay, which has been tested in sleep and schizophrenia."}],"limit":50,"offset":0}