{"total":19,"items":[{"citing_arxiv_id":"2605.23104","ref_index":48,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Query Lower Bounds for Correlation Clustering under Memory Constraints","primary_cat":"cs.CC","submitted_at":"2026-05-21T23:50:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.22963","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Graph Alignment Topology as an Inductive Bias for Grounding Detection","primary_cat":"cs.CL","submitted_at":"2026-05-21T18:49:32+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A GNN trained on bipartite alignment graphs between references and LLM generations reports state-of-the-art hallucination detection across four datasets, beating prior methods and GPT-4o.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19654","ref_index":148,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Hardness and Approximation for Coloring Digraphs","primary_cat":"cs.DS","submitted_at":"2026-05-19T10:44:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Establishes n^{1-ε}-hardness of approximation for dichromatic number and acyclic number on tournaments, plus polynomial-time approximations for ℓ-dicolorable digraphs and special dense cases.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17230","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Maximum Likelihood Decoding of Quantum Error Correction Codes","primary_cat":"quant-ph","submitted_at":"2026-05-17T02:32:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13214","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Backdoor Channels Hidden in Latent Space: Cryptographic Undetectability in Modern Neural Networks","primary_cat":"cs.CR","submitted_at":"2026-05-13T09:06:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Backdoors can be realized as statistically natural latent directions in modern neural networks, achieving high attack success with negligible clean accuracy loss and resisting existing defenses.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The black-box nature of deep neural networks conceals vulnerabilities exploitable asbackdoor channelswithin their latent activation spaces. This concern is not hypothetical: the modern machine learning supply chain increasingly depends on third-party datasets, pre-trained foundations, and specialised computation facilities, which has given rise toMachine Learning as a Service(MLaaS)[ 9, 25, 12]. Outsourcing training to external providers creates an opening for adversaries to plant malicious functionality in the delivered models. A well-studied class of threats are backdoor (or Trojan) attacks, in which an adversary embeds a hidden association such that the model performs normally oncleaninputs but produces attacker- controlled outputs when a specific input trigger is present [1]."},{"citing_arxiv_id":"2605.10778","ref_index":30,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"When Does Sparsity Help for k-Independent Set in Hypergraphs and Other Boolean CSPs?","primary_cat":"cs.CC","submitted_at":"2026-05-11T16:11:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Sparsity helps for k-independent set only below certain density thresholds, with new algorithms achieving O(min(n^{ωk/3} + m^{k/3}, n^k)) time and conditional lower bounds showing brute-force necessity above thresholds for many binary constraint families.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"4 When Does Sparsity Help for k-Independent Set in Hypergraphs and Other Boolean CSPs? 1.2 Result II: On the Influence of Sparsity for Boolean CSPs Boolean constraint satisfaction has long served as a challenge for understanding the precise limits of our algorithmic and complexity-theoretic methods. The extensive list of such works includes classifications of tractability in terms of P vs NP-complete [ 30, 36, 11], counting complexity [10], parameterized complexity [ 24, 12], approximability [20] and many more. We wish to explore how sparsity affects the fine-grained time complexity investigated in [ 21]. Formally, we define, for any finite constraint familyF and γ > 0, the algorithmic problem Cspγ k(F): given a set of m = Θ( nγ) constraints, each formed by applying some function"},{"citing_arxiv_id":"2605.09917","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Dynamic Rank, Basis, and Matching","primary_cat":"cs.DS","submitted_at":"2026-05-11T03:11:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09814","ref_index":50,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Streaming Complexity Separations for Dense and Sparse Graphs","primary_cat":"cs.DS","submitted_at":"2026-05-10T23:31:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Streaming max-cut requires Ω(n) space for dense graphs but Ω(n log(ε² n)/ε²) space for graphs with Θ(n/ε²) edges when outputting the cut, with matching upper bounds for dense case and similar separations for densest subgraph.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09782","ref_index":16,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Near-Linear Time Generalized Sinkhorn Algorithms for Bounded Genus Graphs","primary_cat":"cs.DS","submitted_at":"2026-05-10T22:00:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"GenusSink delivers near-linear-time approximate generalized Sinkhorn algorithms for bounded-genus graphs via separator decompositions, computational geometry, and fast matrix-vector multiplies with generalized distance matrices.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, editors,Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 2053-2061, 2015. URL https://proceedings.neurips. cc/paper/2015/hash/a9eb812238f753132652ae09963a05e9-Abstract.html. [16] Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis R. Bach. Stochastic optimization for large-scale optimal transport. In Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, editors,Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-"},{"citing_arxiv_id":"2604.21791","ref_index":79,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Rigorous Security Proofs for Practical Quantum Key Distribution","primary_cat":"quant-ph","submitted_at":"2026-04-23T15:48:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Rigorous security proofs for variable-length QKD, phase-error bounding with imperfect detectors, marginal-constrained entropy accumulation, and authentication reductions place practical QKD on firmer mathematical ground.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"when dealing with optical implementations of QKD, and are utilized in reducing infinite- dimensional state preparation and measurement operations to finite dimensions. Accordingly, the reader may safely defer a detailed study of these tools until they are encountered later in the thesis. They can also be used more generally to incorporate imperfections [56, 78, 79, 80]. 3.6.1 Source-Replacement Schemes The source-replacement scheme is a technique that can be used to describe Alice's prepara- tion of states in S=(A′) equivalently as her creating a pure, entangled state across S=( ¯A ˆAA′), and then performing measurements on ¯A. The A′ system then behaves as required, whereas the ˆA system is referred to as theshieldsystem, and is required to correctly describe"},{"citing_arxiv_id":"2604.15601","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Communication Complexity of Pattern Matching with Edits Revisited","primary_cat":"cs.DS","submitted_at":"2026-04-17T00:48:06+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"The one-way communication complexity of reporting k-edit occurrences (including the edit sequences) is Θ(n/m · k log(m|Σ|/k)) bits for 0 < k < m < n/2.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07493","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Differentially Private Modeling of Disease Transmission within Human Contact Networks","primary_cat":"cs.CR","submitted_at":"2026-04-08T18:34:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A differentially private pipeline using node-level DP summaries to fit ERGMs or SBMs, generate synthetic networks, and simulate SIS disease spread on ARTNet sexual contact data produces incidence, prevalence, and intervention effect sizes close to non-private versions.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"private graph neural networks, 2021. [20] data.org. Pets for public health challenge.https://data.org/initiatives/pets-challenge/, 2024. [21] W. Day, N. Li, and M. Lyu. Publishing graph degree distribution with node differential privacy. In Proceedings, ACM International Conference on Management of Data (SIGMOD), pages 123-138, 2016. doi: 10.1145/2882903.2926745. [22] L. Dhulipala, Q. C. Liu, S. Raskhodnikova, J. Shi, J. Shun, and S. Yu. Differential privacy from locally adjustable graph algorithms: k-core decomposition, low out-degree ordering, and densest subgraphs. InProceedings, IEEE Symposium on Foundations of Computer Science (FOCS), pages 754-765, 2022. doi: 10.1109/FOCS54457.2022.00077. [23] X. Ding, X."},{"citing_arxiv_id":"2602.10922","ref_index":60,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Implicit representations via the polynomial method","primary_cat":"cs.CG","submitted_at":"2026-02-11T15:00:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Semialgebraic graphs admit O(n^{1-2/(d+1)+ε})-bit adjacency labels via polynomial partitioning; semilinear graphs need only O(log n) bits.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.15849","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Connectivity-Preserving Important Separators: A Framework for Cut-Uncut Problems","primary_cat":"cs.DS","submitted_at":"2025-11-19T20:13:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Connectivity-preserving important separators of size at most k number 2^{O(k log k)} and can be enumerated in the same bound, yielding 2^{O(k log k)} FPT time for constant-class Node Multiway Cut-Uncut.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.10036","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Faster All-Pairs Minimum Cut: Bypassing Exact Max-Flow","primary_cat":"cs.DS","submitted_at":"2025-11-13T07:21:00+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"A cut-preserving sparsifier constructed from approximate max-flow enables faster all-pairs minimum-cut algorithms in unweighted graphs across cut-query, dynamic, and streaming models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.17314","ref_index":12,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Role of Regularity in (Hyper-)Clique Detection and Implications for Optimizing Boolean CSPs","primary_cat":"cs.CC","submitted_at":"2025-05-22T22:08:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Regularity in hypergraphs is fine-grained equivalent to the general case for clique detection, enabling a complete classification of k-sparse Boolean CSP optimization complexity by constraint degree: linear for d≤1, clique-equivalent for d=2, and exhaustive-search for d≥3 under 3-uniform hyperclique","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.09105","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Incremental Approximate Maximum Flow via Residual Graph Sparsification","primary_cat":"cs.DS","submitted_at":"2025-02-13T09:27:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Incremental (1-ε)-approximate s-t max-flow algorithm achieving Õ(m + n F*/ε) total update time, first with polylog amortized updates for dense graphs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2406.14111","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Expander Hierarchies for Normalized Cuts on Graphs","primary_cat":"cs.DS","submitted_at":"2024-06-20T08:50:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"First practical algorithm for expander hierarchies used to build a normalized-cut solver that beats state-of-the-art quality on large real-world graphs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.18500","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Conditional Independence of 1D Gibbs States with Applications to Efficient Learning","primary_cat":"quant-ph","submitted_at":"2024-02-28T17:28:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"1D translation-invariant Gibbs states at positive temperature exhibit superexponential decay of Belavkin-Staszewski conditional mutual information, enabling efficient learning from local measurements and tensor network approximations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}