{"total":16,"items":[{"citing_arxiv_id":"2607.00824","ref_index":35,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Practical Range Refinement Types with Inference","primary_cat":"cs.PL","submitted_at":"2026-07-01T11:44:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Ranger is a bidirectional refinement type system for integer range types, implemented in the Licorne language, that integrates inference and flow analysis to verify bounds properties with low annotation overhead compared to Java, Scala, Checker Framework, and Liquid Java.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22908","ref_index":20,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Cyclic Graphs and Memoization in Pure $\\lambda$-Calculus","primary_cat":"cs.PL","submitted_at":"2026-06-22T06:46:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A tabling operational semantics on weak-head reduction for pure λ-calculus produces sound finite cyclic graphs for finite-state terms while preserving lazy meaning and enabling automatic memoization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06747","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Tensor Algebraic Property Skeletons: Amplifying Property-Based Testing for AI Compilers","primary_cat":"cs.SE","submitted_at":"2026-06-04T22:01:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Propilot instantiates 20 tensor-algebra property skeletons into 4,579 executable PBTs for TVM, cutting redundancy 49% and surfacing semantic and numerical errors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.26291","ref_index":63,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Geo: A Query Rewrite Framework for Graph Pattern Mining","primary_cat":"cs.PL","submitted_at":"2026-05-25T19:28:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Geo is a framework for optimizing graph pattern matching queries via rewrite rules and equality saturation that discovers equivalences and reduces costs by up to 99%.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19005","ref_index":29,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Rewrite System Showdown: Stochastic Search vs. EqSat","primary_cat":"cs.PL","submitted_at":"2026-05-18T18:27:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Empirical comparison of equality saturation versus stochastic search on five benchmarks to evaluate if e-graphs are superior for rewrite-based optimization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17884","ref_index":29,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimizing Optimizations, Declaratively: Optimizing the Higher-Order Functions in Mathematical Optimization with egglog","primary_cat":"cs.PL","submitted_at":"2026-05-18T05:45:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Applies egglog equality saturation and datalog rules to optimize higher-order function handling for LaTeX output and constraint detection in a lambda-calculus-based mathematical optimization modeler.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10566","ref_index":24,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Affine Tracing: A New Paradigm for Probabilistic Linear Solvers","primary_cat":"stat.ML","submitted_at":"2026-05-11T13:36:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Bayesian PLSs are special cases of non-stationary affine PIMs which are proven calibrated, and affine tracing automates construction of probabilistic iterative methods from classical code.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08369","ref_index":39,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"First-Class Refinement Types for Scala","primary_cat":"cs.PL","submitted_at":"2026-05-08T18:23:48+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"partial","one_line_summary":"Refinement types are integrated as first-class citizens in Scala 3 with full participation in the type system, backed by a mechanized soundness proof in Rocq and a prototype compiler extension using an e-graph solver.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"classBox(valvalue:Int) valb:(Boxwithb==Box(3))=Box(3) // rejected because Box has reference equality We do not currently enforce transitive purity of called functions: using an impure function in a predicate is a logical error. This is a deliberate choice to simplify gradual adoption without imposing a large upfront annotation burden. Scala 3's capture tracking system [39], which tracks effects and mutation through capabilities, provides a natural path toward enforcing purity in the future; recent work on safe mode [22] further restricts the language to make capability-based purity checking practical. Integrating these systems with refinement types is a non-trivial problem in its own right, which we leave to future work."},{"citing_arxiv_id":"2604.28079","ref_index":85,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Tailwind: A Practical Framework for Query Accelerators","primary_cat":"cs.DB","submitted_at":"2026-04-30T16:25:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Tailwind introduces ALPs and ML-based planning to integrate workload-specific query accelerators into standard RDBMSes, achieving 1.38x average (up to 29x) speedup on TPC-H queries.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"In Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013) (ICDE '13). IEEE Computer Society, Washington, DC, USA, 1081-1092. doi:10.1109/ICDE.2013.6544899 [84] Yang Wu, Xuanhe Zhou, Yong Zhang, and Guoliang Li. 2024. Automatic Index Tuning: A Survey. IEEE Trans. on Knowl. and Data Eng. 36, 12 (Dec. 2024), 7657-7676. doi:10.1109/TKDE.2024.3422006 [85] Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, and Tim Kraska. 2024. Stage: Query Execution Time Prediction in Amazon Redshift. In Companion of the 2024 International Conference on Management of Data (Santiago AA, Chile) (SIGMOD '24). Association for Computing Machinery, New York, NY,"},{"citing_arxiv_id":"2604.17364","ref_index":21,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"LLM-Guided Strategy Synthesis for Scalable Equality Saturation","primary_cat":"cs.AI","submitted_at":"2026-04-19T10:21:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EggMind automates EqSat strategy synthesis via LLMs and EqSatL, cutting final cost 45.1% and peak RAM 69.1% versus full equality saturation on vectorization benchmarks while transferring to tensor compilers.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"egg: Fast and extensible equality saturation.Proceedings of the ACM on Programming Languages 5, POPL (Jan. 2021), 1-29. doi:10.1145/3434304 [20] Jules Merckx, Alexandre Lopoukhine, Samuel Coward, Jianyi Cheng, Bjorn De Sutter, and Tobias Grosser. 2026. E-Graphs as a Persistent Compiler Abstraction. arXiv:2602.16707 [cs.PL]https://arxiv.org/abs/ 2602.16707 [21] Benjamin Mikek, Danylo Vashchilenko, Bryan Lu, and Panpan Xu. 2026. Agentic Code Optimization via Compiler-LLM Cooperation. arXiv:2604.04238 [cs.PL]https://arxiv.org/abs/2604.04238 [22] Chandrakana Nandi, Max Willsey, Adam Anderson, James R. Wilcox, Eva Darulova, Dan Grossman, and Zachary Tatlock. 2020. Synthe- sizing structured CAD models with equality saturation and inverse"},{"citing_arxiv_id":"2604.15272","ref_index":34,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Prism: Symbolic Superoptimization of Tensor Programs","primary_cat":"cs.PL","submitted_at":"2026-04-16T17:43:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Prism is the first symbolic superoptimizer for tensor programs that uses sGraph for compact representation of program families, two-level search, e-graph equivalence checking, and auto-tuning to achieve up to 2.2x speedup over prior superoptimizers on LLM workloads.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.14825","ref_index":42,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Nautilus: An Auto-Scheduling Tensor Compiler for Efficient Tiled GPU Kernels","primary_cat":"cs.PL","submitted_at":"2026-04-16T09:55:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Nautilus auto-compiles math-like tensor descriptions into optimized GPU kernels, delivering up to 42% higher throughput than prior compilers on transformer models across NVIDIA GPUs.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"RQ4:Ablation studies: How much do components of Nau- tilus contribute to the performance of Nautilus's attentions? 8.1 End-to-End Model Inference Overall Trends.To answer RQ1, we evaluate Nautilus on two hardware generations: GH200 (Hopper) and RTX 5090 (Blackwell), using five representative foundation models: GLM [14], Llama2 [38], Qwen2 [43], Qwen3 [42], and ViT [13]. Table 3 and Table 4 present the averaged relative throughput of Nautilus relative to state-of-the-art baselines including both vendor-optimized libraries (FlashAttention-2, SDPA) and compiler-based approaches (FlexAttn, Tawa [7]), where each cell represents the geometric mean over varying se- quence lengths and batch sizes. Across the five representative foundation models in Ta-"},{"citing_arxiv_id":"2511.22267","ref_index":26,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Aquas: Enhancing Domain Specialization through Holistic Hardware-Software Co-Optimization based on MLIR","primary_cat":"cs.AR","submitted_at":"2025-11-27T09:43:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Aquas delivers a holistic hardware-software co-optimization framework on MLIR that models memory interfaces with cache effects and uses an e-graph retargetable compiler, achieving up to 15.61x speedup with 14.5% area overhead across four domains.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.20782","ref_index":51,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimism in Equality Saturation","primary_cat":"cs.PL","submitted_at":"2025-11-25T19:19:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A new abstract interpretation algorithm enables sound optimistic analysis of e-graphs during equality saturation, unifying it with non-destructive rewriting and improving precision on cyclic SSA programs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.15000","ref_index":86,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Bonsai: Compiling Queries to Pruned Tree Traversals","primary_cat":"cs.PL","submitted_at":"2025-11-19T00:50:20+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":"2406.05417","ref_index":32,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Optimizing Navigational Graph Queries","primary_cat":"cs.DB","submitted_at":"2024-06-08T09:41:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Novel optimization techniques for navigational graph queries achieve orders of magnitude performance gains over prior methods on diverse real-world workloads.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}