Learning-Augmented Heuristics: Simple, yet Smart, Robust and Interpretable Cache Eviction
该研究提出Learning-Augmented Heuristics框架，通过学习静态启发式算法的缓存级参数来优化缓存淘汰策略，提高效率和鲁棒性。
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Representative Papers
New optimal linear codes over $\ZZ_4$
This study addresses the construction of improved linear codes over the quaternary ring $\mathbb{Z}_4$ by leveraging known linear codes over finite fields. The authors propose a novel methodology that integrates algebraic coding theory, computational search, and combinatorial optimization. For the first time, they fully characterize all optimal $\mathbb{Z}_4$-linear codes in the case $k_1=2, k_2=0$, and discover numerous new optimal codes when $k_1=3, k_2=0$. Several of the constructed codes attain established theoretical bounds for specific parameters, thereby substantially expanding the known landscape of optimal $\mathbb{Z}_4$-linear codes.
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Self-dual double cyclic codes over $\mathbb{F}_q$
This study addresses the existence and explicit construction of self-dual bicyclic codes over finite fields. By viewing bicyclic codes as submodules of a polynomial ring, the authors characterize their structure via pairs of generator polynomials and establish necessary and sufficient conditions for such codes to be self-dual. For the first time, systematic construction methods are proposed for specific code lengths—including $(r,r)$, $(r,2r)$, $(2r,r)$, and cases where $\gcd(r,s)=1$—by integrating finite field theory, the algebraic structure of cyclic codes, and properties of dual codes. Concrete examples are provided to demonstrate feasibility, and the work reveals intrinsic connections between these codes and other classes of self-dual codes, thereby filling a notable gap in the existing theoretical framework.
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anyakrakusuma: A Python Library for Entropic Schrödinger Bridges on Idealized Geometries
This work addresses the numerical instability of discrete static Schrödinger bridge problems—equivalently, entropy-regularized optimal transport—under high cost-to-regularization ratios by introducing a log-domain Sinkhorn–Knopp iteration algorithm. The proposed method achieves high-precision solutions even in extreme parameter regimes where the Gibbs kernel suffers severe underflow. A diagnostic framework based on information entropy, covariance analysis, and geometric metrics is developed to precisely characterize the structural properties of optimal couplings and intermediate distributions. The algorithm maintains marginal residuals as low as 10⁻⁹ and perfect marginal fidelity at cost-to-regularization ratios up to 400, demonstrating convergence across approximately eight orders of magnitude. It accurately recovers geometric transformations such as 90-degree rotations with an error of merely 0.07°, and exhibits empirical convergence rates significantly surpassing theoretical worst-case bounds.
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Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems
Current evaluations of visual language models (VLMs) for symbolic reasoning are largely confined to English-centric mathematical tasks, lacking multilingual coverage and visual grounding, thereby limiting their ability to comprehensively assess robustness in STEM domains. This work introduces the first dynamic, multilingual, visually grounded symbolic STEM benchmark, spanning five disciplines and seven languages. Leveraging executable templates, it generates semantically equivalent yet syntactically diverse problem instances, each accompanied by reference solutions and step-by-step reasoning traces. We propose worst-case accuracy and step-level F1 metrics to evaluate 17 state-of-the-art VLMs, revealing a substantial performance gap between average and worst-case scenarios. Larger models demonstrate greater multilingual robustness, while attention analyses uncover cross-lingual imbalances in the alignment between visual and textual tokens.
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Learning-Augmented Heuristics: Simple, yet Smart, Robust and Interpretable Cache Eviction
该研究提出Learning-Augmented Heuristics框架，通过学习静态启发式算法的缓存级参数来优化缓存淘汰策略，提高效率和鲁棒性。
0 citationsRead paper
New optimal linear codes over $\ZZ_4$
This study addresses the construction of improved linear codes over the quaternary ring $\mathbb{Z}_4$ by leveraging known linear codes over finite fields. The authors propose a novel methodology that integrates algebraic coding theory, computational search, and combinatorial optimization. For the first time, they fully characterize all optimal $\mathbb{Z}_4$-linear codes in the case $k_1=2, k_2=0$, and discover numerous new optimal codes when $k_1=3, k_2=0$. Several of the constructed codes attain established theoretical bounds for specific parameters, thereby substantially expanding the known landscape of optimal $\mathbb{Z}_4$-linear codes.
0 citationsRead paper
Self-dual double cyclic codes over $\mathbb{F}_q$
This study addresses the existence and explicit construction of self-dual bicyclic codes over finite fields. By viewing bicyclic codes as submodules of a polynomial ring, the authors characterize their structure via pairs of generator polynomials and establish necessary and sufficient conditions for such codes to be self-dual. For the first time, systematic construction methods are proposed for specific code lengths—including $(r,r)$, $(r,2r)$, $(2r,r)$, and cases where $\gcd(r,s)=1$—by integrating finite field theory, the algebraic structure of cyclic codes, and properties of dual codes. Concrete examples are provided to demonstrate feasibility, and the work reveals intrinsic connections between these codes and other classes of self-dual codes, thereby filling a notable gap in the existing theoretical framework.
0 citationsRead paper
anyakrakusuma: A Python Library for Entropic Schrödinger Bridges on Idealized Geometries
This work addresses the numerical instability of discrete static Schrödinger bridge problems—equivalently, entropy-regularized optimal transport—under high cost-to-regularization ratios by introducing a log-domain Sinkhorn–Knopp iteration algorithm. The proposed method achieves high-precision solutions even in extreme parameter regimes where the Gibbs kernel suffers severe underflow. A diagnostic framework based on information entropy, covariance analysis, and geometric metrics is developed to precisely characterize the structural properties of optimal couplings and intermediate distributions. The algorithm maintains marginal residuals as low as 10⁻⁹ and perfect marginal fidelity at cost-to-regularization ratios up to 400, demonstrating convergence across approximately eight orders of magnitude. It accurately recovers geometric transformations such as 90-degree rotations with an error of merely 0.07°, and exhibits empirical convergence rates significantly surpassing theoretical worst-case bounds.
0 citationsRead paper
Sci-Rho: A Multilingual Visually-Grounded Symbolic Benchmark for STEM Problems
Current evaluations of visual language models (VLMs) for symbolic reasoning are largely confined to English-centric mathematical tasks, lacking multilingual coverage and visual grounding, thereby limiting their ability to comprehensively assess robustness in STEM domains. This work introduces the first dynamic, multilingual, visually grounded symbolic STEM benchmark, spanning five disciplines and seven languages. Leveraging executable templates, it generates semantically equivalent yet syntactically diverse problem instances, each accompanied by reference solutions and step-by-step reasoning traces. We propose worst-case accuracy and step-level F1 metrics to evaluate 17 state-of-the-art VLMs, revealing a substantial performance gap between average and worst-case scenarios. Larger models demonstrate greater multilingual robustness, while attention analyses uncover cross-lingual imbalances in the alignment between visual and textual tokens.
0 citationsRead paper