Tootfinder

High-Performance Wavelength Division Multiplexers Enabled by Co-Optimized Inverse Design
Sydney Mason, Geun Ho Ahn, Jakob Grzesik, Sungjun Eun, Jelena Vu\v{c}kovi\'c
https://arxiv.org/abs/2509.07233

High-Performance Wavelength Division Multiplexers Enabled by Co-Optimized Inverse Design
Wavelength division multiplexers are fundamental to the functioning and performance of integrated photonic circuits, with applications ranging from optical interconnects to sensing and quantum technologies. Current solutions are limited by trade-offs between channel spacing, crosstalk, insertion loss, and device footprint. Here, we develop a novel design approach that co-optimizes inverse-designed wavelength division multiplexers and distributed Bragg gratings to achieve ultra-low crosstalk wit…

The Consequences of Rubin Observatory Time-Domain Survey Design and Host-Galaxy Contamination on the Identification of Binary Supermassive Black Holes
Megan C. Davis, Jonathan R. Trump, Maria Charisi, Jessie C. Runnoe, W. N. Brandt, Kaylee E. Grace, London E. Willson
https://arxiv.org/abs/2508.05742

The Consequences of Rubin Observatory Time-Domain Survey Design and Host-Galaxy Contamination on the Identification of Binary Supermassive Black Holes
Binary supermassive black holes (SMBHs) are consequences of galaxy mergers and dominate the low-frequency gravitational wave background. Finding binary SMBHs in existing time-domain observations has proven difficult, as their periodic, electromagnetic signals can be confused with the natural variability of single quasars. In this work, we investigate the effects of host-galaxy contamination and survey design (cadence and duration) on the detectability of binary SMBHs with the upcoming Rubin Obs…

Airy: Reading Robot Intent through Height and Sky
Baoyang Chen, Xian Xu, Huamin Qu
https://arxiv.org/abs/2510.08381 https://arxiv.org/pdf/2510.08381…

Airy: Reading Robot Intent through Height and Sky
As industrial robots move into shared human spaces, their opaque decision making threatens safety, trust, and public oversight. This artwork, Airy, asks whether complex multi agent AI can become intuitively understandable by staging a competition between two reinforcement trained robot arms that snap a bedsheet skyward. Building on three design principles, competition as a clear metric (who lifts higher), embodied familiarity (audiences recognize fabric snapping), and sensor to sense mapping (r…

A Study on Messaging Trade-offs in Data Streaming for Scientific Workflows
Anjus George, Michael J. Brim, Christopher Zimmer, Tyler J. Skluzacek, A. J. Ruckman, Gustav R. Jansen, Sarp Oral
https://arxiv.org/abs/2509.07199

A Study on Messaging Trade-offs in Data Streaming for Scientific Workflows
Memory-to-memory data streaming is essential for modern scientific workflows that require near real-time data analysis, experimental steering, and informed decision-making during experiment execution. It eliminates the latency bottlenecks associated with file-based transfers to parallel storage, enabling rapid data movement between experimental facilities and HPC systems. These tightly coupled experimental-HPC workflows demand low latency, high throughput, and reliable data delivery to support …

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ClauseLens: Clause-Grounded, CVaR-Constrained Reinforcement Learning for Trustworthy Reinsurance Pricing
Stella C. Dong, James R. Finlay
https://arxiv.org/abs/2510.08429 https:/…

ClauseLens: Clause-Grounded, CVaR-Constrained Reinforcement Learning for Trustworthy Reinsurance Pricing
Reinsurance treaty pricing must satisfy stringent regulatory standards, yet current quoting practices remain opaque and difficult to audit. We introduce ClauseLens, a clause-grounded reinforcement learning framework that produces transparent, regulation-compliant, and risk-aware treaty quotes. ClauseLens models the quoting task as a Risk-Aware Constrained Markov Decision Process (RA-CMDP). Statutory and policy clauses are retrieved from legal and underwriting corpora, embedded into the agent'…

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Yahya Hassanzadeh-Nazarabadi, Sanaz Taheri-Boshrooyeh
https://arxiv.org/abs/2510.05376 https://

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Zero-knowledge Ethereum Virtual Machines (zkEVMs) must reconcile a fundamental contradiction: the Ethereum Virtual Machine was designed for transparent sequential execution, while zero-knowledge proofs require algebraic circuit representations. This survey provides the first systematic analysis of how existing major production zkEVM implementations resolve this tension through distinct constraint engineering strategies. We develop a comparative framework that maps the design space across three …

Position: Intelligent Coding Systems Should Write Programs with Justifications
Xiangzhe Xu, Shiwei Feng, Zian Su, Chengpeng Wang, Xiangyu Zhang
https://arxiv.org/abs/2508.06017 …

Position: Intelligent Coding Systems Should Write Programs with Justifications
Intelligent coding systems are transforming software development by enabling users to specify code behavior in natural language. However, the opaque decision-making of AI-driven coders raises trust and usability concerns, particularly for non-expert users who cannot inspect low-level implementations. We argue that these systems should not only generate code but also produce clear, consistent justifications that bridge model reasoning and user understanding. To this end, we identify two critical…

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
Fr\'ed\'eric Zheng, Yassir Jedra, Alexandre Proutiere
https://arxiv.org/abs/2510.08117 https://

Near-optimal Rank Adaptive Inference of High Dimensional Matrices
We address the problem of estimating a high-dimensional matrix from linear measurements, with a focus on designing optimal rank-adaptive algorithms. These algorithms infer the matrix by estimating its singular values and the corresponding singular vectors up to an effective rank, adaptively determined based on the data. We establish instance-specific lower bounds for the sample complexity of such algorithms, uncovering fundamental trade-offs in selecting the effective rank: balancing the precis…

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Brandon Baker, Elliott Brossard, Chenwei Xie, Zihao Ye, Deen Liu, Yijun Xie, Arthur Zwiegincew, Nitya Kumar Sharma, Gaurav Jain, Eugene Retunsky, Mike Halcrow, Derek Denny-Brown, Istvan Cseri, Tyler Akidau, Yuxiong He
https://arxiv.org/abs/2508.05904…

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Snowflake revolutionized data analytics with an elastic architecture that decouples compute and storage, enabling scalable solutions supporting data architectures like data lake, data warehouse, data lakehouse, and data mesh. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI and ML workloads using Python and other programming languages. This paper outlines Snowpark's design obj…