Tootfinder

Paint samples arrived today and it was obvious which was gonna work better so I ordered a full 2.5l can.
Only to get an email back saying they can't make that colour in that varnish. Weird. That was literally the only actually blue they said they'd do last time they said they couldn't deliver what the website let me order.
After some discussion they have referred it to the Tainting Supervisor and it's apparently going to be here Monday.
Similarly for the wood, failed to arrive again today. They reckon Monday this time really for reals. Fingers crossed coz the carpenter can't do more until the wood arrives really and he's going on holiday this time next week.
So assuming the wood comes, four days to build the doors and finish off the walls of the closet and stuff.
Hopefully it can all be done in that time. Or at least the hard bits leaving the easy stuff for the less carpentery builder.

Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors
Jan Kn\v{e}\v{z}\'ik, Jon\'a\v{s} Herec, Rado Pito\v{n}\'ak
https://arxiv.org/abs/2509.20991

Fast-SEnSeI: Lightweight Sensor-Independent Cloud Masking for On-board Multispectral Sensors
Cloud segmentation is a critical preprocessing step for many Earth observation tasks, yet most models are tightly coupled to specific sensor configurations and rely on ground-based processing. In this work, we propose Fast-SEnSeI, a lightweight, sensor-independent encoder module that enables flexible, on-board cloud segmentation across multispectral sensors with varying band configurations. Building upon SEnSeI-v2, Fast-SEnSeI integrates an improved spectral descriptor, lightweight architecture…

How Quantization Shapes Bias in Large Language Models
Federico Marcuzzi, Xuefei Ning, Roy Schwartz, Iryna Gurevych
https://arxiv.org/abs/2508.18088 https://

How Quantization Shapes Bias in Large Language Models
This work presents a comprehensive evaluation of how quantization affects model bias, with particular attention to its impact on individual demographic subgroups. We focus on weight and activation quantization strategies and examine their effects across a broad range of bias types, including stereotypes, toxicity, sentiment, and fairness. We employ both probabilistic and generated text-based metrics across nine benchmarks and evaluate models varying in architecture family and reasoning ability.…

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning
Srikrishna Bangalore Raghu, Alessandro Roncone
https://arxiv.org/abs/2509.20333 https://

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning
In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds low-cost solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot state traversals, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to navigate through obstacle-rich spaces by sorting and sequencing preprocessed forward propagations;…

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications
Simone Machetti, Pasquale Davide Schiavone, Giovanni Ansaloni, Miguel Pe\'on-Quir\'os, David Atienza
https://arxiv.org/abs/2508.16959

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications
In this work, we present X-HEEP, an open-source, configurable, and extendible RISC-V platform for ultra-low-power edge applications (TinyAI). X-HEEP features the eXtendible Accelerator InterFace (XAIF), which enables seamless integration of accelerators with varying requirements along with an extensive internal configuration of cores, memory, bus, and peripherals. Moreover, it supports various development flows, including FPGA prototyping, ASIC implementation, and mixed SystemC-RTL modeling, en…

ArtiFree: Detecting and Reducing Generative Artifacts in Diffusion-based Speech Enhancement
Bhawana Chhaglani, Yang Gao, Julius Richter, Xilin Li, Syavosh Zadissa, Tarun Pruthi, Andrew Lovitt
https://arxiv.org/abs/2509.19495

ArtiFree: Detecting and Reducing Generative Artifacts in Diffusion-based Speech Enhancement
Diffusion-based speech enhancement (SE) achieves natural-sounding speech and strong generalization, yet suffers from key limitations like generative artifacts and high inference latency. In this work, we systematically study artifact prediction and reduction in diffusion-based SE. We show that variance in speech embeddings can be used to predict phonetic errors during inference. Building on these findings, we propose an ensemble inference method guided by semantic consistency across multiple di…

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
Joshua Daniel Loyal
https://arxiv.org/abs/2509.19748 https://arxiv.org/pdf/2509.19748

Generalized Bayesian Inference for Dynamic Random Dot Product Graphs
The random dot product graph is a popular model for network data with extensions that accommodate dynamic (time-varying) networks. However, two significant deficiencies exist in the dynamic random dot product graph literature: (1) no coherent Bayesian way to update one's prior beliefs about the latent positions in dynamic random dot product graphs due to their complicated constraints, and (2) no approach to forecast future networks with meaningful uncertainty quantification. This work proposes …

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Zinan Zheng, Yang Liu, Jia Li
https://arxiv.org/abs/2509.20911 https://

Mesh Interpolation Graph Network for Dynamic and Spatially Irregular Global Weather Forecasting
Graph neural networks have shown promising results in weather forecasting, which is critical for human activity such as agriculture planning and extreme weather preparation. However, most studies focus on finite and local areas for training, overlooking the influence of broader areas and limiting their ability to generalize effectively. Thus, in this work, we study global weather forecasting that is irregularly distributed and dynamically varying in practice, requiring the model to generalize t…

Two ADI compact difference methods for variable-exponent diffusion wave equations
Hao Zhang, Kexin Li, Wenlin Qiu
https://arxiv.org/abs/2509.21316 https://…

Two ADI compact difference methods for variable-exponent diffusion wave equations
In this work, we study two-dimensional diffusion-wave equations with variable exponent, modeling mechanical diffusive wave propagation in viscoelastic media with spatially varying properties. We first transform the diffusion-wave model into an equivalent form via the convolution method. Two time discretization strategies are then applied to approximate each term in the transformed equation, yielding two fully discrete schemes based on a spatial compact finite difference method. To reduce comput…

Automated Multi-Agent Workflows for RTL Design
Amulya Bhattaram, Janani Ramamoorthy, Ranit Gupta, Diana Marculescu, Dimitrios Stamoulis
https://arxiv.org/abs/2509.20182 https://…

Automated Multi-Agent Workflows for RTL Design
The rise of agentic AI workflows unlocks novel opportunities for computer systems design and optimization. However, for specialized domains such as program synthesis, the relative scarcity of HDL and proprietary EDA resources online compared to more common programming tasks introduces challenges, often necessitating task-specific fine-tuning, high inference costs, and manually-crafted agent orchestration. In this work, we present VeriMaAS, a multi-agent framework designed to automatically compo…