Tootfinder

A look at Model Context Protocol and how it went from a passion project made by Anthropic employees to an industry standard shared through the Linux Foundation (Hayden Field/The Verge)
https://www.theverge.com/ai-artificial-int

AI companies want a new internet — and they think they’ve found the key
Anthropic is donating MCP to the Linux Foundation, to be governed under the Agentic AI Foundation, with other members including OpenAI, Google, and Block.

Altermagnetism and Superconductivity: A Short Historical Review
Zhao Liu, Hui Hu, Xia-Ji Liu
https://arxiv.org/abs/2510.09170 https://arxiv.org/pdf/2510.09…

Altermagnetism and Superconductivity: A Short Historical Review
This article explores the deep interconnections among three seemingly unrelated concepts in condensed matter physics: electronic liquid crystal phases, multipole expansions, and altermagnetism. At the heart of these phenomena lies a shared foundation: spin-momentum locking in the nonrelativistic regime. Originally proposed in the context of electronic liquid crystal phases, spin-momentum locking was later elegantly incorporated into the formalism of multipole expansions. This framework can be f…

When you’re talking to someone with shared context, phrases like “The Bay Area” or “The Twin Cities” might be a totally reasonable way to refer to a place.
When you’re posting something on *the internet* for all the world to see, do you have the slightest idea how little that narrows it down?
Specifics people! Come on!

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Sangmin Bae, Bilge Acun, Haroun Habeeb, Seungyeon Kim, Chien-Yu Lin, Liang Luo, Junjie Wang, Carole-Jean Wu
https://arxiv.org/abs/2510.04800

Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Recent progress in large language models demonstrates that hybrid architectures--combining self-attention mechanisms with structured state space models like Mamba--can achieve a compelling balance between modeling quality and computational efficiency, particularly for long-context tasks. While these hybrid models show promising performance, systematic comparisons of hybridization strategies and analyses on the key factors behind their effectiveness have not been clearly shared to the community.…

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
Pinhao Song, Yurui Du, Ophelie Saussus, Sofie De Schrijver, Irene Caprara, Peter Janssen, Renaud Detry
https://arxiv.org/abs/2509.19954

Robot Trajectron V2: A Probabilistic Shared Control Framework for Navigation
We propose a probabilistic shared-control solution for navigation, called Robot Trajectron V2 (RT-V2), that enables accurate intent prediction and safe, effective assistance in human-robot interaction. RT-V2 jointly models a user's long-term behavioral patterns and their noisy, low-dimensional control signals by combining a prior intent model with a posterior update that accounts for real-time user input and environmental context. The prior captures the multimodal and history-dependent nature o…

Leveraging Prior Knowledge of Diffusion Model for Person Search
Giyeol Kim, Sooyoung Yang, Jihyong Oh, Myungjoo Kang, Chanho Eom
https://arxiv.org/abs/2510.01841 https://…

Leveraging Prior Knowledge of Diffusion Model for Person Search
Person search aims to jointly perform person detection and re-identification by localizing and identifying a query person within a gallery of uncropped scene images. Existing methods predominantly utilize ImageNet pre-trained backbones, which may be suboptimal for capturing the complex spatial context and fine-grained identity cues necessary for person search. Moreover, they rely on a shared backbone feature for both person detection and re-identification, leading to suboptimal features due to …

Long-context Reference-based MT Quality Estimation
Sami Ul Haq, Chinonso Cynthia Osuji, Sheila Castilho, Brian Davis
https://arxiv.org/abs/2509.13980 https://

Long-context Reference-based MT Quality Estimation
In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level Error Span Annotation (ESA) scores using augmented long-context data. To construct long-context training data, we concatenate in-domain, human-annotated sentences and compute a weighted average of their scores. We integrate multiple human judgment datasets…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Nathan He, Cody Chen
https://arxiv.org/abs/2509.12287 https://arxi…

Enhancing Radiographic Disease Detection with MetaCheX, a Context-Aware Multimodal Model
Existing deep learning models for chest radiology often neglect patient metadata, limiting diagnostic accuracy and fairness. To bridge this gap, we introduce MetaCheX, a novel multimodal framework that integrates chest X-ray images with structured patient metadata to replicate clinical decision-making. Our approach combines a convolutional neural network (CNN) backbone with metadata processed by a multilayer perceptron through a shared classifier. Evaluated on the CheXpert Plus dataset, MetaChe…

Demystifying Progressive Web Application Permission Systems
Mengxiao Wang, Guofei Gu
https://arxiv.org/abs/2509.13563 https://arxiv.org/pdf/2509.13563

Demystifying Progressive Web Application Permission Systems
Progressive Web Applications (PWAs) blend the advantages of web and native apps, offering features like offline access, push notifications, and installability. Beyond these, modern PWAs are increasingly granted system-level capabilities such as auto-start on login and shared context with native applications. However, their permission management remains poorly defined and inconsistently implemented across platforms and browsers. To investigate these gaps, we developed Permissioner, a cross-pla…

It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge
Lukas Ellinger, Georg Groh
https://arxiv.org/abs/2509.16107 https://

It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge
Ambiguous words or underspecified references require interlocutors to resolve them, often by relying on shared context and commonsense knowledge. Therefore, we systematically investigate whether Large Language Models (LLMs) can leverage commonsense to resolve referential ambiguity in multi-turn conversations and analyze their behavior when ambiguity persists. Further, we study how requests for simplified language affect this capacity. Using a novel multilingual evaluation dataset, we test DeepS…