Tootfinder

RepoSummary: Feature-Oriented Summarization and Documentation Generation for Code Repositories
Yifeng Zhu, Xianlin Zhao, Xutian Li, Yanzhen Zou, Haizhuo Yuan, Yue Wang, Bing Xie
https://arxiv.org/abs/2510.11039

RepoSummary: Feature-Oriented Summarization and Documentation Generation for Code Repositories
Repository summarization is a crucial research question in development and maintenance for software engineering. Existing repository summarization techniques primarily focus on summarizing code according to the directory tree, which is insufficient for tracing high-level features to the methods that collaboratively implement them. To address these limitations, we propose RepoSummary, a feature-oriented code repository summarization approach that simultaneously generates repository documentation…

Robust Visual Teach-and-Repeat Navigation with Flexible Topo-metric Graph Map Representation
Jikai Wang, Yunqi Cheng, Kezhi Wang, Zonghai Chen
https://arxiv.org/abs/2510.09089 h…

Robust Visual Teach-and-Repeat Navigation with Flexible Topo-metric Graph Map Representation
Visual Teach-and-Repeat Navigation is a direct solution for mobile robot to be deployed in unknown environments. However, robust trajectory repeat navigation still remains challenged due to environmental changing and dynamic objects. In this paper, we propose a novel visual teach-and-repeat navigation system, which consists of a flexible map representation, robust map matching and a map-less local navigation module. During the teaching process, the recorded keyframes are formulated as a topo-me…

Adversarial Robustness in One-Stage Learning-to-Defer
Yannis Montreuil, Letian Yu, Axel Carlier, Lai Xing Ng, Wei Tsang Ooi
https://arxiv.org/abs/2510.10988 https://

Adversarial Robustness in One-Stage Learning-to-Defer
Learning-to-Defer (L2D) enables hybrid decision-making by routing inputs either to a predictor or to external experts. While promising, L2D is highly vulnerable to adversarial perturbations, which can not only flip predictions but also manipulate deferral decisions. Prior robustness analyses focus solely on two-stage settings, leaving open the end-to-end (one-stage) case where predictor and allocation are trained jointly. We introduce the first framework for adversarial robustness in one-stage …

From 8 years ago.
The limits of the best of the reformist approaches, exhausted capitalism and ecosystem crisis, societal collapse and a localist, ecosocialist resurgence. England's northern postindustrial towns as the backdrop.
After peak capitalism: the livelihood challenge – revised version | Uncommontater

After peak capitalism: the livelihood challenge – revised version
Here is the revised version of my working paper, “After peak capitalism: the livelihood challenge”.  I’m grateful to those who have offered encouragement and constructively critic…

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors
Yihong Liu, Raoyuan Zhao, Lena Altinger, Hinrich Sch\"utze, Michael A. Hedderich
https://arxiv.org/abs/2510.09536

Evaluating Robustness of Large Language Models Against Multilingual Typographical Errors
Large language models (LLMs) are increasingly deployed in multilingual, real-world applications with user inputs -- naturally introducing typographical errors (typos). Yet most benchmarks assume clean input, leaving the robustness of LLMs to typos across languages largely underexplored. To address this gap, we introduce MulTypo, a multilingual typo generation algorithm that simulates human-like errors based on language-specific keyboard layouts and typing behavior. We evaluate 18 open-source LL…

Robust Least Squares Problems with Binary Uncertain Data
Yang Zhou, Xiaojun Chen
https://arxiv.org/abs/2510.11519 https://arxiv.org/pdf/2510.11519

Robust Least Squares Problems with Binary Uncertain Data
We propose a Binary Robust Least Squares (BRLS) model that encompasses key robust least squares formulations, such as those involving uncertain binary labels and adversarial noise constrained within a hypercube. We show that the geometric structure of the noise propagation matrix, particularly whether its columns form acute or obtuse angles, implies the supermodularity or submodularity of the inner maximization problem. This structural property enables us to integrate powerful combinatorial opt…

Camera-trap study catches nectar-feeding birds breaking into flowers when their beaks don't fit
Trait matching affects the probability of nectar robbing in plant-pollinator networks
https://doi.org/10.1002/oik.11552

Vector Graph-Based Repository Understanding for Issue-Driven File Retrieval
Kostiantyn Bevziuk, Andrii Fatula, Svetozar Lashin Yaroslav Opanasenko, Anna Tukhtarova, Ashok Jallepalli Pradeepkumar Sharma, Hritvik Shrivastava
https://arxiv.org/abs/2510.08876

Vector Graph-Based Repository Understanding for Issue-Driven File Retrieval
We present a repository decomposition system that converts large software repositories into a vectorized knowledge graph which mirrors project architectural and semantic structure, capturing semantic relationships and allowing a significant level of automatization of further repository development. The graph encodes syntactic relations such as containment, implementation, references, calls, and inheritance, and augments nodes with LLM-derived summaries and vector embeddings. A hybrid retrieval …

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park
https://arxiv.org/abs/2510.10260 https://arx…

Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping problems under ambiguity. In this framework, an agent chooses a stopping rule motivated by two objectives: robust decision-making under ambiguity and learning about the unknown environment. Here, ambiguity refers to considering multiple probability measures dominated by a reference measure, reflecting the agent's awareness that the reference measure representing her learned belief about the env…

Testing and Enhancing Multi-Agent Systems for Robust Code Generation
Zongyi Lyu, Songqiang Chen, Zhenlan Ji, Liwen Wang, Shuai Wang, Daoyuan Wu, Wenxuan Wang, Shing-Chi Cheung
https://arxiv.org/abs/2510.10460

Testing and Enhancing Multi-Agent Systems for Robust Code Generation
Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks by decomposing complex coding tasks across specialized agents with different roles. Despite their prosperous development and adoption, their robustness remains pressingly under-explored, raising critical concerns for real-world deployment. This paper presents the first comprehensive study examining the robustness of MASs for code generatio…