Tootfinder

Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning
Alvaro Belmonte-Baeza, Miguel Cazorla, Gabriel J. Garc\'ia, Carlos J. P\'erez-Del-Pulgar, Jorge Pomares
https://arxiv.org/abs/2510.12684

Autonomous Legged Mobile Manipulation for Lunar Surface Operations via Constrained Reinforcement Learning
Robotics plays a pivotal role in planetary science and exploration, where autonomous and reliable systems are crucial due to the risks and challenges inherent to space environments. The establishment of permanent lunar bases demands robotic platforms capable of navigating and manipulating in the harsh lunar terrain. While wheeled rovers have been the mainstay for planetary exploration, their limitations in unstructured and steep terrains motivate the adoption of legged robots, which offer super…

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
Norbert Tihanyi, Bilel Cherif, Richard A. Dubniczky, Mohamed Amine Ferrag, Tam\'as Bisztray
https://arxiv.org/abs/2510.10493

The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution
In this paper, we present the first large-scale study exploring whether JavaScript code generated by Large Language Models (LLMs) can reveal which model produced it, enabling reliable authorship attribution and model fingerprinting. With the rapid rise of AI-generated code, attribution is playing a critical role in detecting vulnerabilities, flagging malicious content, and ensuring accountability. While AI-vs-human detection usually treats AI as a single category we show that individual LLMs le…

a beautifully weird mckenna-ish mystery: chinese mushrooms that reliably bring the little people, but don't seem to contain any psychoactive elements (& yet obviously do) https://nhmu.utah.edu/articles/experts-explore-new-mushroom-which-cau…

Experts Explore New Mushroom Which Causes Fairytale-Like Hallucinations
Between traditional folklore and modern biology, the wild forest floor and the sterile scientific laboratory, lies the story of the lilliputian mushroom.

A posteriori analysis for nonlinear convection-diffusion systems
Andreas Dedner, Jan Giesselmann, Kiwoong Kwon, Tristan Pryer
https://arxiv.org/abs/2510.09449 https://

A posteriori analysis for nonlinear convection-diffusion systems
This work provides reliable a posteriori error estimates for Runge-Kutta discontinuous Galerkin approximations of nonlinear convection-diffusion systems. The classes of systems we study are quite general with a focus on convection-dominated and degenerate parabolic problems. Our a posteriori error bounds are valid for a family of discontinuous Galerkin spatial discretizations and various temporal discretizations that include explicit and implicit-explicit time-stepping schemes, popular tools fo…

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation
Wentian Zhu, Zhen Xiang, Wei Niu, Le Guan
https://arxiv.org/abs/2510.10271 https://

MetaBreak: Jailbreaking Online LLM Services via Special Token Manipulation
Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs). Serving as metadata of training data, these tokens play a crucial role in instructing LLMs to generate coherent and context-aware responses. We demonstrate that special tokens can be exploited to construct four attack primitives, with which malicious users can reliably bypass the internal safety ali…

Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop
Oz Gitelson, Satya Prakash Nayak, Ritam Raha, Anne-Kathrin Schmuck
https://arxiv.org/abs/2510.12662

Maximal Adaptation, Minimal Guidance: Permissive Reactive Robot Task Planning with Humans in the Loop
We present a novel framework for human-robot \emph{logical} interaction that enables robots to reliably satisfy (infinite horizon) temporal logic tasks while effectively collaborating with humans who pursue independent and unknown tasks. The framework combines two key capabilities: (i) \emph{maximal adaptation} enables the robot to adjust its strategy \emph{online} to exploit human behavior for cooperation whenever possible, and (ii) \emph{minimal tunable feedback} enables the robot to request …

Differential Analysis of Pseudo Haptic Feedback: Novel Comparative Study of Visual and Auditory Cue Integration for Psychophysical Evaluation
Nishant Gautam, Somya Sharma, Peter Corcoran, Kaspar Althoefer
https://arxiv.org/abs/2510.09570

Differential Analysis of Pseudo Haptic Feedback: Novel Comparative Study of Visual and Auditory Cue Integration for Psychophysical Evaluation
Pseudo-haptics exploit carefully crafted visual or auditory cues to trick the brain into "feeling" forces that are never physically applied, offering a low-cost alternative to traditional haptic hardware. Here, we present a comparative psychophysical study that quantifies how visual and auditory stimuli combine to evoke pseudo-haptic pressure sensations on a commodity tablet. Using a Unity-based Rollball game, participants (n = 4) guided a virtual ball across three textured terrains while their…

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo, Merim Dzaferagic, John D. Kelleher
https://arxiv.org/abs/2510.08303

Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven by multi-vendor deployments, hardware upgrades, and evolving service requirements. To address this growing need for flexible learning in non-stationary environments, this vision paper highlights Adaptive Random Forests (ARFs) as a reliable solution for dynamic feature adaptation in communication network scenarios. We…

Exploration-Exploitation-Evaluation (EEE): A Framework for Metaheuristic Algorithms in Combinatorial Optimization
Ethan Davis
https://arxiv.org/abs/2510.05027 https://

Exploration-Exploitation-Evaluation (EEE): A Framework for Metaheuristic Algorithms in Combinatorial Optimization
We introduce a framework for applying metaheuristic algorithms, such as ant colony optimization (ACO), to combinatorial optimization problems (COPs) like the traveling salesman problem (TSP). The framework consists of three sequential stages: broad exploration of the parameter space, exploitation of top-performing parameters, and uncertainty quantification (UQ) to assess the reliability of results. As a case study, we apply ACO to the TSPLIB berlin52 dataset, which has a known optimal tour leng…

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Shiyuan Yin, Chenjia Bai, Zihao Zhang, Junwei Jin, Xinxin Zhang, Chi Zhang, Xuelong Li
https://arxiv.org/abs/2510.08044

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation
Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistem…