Tootfinder

This https://arxiv.org/abs/2504.09030 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Authoritarian Recursions: How Fiction, History, and AI Reinforce Control in Education, Warfare, and Discourse
This article develops the concept of authoritarian recursion to theorize how artificial intelligence (AI) systems consolidate institutional control across education, military operations, and digital discourse. Rather than treating these domains in isolation, it identifies a shared recursive architecture in which algorithmic systems mediate judgment, obscure accountability, and reshape the conditions of moral and epistemic agency. Grounded in critical discourse analysis and sociotechnical ethi…

This https://arxiv.org/abs/2503.02768 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Denotational Semantics for Probabilistic and Concurrent Programs
We develop a denotational model for probabilistic and concurrent imperative programs, a class of programs with standard control flow via conditionals and while-loops, as well as probabilistic actions and parallel composition. Whereas semantics for concurrent or randomized programs in isolation is well studied, their combination has not been thoroughly explored and presents unique challenges. The crux of the problem is that interactions between control flow, probabilistic actions, and concurrent…

This https://arxiv.org/abs/2502.15936 has been replaced.
link: https://scholar.google.com/scholar?q=a

Space-O-RAN: Enabling Intelligent, Open, and Interoperable Non Terrestrial Networks in 6G
Satellite networks are rapidly evolving, yet most \glspl{ntn} remain isolated from terrestrial orchestration frameworks. Their control architectures are typically monolithic and static, limiting their adaptability to dynamic traffic, topology changes, and mission requirements. These constraints lead to inefficient spectrum use and underutilized network capacity. Although \gls{ai} promises automation, its deployment in orbit is limited by computing, energy, and connectivity limitations. This p…

This https://arxiv.org/abs/2502.02959 has been replaced.
initial toot: https://mastoxiv.page/@…

Observation of slow relaxation due to Hilbert space fragmentation in strongly interacting Bose-Hubbard chains
While isolated quantum systems generally thermalize after long-time evolution, there are several exceptions defying thermalization. A notable mechanism of such nonergodicity is the Hilbert space fragmentation (HSF), where the Hamiltonian matrix splits into an exponentially large number of sectors due to the presence of nontrivial conserved quantities. Using ultracold gases, here we experimentally investigate the one-dimensional Bose-Hubbard system with neither disorder nor tilt potential, which…

This https://arxiv.org/abs/2505.12538 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_eco…

On long-duration storage, weather uncertainty and limited foresight
Long-duration energy storage (LDES) is a key component for fully renewable, sector-coupled energy systems based on wind and solar. While capacity expansion planning has begun to take into account interannual weather variability, it often ignores weather uncertainty and limited foresight in capacity and operational decisions. We build a stochastic capacity expansion model for fully decarbonized energy systems with LDES in Europe accounting for weather uncertainty - isolating the effect of limite…

This https://arxiv.org/abs/2502.00698 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MM-IQ: Benchmarking Human-Like Abstraction and Reasoning in Multimodal Models
IQ testing has served as a foundational methodology for evaluating human cognitive capabilities, deliberately decoupling assessment from linguistic background, language proficiency, or domain-specific knowledge to isolate core competencies in abstraction and reasoning. Yet, artificial intelligence research currently lacks systematic benchmarks to quantify these critical cognitive capabilities in multimodal systems. To address this crucial gap, we propose MM-IQ, a comprehensive evaluation framew…

This https://arxiv.org/abs/2505.23847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or viola…

This https://arxiv.org/abs/2505.00812 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization
Recent studies indicate that deep neural networks degrade in generalization performance under noisy supervision. Existing methods focus on isolating clean subsets or correcting noisy labels, facing limitations such as high computational costs, heavy hyperparameter tuning process, and coarse-grained optimization. To address these challenges, we propose a novel two-stage noisy learning framework that enables instance-level optimization through a dynamically weighted loss function, avoiding hyperp…

This https://arxiv.org/abs/2505.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize ho…

This https://arxiv.org/abs/2505.02515 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization
Traditional domain generalization approaches predominantly focus on leveraging target domain-aware features while overlooking the critical role of source domain-specific characteristics, particularly in federated settings with inherent data isolation. To address this gap, we propose the Federated Source Domain Awareness Framework (FedSDAF), the first method to systematically exploit source domain-aware features for enhanced federated domain generalization (FedDG). The FedSDAF framework consists…