Extractive summarization (ES) aims to generate a concise summary by selecting a subset of sentences from a document while maximizing relevance and minimizing redundancy. Although modern ES systems achieve high accuracy using powerful neural models, their deployment typically relies on CPU or GPU infrastructures that are energy-intensive and poorly suited for real-time inference in resource-constrained environments. In this work, we explore the feasibility of implementing McDonald-style extractive summarization on a low-power CMOS coupled oscillator-based Ising machine (COBI) that […]

Ver mais

Deep neural networks constructed from linear maps and positively homogeneous nonlinearities (e.g., ReLU) possess a fundamental gauge symmetry: the network function is invariant to node-wise diagonal rescalings. However, standard gradient descent is not equivariant to this symmetry, causing optimization trajectories to depend heavily on arbitrary parameterizations. Prior work has proposed rescaling-invariant optimization schemes for positively homogeneous networks (e.g., path-based or path-space updates). Our contribution is complementary: we formulate the invariance requirement at the level of the backward adjoint/optimization […]

Ver mais

arXiv:2601.17123v1 Announce Type: new Abstract: We introduce and explore a new multimodal input representation for vision-language models: acoustic field video. Unlike conventional video (RGB with stereo/mono audio), our video stream provides a spatially grounded visualization of sound intensity across a scene, offering a new and powerful dimension of perceptual understanding. Our real-time pipeline uses low-cost beamforming microphone arrays that are already common in smart speakers and increasingly present in robotics and XR headsets, yet this sensing capability remains […]

Ver mais

The most common question prospective startup founders ask is how to get ideas for startups. The second most common question is if you have any ideas for their startup. But giving founders an idea almost always doesn’t work. Having ideas is among the most important qualities for a startup founder to have—you will need to generate lots of new ideas in the course of running a startup. YC once tried an experiment of funding seemingly good founders with […]

Ver mais

arXiv:2602.10680v1 Announce Type: new Abstract: Many real-world datasets contain hidden structure that cannot be detected by simple linear correlations between input features. For example, latent factors may influence the data in a coordinated way, even though their effect is invisible to covariance-based methods such as PCA. In practice, nonlinear neural networks often succeed in extracting such hidden structure in unsupervised and self-supervised learning. However, constructing a minimal high-dimensional model where this advantage can be rigorously analyzed has remained […]

Ver mais

arXiv:2602.06061v1 Announce Type: new Abstract: Relays are pivotal in military communication networks, expanding coverage and ensuring reliable connectivity in challenging operational environments. While traditional terrestrial relays (TR) are constrained by fixed locations and vulnerability to physical obstructions, unmanned aerial vehicle (UAV)-mounted aerial relays (AR) offer a dynamic and flexible alternative by operating above obstacles and adapting to changing battlefield conditions. This paper provides a comprehensive survey of AR systems in military communications, presenting a detailed comparison between AR […]

Ver mais

Large Language Models (LLMs) have demonstrated remarkable abilities to solve problems requiring multiple reasoning steps, yet the internal mechanisms enabling such capabilities remain elusive. Unlike existing surveys that primarily focus on engineering methods to enhance performance, this survey provides a comprehensive overview of the mechanisms underlying LLM multi-step reasoning. We organize the survey around a conceptual framework comprising seven interconnected research questions, from how LLMs execute implicit multi-hop reasoning within hidden activations to how verbalized explicit reasoning remodels […]

Ver mais

arXiv:2601.02444v1 Announce Type: new Abstract: The rapid advancement of speech synthesis technologies, including text-to-speech (TTS) and voice conversion (VC), has intensified security and privacy concerns related to voice cloning. Recent defenses attempt to prevent unauthorized cloning by embedding protective perturbations into speech to obscure speaker identity while maintaining intelligibility. However, adversaries can apply advanced purification techniques to remove these perturbations, recover authentic acoustic characteristics, and regenerate cloneable voices. Despite the growing realism of such attacks, the robustness of […]

Ver mais

Serverless computing is often marketed as a way to eliminate infrastructure management and accelerate software delivery. While this promise is largely true, it also creates a dangerous misconception that removing servers removes security risks. In reality, serverless architectures shift security responsibilities rather than eliminate them. Short-lived functions, event-driven execution models, and managed runtime environments introduce new attack surfaces that many organizations often overlook. Misconfigured permissions, untrusted event data, and reuse of runtime context can silently expose sensitive systems. […]

Ver mais

arXiv:2601.02372v1 Announce Type: new Abstract: News recommendation systems rely on automated sentiment analysis to personalise content and enhance user engagement. Conventional approaches often struggle with ambiguity, lexicon inconsistencies, and limited contextual understanding, particularly in multi-source news environments. Existing models typically treat sentiment as a secondary feature, reducing their ability to adapt to users’ affective preferences. To address these limitations, this study develops an adaptive, sentiment-aware news recommendation framework by integrating hybrid sentiment analysis with reinforcement learning. Using the […]

Ver mais