by jsendak | Oct 10, 2025 | Cosmology & Computing
Black holes are perhaps one of the most mysterious and enigmatic objects in the universe. These massive celestial bodies are known for their immense gravitational pull, which is so strong that not even light can escape from them. At the center of a black hole lies a singularity, a point of infinite density where the laws of physics as we know them break down. Understanding the nature of black hole singularities has been a major challenge for scientists for decades.
The concept of a singularity was first proposed by physicist Albert Einstein in his theory of general relativity. According to general relativity, when a massive star collapses under its own gravity, it forms a singularity at its center. This singularity is a point of infinite density and zero volume, where the laws of physics as we know them cease to apply. The gravitational pull at the singularity is so strong that it warps space and time around it, creating a region of spacetime known as the event horizon, beyond which nothing can escape.
One of the key features of black hole singularities is their enigmatic nature. According to general relativity, the singularity is a point of infinite density, where all the mass of the black hole is concentrated. However, this concept of infinite density is a mathematical idealization that may not accurately reflect the true nature of the singularity. In reality, the singularity may be a region of extremely high density, but not necessarily infinite.
Another enigmatic aspect of black hole singularities is the concept of spacetime curvature. According to general relativity, the gravitational pull of a black hole is so strong that it warps spacetime around it, creating a region of spacetime where time slows down and space is curved. This curvature of spacetime near the singularity is so extreme that it leads to the formation of a point of infinite density, known as the singularity.
Despite the enigmatic nature of black hole singularities, scientists have made significant progress in understanding them. One of the key developments in this field was the discovery of black hole thermodynamics, which showed that black holes have a temperature and entropy, similar to ordinary objects. This led to the development of the concept of black hole evaporation, where black holes can lose mass and energy over time through the emission of Hawking radiation.
Another important development in the study of black hole singularities is the concept of quantum gravity. Quantum gravity is a theory that seeks to unify the principles of quantum mechanics and general relativity, in order to describe the behavior of matter and energy at the smallest scales. By incorporating quantum effects into the theory of black hole singularities, scientists hope to gain a better understanding of the nature of these enigmatic objects.
In conclusion, black hole singularities are one of the most enigmatic and mysterious objects in the universe. Their infinite density and extreme curvature of spacetime make them a fascinating subject of study for scientists. By incorporating quantum effects and exploring the concept of black hole thermodynamics, researchers hope to gain a better understanding of the nature of black hole singularities and unlock the secrets of these enigmatic objects.
by jsendak | Oct 10, 2025 | Cosmology & Computing
arXiv:2510.08004v1 Announce Type: cross Abstract: This paper presents our solution to the Multimodal Personality-aware Depression Detection (MPDD) challenge at ACM MM 2025. We propose a multimodal depression detection model in the Elderly that incorporates personality characteristics. We introduce a multi-feature fusion approach based on a co-attention mechanism to effectively integrate LLDs, MFCCs, and Wav2Vec features in the audio modality. For the video modality, we combine representations extracted from OpenFace, ResNet, and DenseNet to construct a comprehensive visual feature set. Recognizing the critical role of personality in depression detection, we design an interaction module that captures the relationships between personality traits and multimodal features. Experimental results from the MPDD Elderly Depression Detection track demonstrate that our method significantly enhances performance, providing valuable insights for future research in multimodal depression detection among elderly populations.
by jsendak | Oct 10, 2025 | Cosmology & Computing
arXiv:2510.07423v1 Announce Type: new Abstract: Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through exploration and plan evolution. ProSEA features a hierarchical architecture in which a Manager Agent orchestrates domain-specialized Expert Agents, decomposes tasks, and adaptively replans based on structured feedback from failed attempts. Unlike prior systems, ProSEA agents report not only success or failure but also detailed reasons for failure and newly discovered constraints, enabling dynamic plan refinement informed by exploratory traces. The framework operates autonomously but supports seamless integration with human collaborators when needed. Experiments on the challenging FinanceBench benchmark demonstrate that ProSEA, even without human feedback, outperforms state-of-the-art baselines and achieves robust performance across reasoning-heavy tasks. These results underscore ProSEA’s potential as a foundation for more transparent, adaptive, and human-aligned AI agents.
by jsendak | Oct 10, 2025 | Cosmology & Computing
arXiv:2510.07940v1 Announce Type: cross Abstract: Video Foundation Models (VFMs) exhibit remarkable visual generation performance, but struggle in compositional scenarios (e.g., motion, numeracy, and spatial relation). In this work, we introduce Test-Time Optimization and Memorization (TTOM), a training-free framework that aligns VFM outputs with spatiotemporal layouts during inference for better text-image alignment. Rather than direct intervention to latents or attention per-sample in existing work, we integrate and optimize new parameters guided by a general layout-attention objective. Furthermore, we formulate video generation within a streaming setting, and maintain historical optimization contexts with a parametric memory mechanism that supports flexible operations, such as insert, read, update, and delete. Notably, we found that TTOM disentangles compositional world knowledge, showing powerful transferability and generalization. Experimental results on the T2V-CompBench and Vbench benchmarks establish TTOM as an effective, practical, scalable, and efficient framework to achieve cross-modal alignment for compositional video generation on the fly.
by jsendak | Oct 9, 2025 | Cosmology & Computing
The universe has always been a source of fascination and wonder for humanity. From ancient civilizations gazing up at the stars to modern scientists studying the cosmos with advanced technology, the mysteries of the universe continue to captivate our imagination. Cosmology, the study of the origin, evolution, and eventual fate of the universe, has made significant strides in recent years, providing new insights into the nature of our vast and complex cosmos.
One of the most groundbreaking discoveries in cosmology in recent decades is the theory of cosmic inflation. Proposed in the 1980s by physicist Alan Guth, cosmic inflation suggests that the universe underwent a rapid period of exponential expansion in the first fraction of a second after the Big Bang. This theory helps to explain the uniformity and flatness of the universe on large scales, as well as the existence of structures such as galaxies and galaxy clusters. While cosmic inflation has not been definitively proven, it is supported by a growing body of observational evidence, such as the cosmic microwave background radiation and the distribution of galaxies in the universe.
Another major development in cosmology is the discovery of dark matter and dark energy. Dark matter is a mysterious form of matter that does not emit, absorb, or reflect light, making it invisible to telescopes. Despite its elusive nature, dark matter is thought to make up about 27% of the total mass-energy content of the universe, playing a crucial role in the formation and evolution of galaxies. Dark energy, on the other hand, is a mysterious force that is causing the expansion of the universe to accelerate. While the nature of dark energy remains unknown, its existence has been inferred from observations of distant supernovae and the cosmic microwave background.
In addition to these major discoveries, cosmologists are also studying the cosmic web, a vast network of filaments and voids that make up the large-scale structure of the universe. By mapping the distribution of galaxies and dark matter in the universe, scientists are gaining a better understanding of how cosmic structures form and evolve over time. This research is helping to shed light on the processes that drive the growth of galaxies and the formation of galaxy clusters.
As technology continues to advance, cosmologists are able to probe deeper into the mysteries of the universe than ever before. The development of powerful telescopes, such as the Hubble Space Telescope and the upcoming James Webb Space Telescope, allows scientists to observe the universe in unprecedented detail. In addition, large-scale surveys such as the Sloan Digital Sky Survey and the European Space Agency’s Gaia mission are providing vast amounts of data that are helping to unravel the mysteries of the cosmos.
While there is still much we do not know about the universe, the field of cosmology is making rapid progress in uncovering its secrets. By combining theoretical models with observational data, scientists are piecing together a more complete picture of the origin, evolution, and ultimate fate of the universe. As we continue to explore the mysteries of the cosmos, we are sure to uncover even more surprises and revelations that will deepen our understanding of the vast and awe-inspiring universe in which we live.
by jsendak | Oct 9, 2025 | Cosmology & Computing
arXiv:2510.06756v1 Announce Type: new Abstract: We introduce a tool for rigorous and automated verification of large language model (LLM)- based policies in memoryless sequential decision-making tasks. Given a Markov decision process (MDP) representing the sequential decision-making task, an LLM policy, and a safety requirement expressed as a PCTL formula, our approach incrementally constructs only the reachable portion of the MDP guided by the LLM’s chosen actions. Each state is encoded as a natural language prompt, the LLM’s response is parsed into an action, and reachable successor states by the policy are expanded. The resulting formal model is checked with Storm to determine whether the policy satisfies the specified safety property. In experiments on standard grid world benchmarks, we show that open source LLMs accessed via Ollama can be verified when deterministically seeded, but generally underperform deep reinforcement learning baselines. Our tool natively integrates with Ollama and supports PRISM-specified tasks, enabling continuous benchmarking in user-specified sequential decision-making tasks and laying a practical foundation for formally verifying increasingly capable LLMs.
