arXiv subjects

Juan Rodriguez

Publications and source records attributed to Juan Rodriguez.

8 recordsLinked to original sources

arXiv2026

Thermodynamic Criticality in FLRW Cosmology with Non-extensive Loop Quantum Gravity Entropy

We investigate thermodynamic criticality in a spatially flat Friedmann-Lema\^{\i}tre-Robertson-Walker universe with a non-extensive Loop Quantum Gravity inspired entropy on its apparent horizon. Using the full Kodama-Hayward temperature and the unified first law, we derive the modified Friedmann dynamics and construct the corresponding horizon equation of state. We see that a finite physical critical point only appears in the non-extensive branch $q>1$, while in the Bekenstein-Hawking limit $q\to1$, the critical point is continuously pushed to $v_c\to\infty$ and $T_c\to0$. Along the same spinodal curve, both the constant-pressure heat capacity and the isothermal compressibility become unbounded, and the extremum of this curve is located at the critical point. Below the critical temperature, the Gibbs free energy develops several thermodynamic branches, and the phase coexistence for different horizon states is verified by the equality of temperature, pressure, and Gibbs free energy. The critical exponents are $(\alpha_{cr},\beta_{cr},\gamma_{cr},\delta_{cr}) =(0,1/2,1,3)$, indicating that the system belongs to the standard mean-field universality class despite the non-algebraic form of the equation of state. The normalized Ruppeiner curvature diverges precisely on the spinodal curve and shows critical scaling $R_N\sim-|v-v_c|^{-4}$ on the critical isotherm, and $R_N\sim-|t|^{-2}$ on the critical isochore. Finally, the critical expansion scale is given by $H_c^2=2(\sqrt{5}-2)|\beta|$, which implies that the thermodynamic critical point occurs when the entropy deformation is of order unity. These results establish a self-consistent critical structure for the effective thermodynamic state space of the cosmological apparent horizon and retain the mean-field critical universality.

gr-qc↗

arXiv2025

Scope: Selective Cross-modal Orchestration of Visual Perception Experts

Vision-language models (VLMs) benefit from multiple vision encoders, but naively stacking them yields diminishing returns while multiplying inference costs. We propose SCOPE, a Mixture-of-Encoders (MoEnc) framework that dynamically selects one specialized encoder per image-text pair via instance-level routing, unlike token-level routing in traditional MoE. SCOPE maintains a shared encoder and a pool of routed encoders. A lightweight router uses cross-attention between text prompts and shared visual features to select the optimal encoder from the routed encoders. To train this router, we introduce dual entropy regularization with auxiliary losses to balance dataset-level load distribution with instance-level routing confidence. Remarkably, SCOPE with one shared plus one routed encoder outperforms models using all four extra encoders simultaneously, while reducing compute by 24-49\%. This demonstrates that intelligent encoder selection beats brute-force aggregation, challenging the prevailing paradigm in multi-encoder VLMs.

cs.CV↗

arXiv2025

StarFlow: Generating Structured Workflow Outputs From Sketch Images

Workflows are a fundamental component of automation in enterprise platforms, enabling the orchestration of tasks, data processing, and system integrations. Despite being widely used, building workflows can be complex, often requiring manual configuration through low-code platforms or visual programming tools. To simplify this process, we explore the use of generative foundation models, particularly vision-language models (VLMs), to automatically generate structured workflows from visual inputs. Translating hand-drawn sketches or computer-generated diagrams into executable workflows is challenging due to the ambiguity of free-form drawings, variations in diagram styles, and the difficulty of inferring execution logic from visual elements. To address this, we introduce StarFlow, a framework for generating structured workflow outputs from sketches using vision-language models. We curate a diverse dataset of workflow diagrams -- including synthetic, manually annotated, and real-world samples -- to enable robust training and evaluation. We finetune and benchmark multiple vision-language models, conducting a series of ablation studies to analyze the strengths and limitations of our approach. Our results show that finetuning significantly enhances structured workflow generation, outperforming large vision-language models on this task.

cs.CV↗

arXiv2024

Too Big to Fool: Resisting Deception in Language Models

Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by analyzing how models of varying capacities within the same family handle intentionally misleading in-context information. Our experiments demonstrate that larger models exhibit higher resilience to deceptive prompts, showcasing an advanced ability to interpret and integrate prompt information with their internal knowledge. Furthermore, we find that larger models outperform smaller ones in following legitimate instructions, indicating that their resilience is not due to disregarding in-context information. We also show that this phenomenon is likely not a result of memorization but stems from the models' ability to better leverage implicit task-relevant information from the prompt alongside their internally stored knowledge.

cs.CL↗

arXiv2024

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

Multimodal AI has the potential to significantly enhance document-understanding tasks, such as processing receipts, understanding workflows, extracting data from documents, and summarizing reports. Code generation tasks that require long-structured outputs can also be enhanced by multimodality. Despite this, their use in commercial applications is often limited due to limited access to training data and restrictive licensing, which hinders open access. To address these limitations, we introduce BigDocs-7.5M, a high-quality, open-access dataset comprising 7.5 million multimodal documents across 30 tasks. We use an efficient data curation process to ensure our data is high-quality and license-permissive. Our process emphasizes accountability, responsibility, and transparency through filtering rules, traceable metadata, and careful content analysis. Additionally, we introduce BigDocs-Bench, a benchmark suite with 10 novel tasks where we create datasets that reflect real-world use cases involving reasoning over Graphical User Interfaces (GUI) and code generation from images. Our experiments show that training with BigDocs-Bench improves average performance up to 25.8% over closed-source GPT-4o in document reasoning and structured output tasks such as Screenshot2HTML or Image2Latex generation. Finally, human evaluations showed a preference for outputs from models trained on BigDocs over GPT-4o. This suggests that BigDocs can help both academics and the open-source community utilize and improve AI tools to enhance multimodal capabilities and document reasoning. The project is hosted at https://bigdocs.github.io .

cs.LG↗

arXiv2024

Investigating the Impact of Electric Vehicle Charging Loads on CSUN's Electric Grid

This paper examines the impact of electric vehicle (EV) charging stations on the capacity of distribution feeders and transformers within the electric grid at California State University Northridge (CSUN). With the increasing adoption of both residential and commercial EVs and the rapid expansion of EV charging infrastructure, it is critical to evaluate the potential overloading effects of intensive EV charging on power distribution systems. This research assesses the impact of EV charging on the operation of CSUN's electric grid, identifying potential overload risks under projected EV adoption scenarios. Detailed simulations and analyses are conducted to quantify the extent of these impacts, focusing on various levels of EV penetration and charging patterns. The study also explores the impact of distributed generation on reducing the stress incurred by EV loads. The findings provide essential insights for utility companies, highlighting the need for strategic upgrades to distribution systems. These insights will help in developing robust strategies for both current operations and future planning to accommodate growing EV charging demands, ensuring grid stability and reliability in the face of increasing electrification of the transportation sector. The modeled CSUN electric grid can be used as a benchmark to study the impact of EV loads in dense areas on various parameters of the grid.

math.OC↗

arXiv2024

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3 as an effective, open-source evaluator to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive automated data analytics and can be accessed here: https://github.com/ServiceNow/insight-bench.

cs.AI↗

arXiv2011

A Compact Degenerate Primary-Star Progenitor of SN 2011fe

While a white dwarf is, from a theoretical perspective, the most plausible primary star in Type Ia supernova (SN Ia), many other candidates have not been formally ruled out. Shock energy deposited in the envelope of any exploding primary contributes to the early SN brightness and, since this radiation energy is degraded by expansion after the explosion, the diffusive luminosity depends on the initial primary radius. We present a new non-detection limit of the nearby SN Ia 2011fe, obtained what appears to be just 4 hours after explosion, allowing us to directly constrain the initial primary radius, R_p. Coupled with the non-detection of a quiescent X-ray counterpart and the inferred synthesized Ni mass, we show that R_p <~ 0.02 R_sun (a factor of 5 smaller than previously inferred), that the average density of the primary must be rho_p > 10,000 gm cm^{-3}, and that the effective temperature must be less than a few x 10^5 K. This rules out hydrogen burning main sequence stars and giants. Constructing the helium-burning main sequence and carbon-burning main sequence, we find such objects are also excluded. By process of elimination, we find that only degeneracy-supported compact objects---WDs and neutron stars---are viable as the primary star of SN 2011fe. With few caveats, we also restrict the companion (secondary) star radius to R_ c <~ 0.1 R_sun, excluding Roche-Lobe overflowing red giant and main-sequence companions to high significance.

astro-ph.HE↗