technocracy

arXiv:2603.16877v1 Announce Type: new Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a Retrieval-Augmented Generation (RAG) system designed to answer questions about S&P 500 financial reports and evaluates the impact of neural reranking on system performance. Our pipeline employs hybrid search combining full-text and semantic retrieval, followed by an optional reranking stage using a cross-encoder model. We conduct systematic evaluation using the FinDER benchmark dataset, comprising […]

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We study the training and performance of physics-informed learning for initial and boundary value problems (IBVP) with physics-informed neural networks (PINNs) from a statistical learning perspective. Specifically, we restrict ourselves to parameterizations with hard initial and boundary condition constraints and reformulate the problem of estimating PINN parameters as a statistical learning problem. From this perspective, the physics penalty on the IBVP residuals can be better understood not as a regularizing term bus as an infinite source of indirect […]

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arXiv:2503.10773v2 Announce Type: replace Abstract: As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders’ value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually rational and incentive-compatible, ensuring truthful bidding while […]

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Table of Links Abstract and 1. Introduction 2. Financial Market Model and Worst-Case Optimization Problem 3. Solution to the Post-Crash Problem 4. Solution to the Pre-Crash Problem 5. A BSDE Characterization of Indifferences Strategies 6. The Markovian Case 7. Numerical Experiments Acknowledgments and References Appendix A. Proofs from Section 3 Appendix B. Proofs of BASDE Results from Section 5 Appendix C. Proofs of (CIR) Results from Section 6 5. A BSDE Characterization of Indifference Strategies In the previous […]

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arXiv:2601.04270v1 Announce Type: new Abstract: Deep learning optimization exhibits structure that is not captured by worst-case gradient bounds. Empirically, gradients along training trajectories are often temporally predictable and evolve within a low-dimensional subspace. In this work we formalize this observation through a measurable framework for predictable gradient manifolds. We introduce two computable quantities: a prediction-based path length that measures how well gradients can be forecast from past information, and a predictable rank that quantifies the intrinsic temporal dimension […]

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arXiv:2601.19000v1 Announce Type: new Abstract: This work presents (i) a framework for certifying small-signal frequency stability of a power system with line dynamics and heterogeneous bus dynamics, (ii) a novel reduced-order model of damper windings in synchronous machines, and (iii) a proportional-derivative (PD) damper winding emulation control for voltage-source converters (VSCs). Damper windings have long been understood to improve the frequency synchronization between machines. However, the dynamics of the damper windings are complex, making them difficult to analyze […]

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Photo by Barrett Ward on Unsplash This project was presented as the final project to obtain the Founderz AI Business School + Microsoft certificate in AI and Innovation 2025. Summary In contemporary logistics, punctuality has emerged as a fundamental pillar for customer loyalty and business competitiveness. However, a significant part of supply chains continue to operate under reactive paradigms, limiting themselves to managing delays only when they have already materialized. This project addresses this inefficiency by developing a comprehensive […]

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arXiv:2603.23521v1 Announce Type: new Abstract: Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image understanding through large-scale pretraining on interleaved image-text datasets. However, most Vision-Language Models (VLMs) are trained primarily on English datasets, leading to inadequate representation of Indian languages. To address this gap, we introduce the Chitrakshara dataset series, covering 11 Indian languages sourced from Common Crawl. It comprises (1) Chitrakshara-IL, a large-scale interleaved pretraining […]

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