arXiv subjects

Akshay Gulati

Publications and source records attributed to Akshay Gulati.

4 recordsLinked to original sources

arXiv2026

Evaluating Financial Intelligence in Large Language Models: Benchmarking SuperInvesting AI with LLM Engines

Large language models are increasingly used for financial analysis and investment research, yet systematic evaluation of their financial reasoning capabilities remains limited. In this work, we introduce the AI Financial Intelligence Benchmark (AFIB), a multi-dimensional evaluation framework designed to assess financial analysis capabilities across five dimensions: factual accuracy, analytical completeness, data recency, model consistency, and failure patterns. We evaluate five AI systems: GPT, Gemini, Perplexity, Claude, and SuperInvesting, using a dataset of 95+ structured financial analysis questions derived from real-world equity research tasks. The results reveal substantial differences in performance across models. Within this benchmark setting, SuperInvesting achieves the highest aggregate performance, with an average factual accuracy score of 8.96/10 and the highest completeness score of 56.65/70, while also demonstrating the lowest hallucination rate among evaluated systems. Retrieval-oriented systems such as Perplexity perform strongly on data recency tasks due to live information access but exhibit weaker analytical synthesis and consistency. Overall, the results highlight that financial intelligence in large language models is inherently multi-dimensional, and systems that combine structured financial data access with analytical reasoning capabilities provide the most reliable performance for complex investment research workflows.

cs.AI↗

arXiv2023

Optimizing Multi-Domain Performance with Active Learning-based Improvement Strategies

Improving performance in multiple domains is a challenging task, and often requires significant amounts of data to train and test models. Active learning techniques provide a promising solution by enabling models to select the most informative samples for labeling, thus reducing the amount of labeled data required to achieve high performance. In this paper, we present an active learning-based framework for improving performance across multiple domains. Our approach consists of two stages: first, we use an initial set of labeled data to train a base model, and then we iteratively select the most informative samples for labeling to refine the model. We evaluate our approach on several multi-domain datasets, including image classification, sentiment analysis, and object recognition. Our experiments demonstrate that our approach consistently outperforms baseline methods and achieves state-of-the-art performance on several datasets. We also show that our method is highly efficient, requiring significantly fewer labeled samples than other active learning-based methods. Overall, our approach provides a practical and effective solution for improving performance across multiple domains using active learning techniques.

cs.LG↗

arXiv2017

IITMSAT Communications System : A LeanSat Design Approach

IITMSAT is a student-built nano satellite mission of Indian Institute of Technology Madras, Chennai, India. The objective is to study the precipitation of high energy electrons and protons from Van-Allen radiation belts to lower altitude of 600-900 km due to resonance interaction with low frequency EM waves. The unique communications system design of IITMSAT evolves from the challenging downlink data requirement of 1 MB per day in the UHF band posed by the mission and the satellite's payload, SPEED (Space based Proton and Electron Energy Detector). To ensure continuous downlink data stream in the short Low earth Orbit passes, a robust physical layer protocol was designed to counter time-varying aspects of a Space-Earth telecom link. For the on-board communications system, two types of design alternatives exist for each module. The first option is a custom design wherein a module is developed from scratch using discrete components.The other option is an integrated design wherein an electronics COTS module can be directly plugged into the subsystem. This module is evaluated by carrying out vibration and thermal tests. If an integrated module is low-cost and meets the design requirements, it is preferred over a custom design. In order to carry out performance tests under simulated link conditions, an RF attenuation test setup was designed that can work at extreme temperatures. Burn-In tests for 72 hours at ambient and extreme temperatures were carried out. Integrated tests indicate all IITMSAT design requirements have been met. Hence a robust communications system has been validated. The time taken for development of on-board telecom and GS was less than a year and was achieved at a low cost which agrees to a LeanSat approach.

astro-ph.IM↗

arXiv2014

A Nano-satellite Mission to Study Charged Particle Precipitation from the Van Allen Radiation Belts caused due to Seismo-Electromagnetic Emissions

In the past decade, several attempts have been made to study the effects of seismo-electromagnetic emissions - an earthquake precursor, on the ionosphere and the radiation belts. The IIT Madras nano-satellite (IITMSAT) mission is designed to make sensitive measurements of charged particle fluxes in a Low Earth Orbit to study the nature of charged particle precipitation from the Van Allen radiation belts caused due to such emissions. With the Space-based Proton Electron Energy Detector on-board a single nano-satellite, the mission will attempt to gather statistically significant data to verify possible correlations with seismo-electromagnetic emissions before major earthquakes.

astro-ph.IM↗