3 citations · 7 across the 23 of their papers we have counts for
8 papers · 1 filter
Memoria: A Scalable Agentic Memory Framework for Personalized Conversational AI
Samarth Sarin, Lovepreet Singh, Bhaskarjit Sarmah +1
Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical c…
A Unified AI System For Data Quality Control and DataOps Management in Regulated Environments
Devender Saini, Bhavika Jain, Nitish Ujjwal +3
In regulated domains such as finance, the integrity and governance of data pipelines are critical - yet existing systems treat data quality control (QC) as an isolated preprocessin…
Interpretable Model-Aware Counterfactual Explanations for Random Forest
Joshua S. Harvey, Guanchao Feng, Sai Anusha Meesala +2
Despite their enormous predictive power, machine learning models are often unsuitable for applications in regulated industries such as finance, due to their limited capacity to pro…
STRAPSim: A Portfolio Similarity Metric for ETF Alignment and Portfolio Trades
Mingshu Li, Dhruv Desai, Jerinsh Jeyapaulraj +4
Accurately measuring portfolio similarity is critical for a wide range of financial applications, including Exchange-traded Fund (ETF) recommendation, portfolio trading, and risk a…
AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions
Tianjiao Zhao, Jingrao Lyu, Stokes Jones +3
The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human…
Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis
Dimitris Vamvourellis, Dhagash Mehta
We investigate the effectiveness of large language models (LLMs), including reasoning-based and non-reasoning models, in performing zero-shot financial sentiment analysis. Using th…
Explainable Unsupervised Anomaly Detection with Random Forest
Joshua S. Harvey, Joshua Rosaler, Mingshu Li +2
We describe the use of an unsupervised Random Forest for similarity learning and improved unsupervised anomaly detection. By training a Random Forest to discriminate between real d…
Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning
Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan +5
We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formali…