AIConfPaper
← Search

Chathurangi Shyalika

6 accepted papers

2026

AssetOpsBench-Live: Privacy-Aware Online Evaluation of Multi-Agent Performance in Industrial Operations

Dhaval Patel, Nianjun Zhou, Shuxin Lin, James Rayfield, Chathurangi Shyalika, Suryanarayana Reddy Yarrabothula
AAAI 2026technical

Industrial automation increasingly relies on multi-agent AI, yet evaluation remains difficult due to task complexity and data confidentiality. We present AssetOpsBench-Live, a demo of a competition-ready platform for real-time, privacy-preserving evaluation of multi-agent AI in industrial contexts.

Cited by 0Source ↗PDF ↗Scholar ↗
2026

CausalPulse: Agentic Copilot for Root Cause Analysis in Smart Manufacturing

Chathurangi Shyalika, Utkarshani Jaimini, Cory Henson, Amit Sheth
AAAI 2026technical

Modern manufacturing systems demand real-time, trustworthy, and interpretable insights into anomalies and their underlying causes. However, conventional pipelines treat anomaly detection, causal inference, and decision-making as siloed tasks, lacking integration, explainability, and adaptability. We

Cited by 0Source ↗PDF ↗Scholar ↗
2026

CausalTrace: A Neurosymbolic Causal Analysis Agent for Smart Manufacturing

Chathurangi Shyalika, Aryaman Sharma, Fadi El Kalach, Utkarshani Jaimini, Cory Henson, Ramy Harik +1
AAAI 2026technical

Modern manufacturing environments demand not only accurate predictions but also interpretable insights to process anomalies, root causes, and potential interventions. Existing AI systems often function as isolated black boxes, lacking the seamless integration of prediction, explanation, and causal r

Cited by 0Source ↗PDF ↗Scholar ↗
2026

DETONATE – A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization

Renjith Prasad Kaippilly Mana, Abhilekh Borah, Hasnat Md Abdullah, Chathurangi Shyalika, Gurpreet Singh, Ritvik Garimella +6
AAAI 2026technical

Alignment is crucial for text-to-image (T2I) models to ensure that the generated images faithfully capture user intent while maintaining safety and fairness. Direct Preference Optimization (DPO) has emerged as a key alignment technique for large language models (LLMs), and its influence is now exten

Cited by 0Source ↗PDF ↗Scholar ↗
2026

In-Situ Eval: A Modular Framework for Custom and Real-Time RAG Benchmarking

Ritvik Garimella, Kaushik Roy, Chathurangi Shyalika, Amit Sheth
AAAI 2026technical

Retrieval-Augmented Generation (RAG) has become the standard approach for integrating domain knowledge into Large Language Models (LLMs). However, fair comparison of RAG pipelines remains difficult: data preparation is often ad hoc, subsampling methods are opaque, parameters vary across implementati

Cited by 0Source ↗PDF ↗Scholar ↗
2025

NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines

Chathurangi Shyalika, Renjith Prasad, Fadi El Kalach, Revathy Venkataramanan, Ramtin Zand, Ramy Harik +1
IJCAI 2025

In modern assembly pipelines, identifying anomalies is crucial in ensuring product quality and operational efficiency. Conventional single-modality methods fail to capture the intricate relationships required for precise anomaly prediction in complex predictive environments with abundant data and mu

Cited by 0Source ↗PDF ↗code ↗Scholar ↗