1 citations · 1 across the 12 of their papers we have counts for
12 papers
Trace Integrity for LLM Data Agents: Auditing Structured-Data Computation Beyond Answer Accuracy in Real-World Systems
Srimonti Dutta, Akshata Kishore Moharir
Large language model (LLM) data agents answer structured-data questions by generating SQL, invoking tools, executing queries, and summarizing results. In operational settings, reli…
AgentRiskBOM: A Risk-Scoping Security Bill of Materials for Agentic AI Systems
Srimonti Dutta, Akshata Kishore Moharir
Agentic AI systems retrieve private context, invoke tools, write files, call external services, coordinate with other agents, and may act without human approval. Existing bill of m…
Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges
Srimonti Dutta, Akshata Kishore Moharir
LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators. These pipelines typically assume that judg…
Designing Explainable Conversational Agentic Systems for Guaraní Speakers
Samantha Adorno, Akshata Kishore Moharir, Ratna Kandala
Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, unders…
The Nonverbal Gap: Toward Affective Computer Vision for Safer and More Equitable Online Dating
Ratna Kandala, Niva Manchanda, Akshata Kishore Moharir
Online dating has become the dominant way romantic relationships begin, yet current platforms strip the nonverbal cues: gaze, facial expression, body posture, response timing, that…
EchoGuard: An Agentic Framework with Knowledge-Graph Memory for Detecting Manipulative Communication in Longitudinal Dialogue
Ratna Kandala, Niva Manchanda, Akshata Kishore Moharir +1
Manipulative communication, such as gaslighting, guilt-tripping, and emotional coercion, is often difficult for individuals to recognize. Existing agentic AI systems lack the struc…
"What if she doesn't feel the same?" What Happens When We Ask AI for Relationship Advice
Niva Manchanda, Akshata Kishore Moharir, Ratna Kandala
Large Language Models (LLMs) are increasingly being used to provide support and advice in personal domains such as romantic relationships, yet little is known about user perception…
Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data
Ashish Kattamuri, Arpita Vats, Harshwardhan Fartale +3
Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics acro…
From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening
Ratna Kandala, Akshata Kishore Moharir, Divya Arvinda Nayak
Explainable Artificial Intelligence (XAI) has been presented as the critical component for unlocking the potential of machine learning in mental health screening (MHS). However, a…
Cross-Lingual Mental Health Ontologies for Indian Languages: Bridging Patient Expression and Clinical Understanding through Explainable AI and Human-in-the-Loop Validation
Ananth Kandala, Ratna Kandala, Akshata Kishore Moharir +2
Mental health communication in India is linguistically fragmented, culturally diverse, and often underrepresented in clinical NLP. Current health ontologies and mental health resou…
Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis
Harshwardhan Fartale, Ashish Kattamuri, Rahul Raja +3
Transformer-based language models excel at both recall (retrieving memorized facts) and reasoning (performing multi-step inference), but whether these abilities rely on distinct in…
Time Series Viewmakers for Robust Disruption Prediction
Dhruva Chayapathy, Tavis Siebert, Lucas Spangher +3
Machine Learning guided data augmentation may support the development of technologies in the physical sciences, such as nuclear fusion tokamaks. Here we endeavor to study the probl…