2 citations · 3 across the 15 of their papers we have counts for
15 papers
Studying Without a Syllabus: Task-Agnostic Environment Preprocessing
Vinay Samuel, Varun Ursekar, Vijay S. Kalmath +3
Before an LLM agent tackles tasks in a new environment, it can inspect available corpora and tools and construct reusable resources such as indices, scripts, or procedural guidance…
READY or Not: Reliable Enterprise Agent Deployment
Veronica Chatrath, Bryan Zhu, Jingxuan Fan +15
An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete realistic professional work, w…
CliniCARE-Bench: Clinical Calibrated Audit of Medical Reasoning in EHR
Veronica Chatrath, Bryan Zhu, George Pu +16
Large language models perform strongly on medical knowledge benchmarks, but reliable clinical deployment requires agents to conduct defensible investigations over heterogeneous, lo…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Varun Ursekar, Apaar Shanker, Yash Maurya +4
As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory,…
Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents
Akshay Manglik, Apaar Shanker, Kaustubh Deshpande +6
Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterate. This process misses pattern…
MIDST Challenge at SaTML 2025: Membership Inference over Diffusion-models-based Synthetic Tabular data
Masoumeh Shafieinejad, Xi He, Mahshid Alinoori +6
Synthetic data is often perceived as a silver-bullet solution to data anonymization and privacy-preserving data publishing. Drawn from generative models like diffusion models, synt…
VeRO: A Harness for Agents to Optimize Agents
Varun Ursekar, Apaar Shanker, Veronica Chatrath +2
An important emerging application of coding agents is agent harness optimization: the iterative improvement of a target agent by editing and evaluating its code. Despite its releva…
FedRAG: A Framework for Fine-Tuning Retrieval-Augmented Generation Systems
Val Andrei Fajardo, David B. Emerson, Amandeep Singh +5
Retrieval-augmented generation (RAG) systems have been shown to be effective in addressing many of the drawbacks of relying solely on the parametric memory of large language models…
ViLBias: Detecting and Reasoning about Bias in Multimodal Content
Shaina Raza, Caesar Saleh, Azib Farooq +11
Detecting bias in multimodal news requires models that reason over text--image pairs, not just classify text. In response, we present ViLBias, a VQA-style benchmark and framework f…
Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths?
Veronica Chatrath, Marcelo Lotif, Shaina Raza
Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability an…
MBIAS: Mitigating Bias in Large Language Models While Retaining Context
Shaina Raza, Ananya Raval, Veronica Chatrath
The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. T…
FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
Shaina Raza, Tahniat Khan, Veronica Chatrath +3
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information.…
FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
Tahniat Khan, Mizanur Rahman, Veronica Chatrath +2
In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of informa…
She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models
Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza
As the use of large language models (LLMs) increases within society, as does the risk of their misuse. Appropriate safeguards must be in place to ensure LLM outputs uphold the ethi…
Unlocking Bias Detection: Leveraging Transformer-Based Models for Content Analysis
Shaina Raza, Oluwanifemi Bamgbose, Veronica Chatrath +3
Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challeng…