5 citations · 6 across the 10 of their papers we have counts for
10 papers
PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
Manasa Bharadwaj, Yolanda Liu, InJung Yang +5
Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insufficiently characterized. To addre…
MAPLE: Modality-Aware Post-training and Learning Ecosystem
Nikhil Verma, Minjung Kim, JooYoung Yoo +5
Multimodal language models now integrate text, audio, and video for unified reasoning. Yet existing RL post-training pipelines treat all input signals as equally relevant, ignoring…
SMaRT: Select, Mix, and ReinvenT -- A Strategy Fusion Framework for LLM-Driven Reasoning and Planning
Nikhil Verma, Manasa Bharadwaj, Wonjun Jang +4
Large Language Models (LLMs) have redefined complex task automation with exceptional generalization capabilities. Despite these advancements, state-of-the-art methods rely on singl…
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems
Jisoo Lee, Raeyoung Chang, Dongwook Kwon +2
Multi-agent systems built on language models have shown strong performance on collaborative reasoning tasks. However, existing evaluations focus only on the correctness of the fina…
OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections
Manasa Bharadwaj, Nikhil Verma, Kevin Ferreira
Efforts to improve Large Language Model (LLM) agent performance on complex tasks have largely focused on fine-tuning and iterative self-correction. However, these approaches often…
Cross-Attention Speculative Decoding
Wei Zhong, Manasa Bharadwaj, Yixiao Wang +2
Speculative decoding (SD) is a widely adopted approach for accelerating inference in large language models (LLMs), particularly when the draft and target models are well aligned. H…
The Hidden Space of Safety: Understanding Preference-Tuned LLMs in Multilingual context
Nikhil Verma, Manasa Bharadwaj
Alignment tuning has enabled large language models to excel in reasoning, instruction-following, and minimizing harmful generations. However, despite their widespread deployment, t…
GPT-DETOX: An In-Context Learning-Based Paraphraser for Text Detoxification
Ali Pesaranghader, Nikhil Verma, Manasa Bharadwaj
Harmful and offensive communication or content is detrimental to social bonding and the mental state of users on social media platforms. Text detoxification is a crucial task in na…
Image Reconstruction using Enhanced Vision Transformer
Nikhil Verma, Deepkamal Kaur, Lydia Chau
Removing noise from images is a challenging and fundamental problem in the field of computer vision. Images captured by modern cameras are inevitably degraded by noise which limits…
Diffusion idea exploration for art generation
Nikhil Verma
Cross-Modal learning tasks have picked up pace in recent times. With plethora of applications in diverse areas, generation of novel content using multiple modalities of data has re…