1 citations · 2 across the 15 of their papers we have counts for
15 papers
Iterative Refinement Improves Compositional Image Generation
Shantanu Jaiswal, Mihir Prabhudesai, Nikash Bhardwaj +5
Text-to-image (T2I) models have achieved remarkable progress, yet they continue to struggle with complex prompts that require simultaneously handling multiple objects, relations, a…
Chat with UAV -- Human-UAV Interaction Based on Large Language Models
Haoran Wang, Zhuohang Chen, Guang Li +2
The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on…
NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
Chia-Yu Hung, Navonil Majumder, Haoyuan Deng +7
Vision--language--action (VLA) models have recently shown promising performance on a variety of embodied tasks, yet they still fall short in reliability and generalization, especia…
10 Open Challenges Steering the Future of Vision-Language-Action Models
Soujanya Poria, Navonil Majumder, Chia-Yu Hung +7
Due to their ability of follow natural language instructions, vision-language-action (VLA) models are increasingly prevalent in the embodied AI arena, following the widespread succ…
From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
Haonan Wang, Weida Liang, Zihang Fu +8
Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this…
OffTopicEval: When Large Language Models Enter the Wrong Chat, Almost Always!
Jingdi Lei, Varun Gumma, Rishabh Bhardwaj +4
Large Language Model (LLM) safety is one of the most pressing challenges for enabling wide-scale deployment. While most studies and global discussions focus on generic harms, such…
FedCoT: Communication-Efficient Federated Reasoning Enhancement for Large Language Models
Chuan Li, Qianyi Zhao, Fengran Mo +1
Enhancing LLM reasoning in federated settings is nontrivial due to stringent computational, communication, and privacy constraints, especially in healthcare, where clinically conse…
LLMs Can't Handle Peer Pressure: Crumbling under Multi-Agent Social Interactions
Maojia Song, Tej Deep Pala, Ruiwen Zhou +5
Large language models (LLMs) are increasingly integrated into multi-agent systems (MAS), where peer interactions shape individual decisions. While prior work has mainly examined co…
SSFO: Self-Supervised Faithfulness Optimization for Retrieval-Augmented Generation
Xiaqiang Tang, Yi Wang, Keyu Hu +5
Retrieval-Augmented Generation (RAG) systems require Large Language Models (LLMs) to generate responses that are faithful to the retrieved context. However, faithfulness hallucinat…
JAM: A Tiny Flow-based Song Generator with Fine-grained Controllability and Aesthetic Alignment
Renhang Liu, Chia-Yu Hung, Navonil Majumder +5
Diffusion and flow-matching models have revolutionized automatic text-to-audio generation in recent times. These models are increasingly capable of generating high quality and fait…
Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges
Liyuan Chen, Shuoling Liu, Jiangpeng Yan +8
The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-pur…
Lessons from Training Grounded LLMs with Verifiable Rewards
Shang Hong Sim, Tej Deep Pala, Vernon Toh +5
Generating grounded and trustworthy responses remains a key challenge for large language models (LLMs). While retrieval-augmented generation (RAG) with citation-based grounding hol…
Error Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision
Tej Deep Pala, Panshul Sharma, Amir Zadeh +2
Large Language Models (LLMs) are prone to hallucination, especially during multi-hop and reasoning-intensive tasks such as mathematical problem solving. While Outcome Reward Models…
NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
Chia-Yu Hung, Qi Sun, Pengfei Hong +5
Existing Visual-Language-Action (VLA) models have shown promising performance in zero-shot scenarios, demonstrating impressive task execution and reasoning capabilities. However, a…
PromptDistill: Query-based Selective Token Retention in Intermediate Layers for Efficient Large Language Model Inference
Weisheng Jin, Maojia Song, Tej Deep Pala +4
As large language models (LLMs) tackle increasingly complex tasks and longer documents, their computational and memory costs during inference become a major bottleneck. To address…