activity
20192026
most citedLLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding

27 citations · 153 across the 63 of their papers we have counts for

collaborators

69 papers

cs.CV2026

SliderEdit++: Improved Slider Trajectories in Instruction-Based Image Editing

Arman Zarei, Sayan Nag, Bryan Wang +5

Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing. However, existing approaches fai…

cs.CV2026

Personalized Image Generation with Reasoning and Reflection

Bo Ni, Ngoc N. Tran, Qinwen Ge +10

Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user is. In practice, however, a use…

cs.CL2026

DuplexSpeechBench-Document Grounding: Benchmarking Document Grounding and Hallucinations in Voice Agents

Puneet Mathur, Nedim Lipka, Zeyu Jin +1

Voice agents enable low-latency, natural interaction, yet their ability to faithfully ground responses in external documents remains underexplored. We introduce DuplexSpeechBench-D…

cs.CV2026

Joint and Cross-Modal Video-Audio Generation and Editing: A Unified Formulation and Design Taxonomy

Abhinav Sharma, Sai Karthik Navuluru, Wang Wei +21

Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, o…

cs.CV2026

A Glance Is All You Need: Single-Pass Fine-Grained Image Captioning with SimLoss

Suryaansh Jain, Rahasya Barkur, Vishal G +8

An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level captions, yet routinely miss t…

cs.AI2026

Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents

Harshitha Kolukuluru, Reshma Ashok, Kirat Arora +7

Long-horizon research agents solve open-ended tasks through iterative retrieval, aggregation, and synthesis, but context grows rapidly while the marginal value of additional eviden…

cs.CL2026

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

Dang Quang Thien Tran, Quang V. Dang, Vinamra Tyagi +7

As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and model safety. While unimodal…

cs.IR2026

RL-Index: Reinforcement Learning for Retrieval Index Reasoning

Yongjia Lei, Nedim Lipka, Zhisheng Qi +7

Retrieving external knowledge is crucial for real-world tasks but remains difficult when queries and relevant knowledge are linked by implicit reasoning (e.g., shared theorems or c…

cs.AI2026

Sparse Personalized Text Generation with Multi-Trajectory Reasoning

Bo Ni, Haowei Fu, Qinwen Ge +10

As Large Language Models (LLMs) advance, personalization has become a key mechanism for tailoring outputs to individual user needs. However, most existing methods rely heavily on d…

cs.CL2026

A Survey on LLM-based Conversational User Simulation

Bo Ni, Leyao Wang, Yu Wang +27

User simulation has long played a vital role in computer science due to its potential to support a wide range of applications. Language, as the primary medium of human communicatio…

cs.AI2026

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

Vipula Rawte, Ryan Rossi, Franck Dernoncourt +1

Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual inaccuracies and hallucinations…

cs.IR2026

Test-Time Strategies for More Efficient and Accurate Agentic RAG

Brian Zhang, Deepti Guntur, Zhiyang Zuo +7

Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iterative…

cs.CL2026

Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents

Peijun Qing, Puneet Mathur, Nedim Lipka +5

General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. In contrast, instructio…

cs.CV2026

InfinityStory: Unlimited Video Generation with World Consistency and Character-Aware Shot Transitions

Mohamed Elmoghany, Liangbing Zhao, Xiaoqian Shen +27

Generating long-form storytelling videos with consistent visual narratives remains a significant challenge in video synthesis. We present a novel framework, dataset, and a model th…

cs.CL2026

Blind to the Human Touch: Overlap Bias in LLM-Based Summary Evaluation

Jiangnan Fang, Cheng-Tse Liu, Hanieh Deilamsalehy +5

Large language model (LLM) judges have often been used alongside traditional, algorithm-based metrics for tasks like summarization because they better capture semantic information,…

cs.CL2025

Decomposition-Enhanced Training for Post-Hoc Attributions In Language Models

Sriram Balasubramanian, Samyadeep Basu, Koustava Goswami +6

Large language models (LLMs) are increasingly used for long-document question answering, where reliable attribution to sources is critical for trust. Existing post-hoc attribution…

cs.CL2025

Structured Uncertainty guided Clarification for LLM Agents

Manan Suri, Puneet Mathur, Nedim Lipka +3

LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches ope…

cs.HC2025★ 1 cited

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces

Reuben A. Luera, Ryan Rossi, Franck Dernoncourt +12

In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration…

cs.DC2025

Efficient Tree-Structured Deep Research with Adaptive Resource Allocation

Lunyiu Nie, Nedim Lipka, Ryan A. Rossi +1

Deep research agents, which synthesize information across diverse sources, are significantly constrained by the sequential nature of reasoning. This bottleneck results in high late…

cs.LG2025

Knowledge Homophily in Large Language Models

Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6

Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…

cs.CV2025

A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality

Mohamed Elmoghany, Ryan Rossi, Seunghyun Yoon +26

Despite the significant progress that has been made in video generative models, existing state-of-the-art methods can only produce videos lasting 5-16 seconds, often labeled "long-…

cs.CL2025

Follow the Flow: Fine-grained Flowchart Attribution with Neurosymbolic Agents

Manan Suri, Puneet Mathur, Nedim Lipka +4

Flowcharts are a critical tool for visualizing decision-making processes. However, their non-linear structure and complex visual-textual relationships make it challenging to interp…

cs.CL2025

LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles

Ho Yin 'Sam' Ng, Ting-Yao Hsu, Aashish Anantha Ramakrishnan +8

Figure captions are crucial for helping readers understand and remember a figure's key message. Many models have been developed to generate these captions, helping authors compose…

cs.CL2025

Quantitative LLM Judges

Aishwarya Sahoo, Jeevana Kruthi Karnuthala, Tushar Parmanand Budhwani +9

LLM-as-a-judge is a framework where a large language model (LLM) evaluates the output of another LLM. While LLMs excel at producing qualitative textual evaluations, they often stru…