27 citations · 91 across the 55 of their papers we have counts for
59 papers
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval
Zhisheng Qi, Haoyu Han, Jiaxi Li +9
Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on unconstrained retrieval or predefine…
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…
CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing
Huu Huy Nguyen, Chien Van Nguyen, Franck Dernoncourt +4
The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a severe computational bottleneck. Traditional sparse attention methods mit…
Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction
Utkarsh Sahu, Zhisheng Qi, Li Zhu +4
Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction enables key downstream applic…
CAVEWOMAN: How Large Language Models Behave Under Linguistic Input and Output Compression
Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt
"Talk short. Drop grammar. Save token." This caveman style is widely promoted as a way to cut inference cost, but whether it actually saves anything depends on which channel (the u…
TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents
Vijitha Mittapalli, Shreyaa Jayant Dani, Satya Srujana Pilli +7
Autonomous LLM agents can pursue hidden malicious objectives through sequences of individually benign actions, making sabotage difficult to detect using standard trajectory-level m…
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…
Multimodal Music Recommendation System using LLMs
Srikar Prabhas Kandagatla, Sreehitha R. Narayana, Chandana Magapu +6
Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoustic content. Prior work has exp…
FASH-iCNN: Making Editorial Fashion Identity Inspectable Through Multimodal CNN Probing
Morayo Danielle Adeyemi, Ryan A. Rossi, Franck Dernoncourt
Fashion AI systems routinely encode the aesthetic logic of specific houses, editors, and historical moments without disclosing it. We present FASH-iCNN, a multimodal system trained…
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…
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…
Anticipatory Planning for Multimodal AI Agents
Yongyuan Liang, Shijie Zhou, Yu Gu +6
Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reaso…
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…
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…
Agent Banana: High-Fidelity Image Editing with Agentic Thinking and Tooling
Ruijie Ye, Jiayi Zhang, Zhuoxin Liu +10
We study instruction-based image editing under professional workflows and identify three persistent challenges: (i) editors often over-edit, modifying content beyond the user's int…
Human-Aligned MLLM Judges for Fine-Grained Image Editing Evaluation: A Benchmark, Framework, and Analysis
Runzhou Liu, Hailey Weingord, Sejal Mittal +18
Evaluating image editing models remains challenging due to the coarse granularity and limited interpretability of traditional metrics, which often fail to capture aspects important…
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,…
Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
Zhisheng Qi, Utkarsh Sahu, Li Ma +9
Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory manageme…
Segment Length Matters: A Study of Segment Lengths on Audio Fingerprinting Performance
Ziling Gong, Yunyan Ouyang, Iram Kamdar +5
Audio fingerprinting provides an identifiable representation of acoustic signals, which can be later used for identification and retrieval systems. To obtain a discriminative repre…
Charts Are Not Images: On the Challenges of Scientific Chart Editing
Shawn Li, Ryan Rossi, Sungchul Kim +5
Generative models, such as diffusion and autoregressive approaches, have demonstrated impressive capabilities in editing natural images. However, applying these tools to scientific…
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…
Iterative Critique-Refine Framework for Enhancing LLM Personalization
Durga Prasad Maram, Dhruvin Gandhi, Zonghai Yao +5
Personalized text generation requires models not only to produce coherent text but also to align with a target user's style, tone, and topical focus. Existing retrieval-augmented a…
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.…
Steering MoE LLMs via Expert (De)Activation
Mohsen Fayyaz, Ali Modarressi, Hanieh Deilamsalehy +5
Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a fra…