1 citations · 2 across the 14 of their papers we have counts for
18 papers
DOGS: Design-Space Sampling for Prompt-Driven Logo Generation
Ganyu Zou, Chen Dai, Nathan Self +5
Prompt optimization for text-to-image (T2I) generation has been pursued almost entirely as text rewriting, in which a short user brief is expanded into a longer, model-preferred to…
Compositional Concept Erasure in Text-to-Image Diffusion Models via Hierarchically Grounded Semantic Surgery
Chen Dai, Ganyu Zou, Nathan Self +5
Removing copyrighted, unsafe, or user-specified concepts from a deployed text-to-image diffusion model is now a practical requirement. Weight-editing methods can suppress fixed tar…
Graph World Models for Constrained Epidemic Policy Planning
Yiqi Su, Rashed Shelim, Lingyi Wang +2
Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of limited resources. Existing…
Dual-Mind World Models: A General Framework for Learning in Dynamic Wireless Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Despite the popularity of reinforcement learning (RL) in wireless networks, existing approaches that rely on model-free RL (MFRL) and model-based RL (MBRL) are data inefficient and…
A Theoretically-Grounded Codebook for Digital Semantic Communications
Lingyi Wang, Rashed Shelim, Walid Saad +1
The use of a learnable codebook provides an efficient way for semantic communications to map vector-based high-dimensional semantic features onto discrete symbol representations re…
Can an LLM Induce a Graph? Investigating Memory Drift and Context Length
Raquib Bin Yousuf, Aadyant Khatri, Shengzhe Xu +2
Recently proposed evaluation benchmarks aim to characterize the effective context length and the forgetting tendencies of large language models (LLMs). However, these benchmarks of…
Dynamic Strategy Adaptation in Multi-Agent Environments with Large Language Models
Shaurya Mallampati, Rashed Shelim, Walid Saad +1
Large language models (LLMs) demonstrate strong reasoning abilities across mathematical, strategic, and linguistic tasks, yet little is known about how well they reason in dynamic,…
When can isotropy help adapt LLMs' next word prediction to numerical domains?
Rashed Shelim, Shengzhe Xu, Walid Saad +1
Vector representations of contextual embeddings learned by pre-trained large language models (LLMs) are effective in various downstream tasks in numerical domains such as time seri…
The Prompt is Mightier than the Example
Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan
Numerous recent prompt optimization approaches like chain-of-thought, have been demonstrated to significantly improve the quality of content generated by large language models (LLM…
World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks
Lingyi Wang, Rashed Shelim, Walid Saad +1
Traditional reinforcement learning (RL)-based learning approaches for wireless networks rely on expensive trial-and-error mechanisms and real-time feedback based on extensive envir…
Optimizing Product Provenance Verification using Data Valuation Methods
Raquib Bin Yousuf, Hoang Anh Just, Shengzhe Xu +8
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives…
DMWM: Dual-Mind World Model with Long-Term Imagination
Lingyi Wang, Rashed Shelim, Walid Saad +1
Imagination in world models is crucial for enabling agents to learn long-horizon policy in a sample-efficient manner. Existing recurrent state-space model (RSSM)-based world models…
LLM Augmentations to support Analytical Reasoning over Multiple Documents
Raquib Bin Yousuf, Nicholas Defelice, Mandar Sharma +2
Building on their demonstrated ability to perform a variety of tasks, we investigate the application of large language models (LLMs) to enhance in-depth analytical reasoning within…
Information Guided Regularization for Fine-tuning Language Models
Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu +2
The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern language modeling. With the understanding that task adaptation in LMs is often a…
Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma +3
Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek. While LLMs…
Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds
Rashed Shelim, Walid Saad, Naren Ramakrishnan
Domain or statistical distribution shifts are a key staple of the wireless communication channel, because of the dynamics of the environment. Deep learning (DL) models for detectin…
Laying Anchors: Semantically Priming Numerals in Language Modeling
Mandar Sharma, Rutuja Murlidhar Taware, Pravesh Koirala +2
Off-the-shelf pre-trained language models have become the de facto standard in NLP pipelines for a multitude of downstream tasks. However, the inability of these models to properly…
Large Multi-Modal Models (LMMs) as Universal Foundation Models for AI-Native Wireless Systems
Shengzhe Xu, Christo Kurisummoottil Thomas, Omar Hashash +3
Large language models (LLMs) and foundation models have been recently touted as a game-changer for 6G systems. However, recent efforts on LLMs for wireless networks are limited to…