48 citations · 58 across the 16 of their papers we have counts for
18 papers
Correctness Is a Direction: Geometric Answer Selection in Language Models
Marcus Armstrong, Navid Ayoobi, Pradham Mummaleti +2
Answer correctness is encoded as a recoverable geometric direction in the hidden states of language models. We show that the mean displacement from incorrect to correct answer repr…
Decodable But Not Detachable: Training Data Granularity Determines Parametric Modularity in Large Language Models
Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee
Do large language models contain domain-specific parametric shells: concentrated, causally necessary neuron populations whose removal selectively degrades a target domain while spa…
Dead Weights, Live Signals: Feedforward Graphs of Frozen Language Models
Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee
We present a feedforward graph architecture in which heterogeneous frozen large language models serve as computational nodes, communicating through a shared continuous latent space…
Thinking in Different Spaces: Domain-Specific Latent Geometry Survives Cross-Architecture Translation
Marcus Armstrong, Navid Ayoobi, Arjun Mukherjee
We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct…
Say Anything but This: When Tokenizer Betrays Reasoning in LLMs
Navid Ayoobi, Marcus I Armstrong, Arjun Mukherjee
Large language models (LLMs) reason over discrete token ID sequences, yet modern subword tokenizers routinely produce non-unique encodings: multiple token ID sequences can detokeni…
Exposing Pink Slime Journalism: Linguistic Signatures and Robust Detection Against LLM-Generated Threats
Sadat Shahriar, Navid Ayoobi, Arjun Mukherjee +2
The local news landscape, a vital source of reliable information for 28 million Americans, faces a growing threat from Pink Slime Journalism, a low-quality, auto-generated articles…
The Erosion of LLM Signatures: Can We Still Distinguish Human and LLM-Generated Scientific Ideas After Iterative Paraphrasing?
Sadat Shahriar, Navid Ayoobi, Arjun Mukherjee
With the increasing reliance on LLMs as research agents, distinguishing between LLM and human-generated ideas has become crucial for understanding the cognitive nuances of LLMs' re…
Beyond Easy Wins: A Text Hardness-Aware Benchmark for LLM-generated Text Detection
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
We present a novel evaluation paradigm for AI text detectors that prioritizes real-world and equitable assessment. Current approaches predominantly report conventional metrics like…
ChatGPT or A Silent Everywhere Helper: A Survey of Large Language Models
Azim Akhtarshenas, Afshin Dini, Navid Ayoobi
Large Language Models (LLMs) have revo lutionized natural language processing Natural Language Processing (NLP), with Chat Generative Pre-trained Transformer (ChatGPT) standing out…
ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination
Navid Ayoobi, Lily Knab, Wen Cheng +5
While large language models (LLMs) exhibit significant utility across various domains, they simultaneously are susceptible to exploitation for unethical purposes, including academi…
Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
LLMs offer valuable capabilities, yet they can be utilized by malicious users to disseminate deceptive information and generate fake news. The growing prevalence of LLMs poses diff…
CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector in MIMO-enabled UAV Networks
Azim Akhtarshenas, Navid Ayoobi, David Lopez-Perez +2
Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenario…
Measuring Impacts of Poisoning on Model Parameters and Neuron Activations: A Case Study of Poisoning CodeBERT
Aftab Hussain, Md Rafiqul Islam Rabin, Navid Ayoobi +1
Large language models (LLMs) have revolutionized software development practices, yet concerns about their safety have arisen, particularly regarding hidden backdoors, aka trojans.…
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications
Azim Akhtarshenas, Mohammad Ali Vahedifar, Navid Ayoobi +4
Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated lear…
The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
Navid Ayoobi, Sadat Shahriar, Arjun Mukherjee
In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration an…
Unsupervised Motor Imagery Saliency Detection Based on Self-Attention Mechanism
Navid Ayoobi, Elnaz Banan Sadeghian
Detecting the salient parts of motor-imagery electroencephalogram (MI-EEG) signals can enhance the performance of the brain-computer interface (BCI) system and reduce the computati…
A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder
Navid Ayoobi, Elnaz Banan Sadeghian
A calibration procedure is required in motor imagery-based brain-computer interface (MI-BCI) to tune the system for new users. This procedure is time-consuming and prevents naïve u…
A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm
Navid Ayoobi, Elnaz Banan Sadeghian
In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown. To detect th…