2 citations · 2 across the 9 of their papers we have counts for
11 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…
Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan
Alexander Chulzhanov, Soeren Eberhardt, Arjun Mukherjee
Neural machine translation for digitally low-resource Indigenous languages is often hindered by extreme data scarcity, prompting reliance on extractive web-scraping. To ensure data…
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…
Investigating the Fundamental Limit: A Feasibility Study of Hybrid-Neural Archival
Marcus Armstrong, ZiWei Qiu, Huy Q. Vo +1
Large Language Models (LLMs) possess a theoretical capability to model information density far beyond the limits of classical statistical methods (e.g., Lempel-Ziv). However, utili…
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…
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…