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20242026
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cs.LG2026

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…

cs.LG2026

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…

cs.LG2025

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…

cs.LG2024

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…

cs.LG2024

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…