4 citations · 8 across the 17 of their papers we have counts for
18 papers
Tropical Reinforcement Learning
Arip Asadulaev, Aladin Djuhera, Karim Salta +3
Reinforcement learning for large language models typically maximizes expected return, adding up the probabilities of all successful trajectories. However, the classical sum formula…
Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts
Massi-Nissa Abboud, Aladin Djuhera, Elena Cabrio +1
Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are d…
Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Aladin Djuhera, Farhan Ahmed, Vlad C. Andrei +4
AI-native 6G visions increasingly invoke wireless foundation models, large multimodal models, and wireless world models as the natural endpoint of AI-native networking, drawing an…
MambaCSP: Hybrid-Attention State Space Models for Hardware-Efficient Channel State Prediction
Aladin Djuhera, Haris Gacanin, Holger Boche
Recent works have demonstrated that attention-based transformer and large language model (LLM) architectures can achieve strong channel state prediction (CSP) performance by captur…
TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents
Aladin Djuhera, Swanand Kadhe, Swanand Ravindra Kadhe +4
Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn interactions across tasks. However,…
Joint Partitioning and Placement of Foundation Models for Real-Time Edge AI
Aladin Djuhera, Fernando Koch, Alecio Binotto
Inference over large-scale foundation models within heterogeneous edge environments necessitates a fundamentally reconfigurable orchestration substrate. Static partitioning of mode…
When Data is the Algorithm: A Systematic Study and Curation of Preference Optimization Datasets
Aladin Djuhera, Farhan Ahmed, Swanand Ravindra Kadhe +3
Aligning large language models (LLMs) is a central objective of post-training, often achieved through reward modeling and reinforcement learning methods. Among these, direct prefer…
Fixing It in Post: A Comparative Study of LLM Post-Training Data Quality and Model Performance
Aladin Djuhera, Swanand Ravindra Kadhe, Syed Zawad +3
Recent work on large language models (LLMs) has increasingly focused on post-training and alignment with datasets curated to enhance instruction following, world knowledge, and spe…
"Don't Do That!": Guiding Embodied Systems through Large Language Model-based Constraint Generation
Amin Seffo, Aladin Djuhera, Masataro Asai +1
Recent advancements in large language models (LLMs) have spurred interest in robotic navigation that incorporates complex spatial, mathematical, and conditional constraints from na…
SafeCOMM: A Study on Safety Degradation in Fine-Tuned Telecom Large Language Models
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed +4
Fine-tuning large language models (LLMs) on telecom datasets is a common practice to adapt general-purpose models to the telecom domain. However, little attention has been paid to…
Intelligent Orchestration of Distributed Large Foundation Model Inference at the Edge
Fernando Koch, Aladin Djuhera, Alecio Binotto
Large Foundation Models (LFMs), including multi-modal and generative models, promise to unlock new capabilities for next-generation Edge AI applications. However, performing infere…
SafeMERGE: Preserving Safety Alignment in Fine-Tuned Large Language Models via Selective Layer-Wise Model Merging
Aladin Djuhera, Swanand Ravindra Kadhe, Farhan Ahmed +2
Fine-tuning large language models (LLMs) is a common practice to adapt generalist models to specialized domains. However, recent studies show that fine-tuning can erode safety alig…
A Simultaneous Decoding Approach to Joint State and Message Communications
Xinyang Li, Yiqi Chen, Vlad C. Andrei +3
The capacity-distortion (C-D) trade-offs for joint state and message communications (JSMC) over single- and multi-user channels are investigated, where the transmitters have access…
R-MTLLMF: Resilient Multi-Task Large Language Model Fusion at the Wireless Edge
Aladin Djuhera, Vlad C. Andrei, Mohsen Pourghasemian +3
Multi-task large language models (MTLLMs) are important for many applications at the wireless edge, where users demand specialized models to handle multiple tasks efficiently. Howe…
SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins
Aladin Djuhera, Amin Seffo, Vlad C. Andrei +2
Path planning under wireless performance constraints is a complex challenge in robot navigation. However, naively incorporating such constraints into classical planning algorithms…
R-SFLLM: Jamming Resilient Framework for Split Federated Learning with Large Language Models
Aladin Djuhera, Vlad C. Andrei, Xinyang Li +3
Split federated learning (SFL) is a compute-efficient paradigm in distributed machine learning (ML), where components of large ML models are outsourced to remote servers. A signifi…
Resilient-By-Design Framework for MIMO-OFDM Communications under Smart Jamming
Vlad C. Andrei, Aladin Djuhera, Xinyang Li +3
Native jamming mitigation is essential for addressing security and resilience in future 6G wireless networks. In this paper a resilient-by-design framework for effective anti-jammi…
An Analysis of Capacity-Distortion Trade-Offs in Memoryless ISAC Systems
Xinyang Li, Vlad C. Andrei, Aladin Djuhera +2
This manuscript investigates the information-theoretic limits of integrated sensing and communications (ISAC), aiming for simultaneous reliable communication and precise channel st…