1 citations · 2 across the 18 of their papers we have counts for
22 papers
Monte Carlo Estimation for KV Cache Eviction
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +5
Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask which memory will matter while answering? Since decoding que…
Refining Over Resampling: Test-Time Self-Correction for LLM Reasoning
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +4
Test-time scaling improves LLM reasoning by using additional inference compute, but wider sampling alone can suffer from diminishing returns: new rollouts often repeat existing ans…
General Preference Reinforcement Learning
Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +5
Post-training has split large language model (LLM) alignment into two largely disconnected tracks. Online reinforcement learning (RL) with verifiable rewards drives emergent reason…
Canonical Optimization for MIMO MAC Design
Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +1
Resource allocation in the multiple-input multiple-output (MIMO) multiple access channel (MAC) is a fundamental problem in multiuser communications, yet it is increasingly treated…
Pressure, What Pressure? Sycophancy Disentanglement in Language Models via Reward Decomposition
Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer +1
Large language models exhibit sycophancy, the tendency to shift their stated positions toward perceived user preferences or authority cues regardless of evidence. Standard alignmen…
What If We Allocate Test-Time Compute Adaptively?
Ahsan Bilal, Ahmed Mohsin, Muhammad Umer +4
Test-time compute scaling allocates inference computation uniformly, uses fixed sampling strategies, and applies verification only for reranking. In contrast, we propose a verifier…
Continuous-Utility Direct Preference Optimization
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +6
Large language model reasoning is often treated as a monolithic capability, relying on binary preference supervision that fails to capture partial progress or fine-grained reasonin…
Transformer-Based Sparse CSI Estimation for Non-Stationary Channels
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +4
Accurate and efficient estimation of Channel State Information (CSI) is critical for next-generation wireless systems operating under non-stationary conditions, where user mobility…
On the Fundamental Limits of LLMs at Scale
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +13
Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) re…
6G Twin: Hybrid Gaussian Radio Fields for Channel Estimation and Non-Linear Precoder Design for Radio Access Networks
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +3
This work introduces 6G Twin, the first end-to-end artificial intelligence (AI)-native radio access network (RAN) design that unifies (i) neural Gaussian Radio Fields (GRF) for com…
Channel Prediction under Network Distribution Shift Using Continual Learning-based Loss Regularization
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +4
Modern wireless networks face critical challenges when mobile users traverse heterogeneous network configurations with varying antenna layouts, carrier frequencies, and scattering…
Conditional Prior-based Non-stationary Channel Estimation Using Accelerated Diffusion Models
Muhammad Ahmed Mohsin, Ahsan Bilal, Muhammad Umer +4
Wireless channels in motion-rich urban microcell (UMi) settings are non-stationary; mobility and scatterer dynamics shift the distribution over time, degrading classical and deep e…
Neural Gaussian Radio Fields for Channel Estimation
Muhammad Umer, Muhammad Ahmed Mohsin, Ahsan Bilal +1
Accurate channel state information (CSI) is a critical bottleneck in modern wireless networks, with pilot overhead consuming 11\% to 21\% of transmission bandwidth and feedback del…
On Energy-Efficient Passive Beamforming Design of RIS-Assisted CoMP-NOMA Networks
Muhammad Umer, Muhammad Ahmed Mohsin, Aamir Mahmood +4
This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and perfor…
Continual Learning for Wireless Channel Prediction
Muhammad Ahmed Mohsin, Muhammad Umer, Ahsan Bilal +2
Modern 5G/6G deployments routinely face cross-configuration handovers--users traversing cells with different antenna layouts, carrier frequencies, and scattering statistics--which…
Resource Allocation for RIS-Assisted CoMP-NOMA Networks using Reinforcement Learning
Muhammad Umer, Muhammad Ahmed Mohsin, Huma Ghafoor +1
This thesis delves into the forefront of wireless communication by exploring the synergistic integration of three transformative technologies: STAR-RIS, CoMP, and NOMA. Driven by t…
Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +2
This survey explores the development of meta-thinking capabilities in Large Language Models (LLMs) from a Multi-Agent Reinforcement Learning (MARL) perspective. Meta-thinking self-…
Intelligent Spectrum Sharing in Integrated TN-NTNs: A Hierarchical Deep Reinforcement Learning Approach
Muhammad Umer, Muhammad Ahmed Mohsin, Ali Arshad Nasir +2
Integrating non-terrestrial networks (NTNs) with terrestrial networks (TNs) is key to enhancing coverage, capacity, and reliability in future wireless communications. However, the…
Computation Offloading Strategies in Integrated Terrestrial and Non-Terrestrial Networks
Muhammad Ahmed Mohsin, Muhammad Umer, Amara Umar +2
The rapid growth of computation-intensive applications like augmented reality, autonomous driving, remote healthcare, and smart cities has exposed the limitations of traditional te…
Hierarchical Deep Reinforcement Learning for Adaptive Resource Management in Integrated Terrestrial and Non-Terrestrial Networks
Muhammad Ahmed Mohsin, Hassan Rizwan, Muhammad Umer +3
Efficient spectrum allocation has become crucial as the surge in wireless-connected devices demands seamless support for more users and applications, a trend expected to grow with…
RIS-Assisted Aerial Non-Terrestrial Networks: An Intelligent Synergy with Deep Reinforcement Learning
Muhammad Umer, Muhammad Ahmed Mohsin, Aryan Kaushik +3
Reconfigurable intelligent surface (RIS)-assisted aerial non-terrestrial networks (NTNs) offer a promising paradigm for enhancing wireless communications in the era of 6G and beyon…
Deep Reinforcement Learning for Trajectory and Phase Shift Optimization of Aerial RIS in CoMP-NOMA Networks
Muhammad Umer, Muhammad Ahmed Mohsin, Aamir Mahmood +4
This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) networks. We co…