1 citations · 2 across the 18 of their papers we have counts for
24 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…
AgentPersonaBench: Benchmarking Persona-Driven User Simulation
Jintao Huang, Yifan Wang, Hongyu Shen +43
We introduce AgentPersonaBench (APB), a benchmark evaluating whether persona conditioning faithfully steers downstream agent behavior. While language models are increasingly deploy…
Faithfulness Is Not Free: Auditing Offline KV-Cache Quantization in Retrieval-Augmented Generation
Atta Ul Asad, Ahsan Bilal, Muhammad Ali +2
Retrieval-augmented generation systems can precompute and store key-value caches of retrieved documents to avoid re-encoding context at every query. Quantizing these caches further…
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…
Epistemic Uncertainty for Test-Time Discovery
Kainat Riaz, Muhammad Ahmed Mohsin, Ahsan Bilal +5
Automated scientific discovery using large language models relies on identifying genuinely novel solutions. Standard reinforcement learning penalizes high-variance mutations, which…
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…
: Stratified Scaling Search for Test-Time in Diffusion Language Models
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer +6
Test-time scaling investigates whether a fixed diffusion language model (DLM) can generate better outputs when given more inference compute, without additional training. However, n…
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…
Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
Oluwaseyi Giwa, Tobi Awodunmila, Muhammad Ahmed Mohsin +2
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infea…
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…
ItDPDM: Information-Theoretic Discrete Poisson Diffusion Model
Sagnik Bhattacharya, Abhiram Gorle, Ahsan Bilal +3
Generative modeling of non-negative, discrete data, such as symbolic music, remains challenging due to two persistent limitations in existing methods. Firstly, many approaches rely…
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-…
Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments
Muhammad Ahmed Mohsin, Ahsan Bilal, Sagnik Bhattacharya +1
Future wireless networks aim to deliver high data rates and lower power consumption while ensuring seamless connectivity, necessitating robust optimization. Large language models (…
Task and Perception-aware Distributed Source Coding for Correlated Speech under Bandwidth-constrained Channels
Sagnik Bhattacharya, Muhammad Ahmed Mohsin, Ahsan Bilal +1
Emerging wireless AR/VR applications require real-time transmission of correlated high-fidelity speech from multiple resource-constrained devices over unreliable, bandwidth-limited…
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…