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University of Illinois at Chicago

Academic institutionnorthamerica · us
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Research library745linked papers
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Selected work

Representative Papers

Robust Consensus-Based Distributed Beamforming for Wideband Cell-free Multi-RIS MISO Systems

Jan 13, 2026

This work addresses the high coordination overhead and poor robustness of centralized beamforming in wideband cell-free multi-RIS-assisted MISO systems, which arise from imperfect channel state information and the frequency-selective response of reconfigurable intelligent surfaces (RISs). To overcome these challenges, the paper proposes a consensus-based distributed framework for joint active and passive beamforming. For the first time, consensus optimization is introduced into wideband multi-RIS cell-free systems, enabling distributed and robust phase control of RISs without requiring a central processing unit. The approach integrates Lorentzian-based RIS modeling with imperfect channel information handling to ensure practical feasibility. Simulation results demonstrate that the proposed method significantly reduces coordination overhead, enhances system robustness, and outperforms existing Lorentzian-model-based centralized schemes in terms of overall performance.

2 citationsRead paper

Fair Distribution of Digital Payments: Balancing Transaction Flows for Regulatory Compliance

Nov 30, 2025arXiv.org

This study addresses the problem of fairly redistributing payment traffic under the regulatory constraint that no single UPI application may handle more than 30% of total transaction volume, modeling it as a Minimum Edge Activation Flow (MEAF) problem on a bipartite graph to minimize the number of additional payment applications users must install. The work formally defines this regulation-driven problem for the first time and proves its NP-completeness. To solve it efficiently, the authors propose a scalable two-stage decoupled allocation strategy (DTAS) that combines integer linear programming with heuristic methods, leveraging structural properties of traffic flows and capacity reuse mechanisms. Experimental results demonstrate that DTAS generates near-optimal, high-quality solutions within seconds on large-scale semi-synthetic networks, substantially improving regulatory compliance efficiency.

1 citations1 influentialRead paper

From Observations to States: Latent Time Series Forecasting

Jan 30, 2026

This work addresses a critical yet previously unarticulated issue in time series forecasting—termed “latent chaos”—where conventional methods operating directly in the observation space learn representations that are temporally inconsistent and lack continuity, thereby failing to capture the true underlying dynamics of the system. To overcome this limitation, the paper introduces LatentTSF, a novel paradigm that leverages an autoencoder to construct a high-dimensional latent state space in which prediction is performed. By implicitly maximizing the mutual information among latent states, ground-truth system states, and observations, LatentTSF enforces temporal coherence and dynamical fidelity. Theoretical analysis and extensive experiments demonstrate that this approach substantially mitigates latent chaos and achieves state-of-the-art forecasting performance across multiple established benchmarks.

1 citationsRead paper

What Do LLMs Know About Alzheimer's Disease? Fine-Tuning, Probing, and Data Synthesis for AD Detection

Jan 20, 2026arXiv.org

This study addresses the challenges of scarce labeled data and limited model generalizability in early Alzheimer’s disease (AD) detection by systematically evaluating and fine-tuning large language models—including BERT, T5, and Llama-1B—using a novel multi-loss supervised fine-tuning strategy. The approach is trained and validated across three heterogeneous clinical corpora: Pitt, CCC, and ADRC. Through linear probing and cross-corpus transfer analyses, the work demonstrates that fine-tuning substantially enhances the models’ ability to encode AD-related linguistic signals. Notably, decoder-only architectures such as Llama-1B exhibit competitive or even superior performance compared to encoder-decoder models on this task. The method achieves new state-of-the-art results on both the Pitt and CCC datasets and shows strong performance on ADRC.

1 citationsRead paper

Lost in the Noise: How Reasoning Models Fail with Contextual Distractors

Jan 12, 2026

This work addresses the critical gap in evaluating the robustness of reasoning models under realistic noisy conditions—such as irrelevant documents, chat histories, and strong negative examples—where current benchmarks fall short. To this end, we introduce NoisyBench, the first systematic benchmark assessing model resilience across 11 tasks spanning RAG, reasoning, alignment, and tool use. Our analysis uncovers several counterintuitive findings: context noise can degrade performance by up to 80%, agent workflows amplify errors, and increased test-time computation may harm accuracy. We further propose Rationale-Aware Reward (RARE), a reinforcement learning method that steers models toward valid reasoning traces, significantly enhancing noise robustness. Experiments show that conventional approaches—including prompt engineering, supervised fine-tuning, and outcome-based reward RL—fail to improve resilience, whereas RARE effectively mitigates over-attention to distractor tokens, offering key insights for building robust reasoning agents.

1 citationsRead paper
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