2026

1. CVPR

   

   LLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   arXiv preprint arXiv:2511.08903, 2026

   Accepted to IEEE/CVF CVPR 2026

   arXiv

2025

1. 

   HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In ECAI 2025 – 27th European Conference on Artificial Intelligence, 2025

   DOI

2. 

   Efficient Unstructured Pruning of Mamba State-Space Models for Resource-Constrained Environments

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025

   Abs DOI

   As the deployment of AI models shifts towards edge devices, developing efficient sequence models has become critical. State-space models (SSMs), particularly Mamba, have emerged as strong rivals to Transformers due to their linear-time complexity and impressive performance across a range of tasks. However, their large parameter counts still hinder their use in resource-constrained environments. To address this, we propose a novel unstructured pruning framework specifically tailored for Mamba, achieving up to 70% parameter reduction with only a 3–9% drop in performance. Unlike pruning techniques designed for Transformers, our approach leverages Mamba’s unique recurrent dynamics by incorporating pruning based on both weight and gradient importance to preserve critical parameters, a gradual pruning schedule to maintain model stability, and a global strategy to optimize parameter allocation across the model. Extensive experiments on the WikiText-103, Long Range Arena, and ETT benchmarks demonstrate significant efficiency gains, including 1.77× faster inference and a 46% reduction in memory usage. Our component analysis confirms Mamba’s robustness to pruning, highlighting the framework’s potential for enabling practical deployment while underscoring the need for careful evaluation to avoid introducing biases in sensitive applications.

3. 

   Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains

   Ibne Farabi Shihab, Sanjeda Akter, and Anuj Sharma

   In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Nov 2025

   Abs DOI

   Integrating large language models (LLMs) as action proposers in reinforcement learning (RL) significantly boosts performance in text-based environments but incurs prohibitive computational costs. We introduce a cache-efficient framework for Bayesian RL that leverages LLM-derived action suggestions, drastically reducing these costs while maintaining near-optimal performance. Our approach features an adaptive caching mechanism, optimized via meta-learning based on policy performance, to enable efficient inference across text-based games (e.g., TextWorld, ALFWorld) and robotic control tasks (e.g., MuJoCo, MetaWorld). This framework achieves a 3.8×–4.7× reduction in LLM queries and 4.0×–12.0× lower median latencies (85–93ms on consumer hardware), while retaining 96–98% of the uncached policy’s performance. We provide theoretical guarantees on the reliability of cached decisions with Kullback-Leibler (KL) divergence bounds, which are validated empirically by high success rates (90.4–95.6%) in complex text environments. For offline RL, our proposed CQL-Prior variant improves performance by 14–29% and reduces training time by 38–40%. Evaluations across eight diverse tasks demonstrate the framework’s generalizability and practicality for resource-constrained settings, making LLM-guided RL a viable and accessible approach for both text-based and robotic applications.

4. ITSC

   

   ClearVision: Leveraging CycleGAN and SigLIP-2 for Robust All-Weather Classification in Traffic Camera Imagery

   Anush L. Sivaraman, Kwadwo Adu-Gyamfi, Ibne Farabi Shihab, and 1 more author

   arXiv preprint arXiv:2504.19684, Nov 2025

   Accepted to ITSC 2025

   arXiv
5. ITSC

   

   Accuracy Is Not Agreement: Expert-Aligned Evaluation of Crash Narrative Classification Models

   Sudesh Ramesh Bhagat, Ibne Farabi Shihab, and Anuj Sharma

   arXiv preprint arXiv:2504.13068, Nov 2025

   Accepted to ITSC 2025

   arXiv

6. 

   Quantum-driven Zero Trust Architecture with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach

   Shakil Ahmed, Ibne Farabi Shihab, and Ashfaq Khokhar

   Measurement: Digitalization, Nov 2025

2024

1. CVPR

   

   Deeplocalization: Using Change Point Detection for Temporal Action Localization

   Mohammed Shaiqur Rahman, Ibne Farabi Shihab, Lu Chu, and 1 more author

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nov 2024
2. Biotech

   

   Reinforcement Learning-Guided Control Strategies for CAR T-Cell Activation and Expansion

   Sakib Ferdous, Ibne Farabi Shihab, Ratul Chowdhury, and 1 more author

   Biotechnology and Bioengineering, Nov 2024
3. TRC-30

   

   Leveraging Video-LLMs for Crash Detection and Narrative Generation: Performance Analysis and Challenges

   Ibne Farabi Shihab, B. I. Alvee, and Anuj Sharma

   In Proceedings of the TRC-30 Conference, Nov 2024

2023

1. ITSC

   

   Robust and Precise Sidewalk Detection with Ensemble Learning: Enhancing Road Safety and Facilitating Curb Space Management

   Ibne Farabi Shihab, Sudesh Ramesh Bhagat, and Anuj Sharma

   In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Nov 2023

   DOI

2022

1. ICCIT

   

   Detecting Faulty Machinery of Waste Water Treatment Plant Using Statistical Analysis & Machine Learning

   Md. Mazed Ul Islam, Joyanta J. Mondal, and Ibne Farabi Shihab

   In 2022 25th International Conference on Computer and Information Technology (ICCIT), Nov 2022
2. BEJ

   

   Effects of Sequence Features on Machine-Learned Enzyme Classification Fidelity

   Sakib Ferdous, Ibne Farabi Shihab, and Nigel F. Reuel

   Biochemical Engineering Journal, Nov 2022

2018

1. R10-HTC

   

   A Machine Learning Approach to Suggest Ideal Geographical Location for New Restaurant Establishment

   Ibne Farabi Shihab, Maliha Moonwara Oishi, Samiul Islam, and 2 more authors

   In 2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Nov 2018