PerfMamba: Performance Analysis and Pruning of Selective State Space Models

Thu, 01 Jan 2026 00:00:00 +0000

This paper contributes to research on PerfMamba, Mamba-1, Mamba-2, selective state space models, performance analysis, model pruning, benchmarking, efficient AI, and high-performance computing by providing a systematic empirical study of the runtime behavior and optimization opportunities of Mamba-style architectures.

Recent sequence modeling research has introduced selective state space models as efficient alternatives to Transformer architectures. However, their real-world performance behavior, memory access patterns, I/O characteristics, resource utilization, and scaling properties require deeper analysis for effective deployment.

PerfMamba profiles Mamba-1 and Mamba-2 across sequence lengths from 64 to 16,384 tokens. The study analyzes computation patterns, memory behavior, I/O characteristics, and scaling trends to identify the components that dominate runtime and resource usage.

Based on these insights, the paper proposes a pruning technique that removes low-activity states within the SSM component. This supports improved throughput and reduced memory usage while maintaining accuracy under moderate pruning.

This work is relevant to researchers working on Mamba, selective state space models, state space models, large language models, sequence modeling, model pruning, model compression, runtime profiling, AI systems, neural network acceleration, and hardware-aware optimization.

Keywords: PerfMamba, Mamba, Mamba-1, Mamba-2, selective state space models, state space models, SSM, sequence modeling, Transformer alternatives, performance analysis, benchmarking, runtime profiling, resource utilization, memory access patterns, I/O characteristics, scaling analysis, model pruning, state pruning, model compression, efficient AI, high-performance computing, hardware-aware optimization, large language models, AI systems.

Citation:

Al Asif, A., Kashaniyan, M., Yu, S., Muñoz, J. P., & Jannesari, A. (2026). PerfMamba: Performance analysis and pruning of selective state space models. In International Symposium on Benchmarking, Measuring and Optimization (pp. 27–44). Springer, Singapore.

LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4

Tue, 28 Oct 2025 00:00:00 +0000

This paper contributes to research on handwritten OCR, cross-lingual OCR, multilingual AI, AutoML, neural architecture search, and LLM-driven model design by introducing a fully automated pipeline for handwritten optical character recognition across multiple scripts.

Unlike traditional OCR approaches that rely on manual neural architecture design, language-specific preprocessing, or expert-guided model selection, this work uses large language models to generate, evaluate, and refine OCR architectures in a closed-loop neural architecture search process.

The proposed framework applies GPT-5, GPT-4o, and Claude Sonnet 4 to automate model design for Arabic, English, and Persian handwritten text recognition. By testing the method across scripts with different visual and linguistic characteristics, the paper highlights the potential of LLM-driven AutoML for scalable multilingual OCR and low-resource language applications.

This work is relevant to researchers working on optical character recognition, handwritten text recognition, multilingual document intelligence, neural architecture search, AutoML, LLM agents, computer vision, and low-resource language processing.

Keywords: handwritten OCR, cross-lingual OCR, multilingual OCR, optical character recognition, handwritten text recognition, AutoML, neural architecture search, LLM-driven AutoML, large language models, GPT-5, GPT-4o, Claude Sonnet 4, LLM agents, automated model design, document intelligence, multilingual AI, Arabic handwriting recognition, Persian handwriting recognition, low-resource language processing, computer vision for text recognition.

Citation:

Kashaniyan, M., Ghassemi, A., & Mozayani, N. (2025). LLM-driven AutoML for cross-lingual handwritten OCR: Closed-loop neural architecture search with GPT-5, GPT-4o, and Claude Sonnet 4. In 2025 15th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, pp. 1–6. https://doi.org/10.1109/ICCKE68588.2025.11273810

Conference Paper | Mobina Kashaniyan

PerfMamba: Performance Analysis and Pruning of Selective State Space Models

LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4