5 papers
Sample Count Is Not Enough: Candidate-Generation Strategy Shapes the Energy and Performance of LLM Test-Time Scaling
Mobina Kashaniyan, Ali Jannesari
Test-time scaling can improve large language model reasoning by generating and combining multiple candidate responses. In sampling-based methods, the inference budget is often desc…
LLM-Driven AutoML for Cross-Lingual Handwritten OCR: Closed-Loop Neural Architecture Search with GPT-5, GPT-4o, and Claude Sonnet 4
Mobina Kashaniyan, Amirhossein Ghassemi, Nasser Mozayani
We present a fully automated closed-loop AutoML framework that uses GPT-5, GPT-4o, and Claude Sonnet 4 as autonomous neural architecture designers for cross-lingual handwritten opt…
An Auto-Scaling Approach for Serverless Environments Based on a Multi-Expert Consensus Mechanism
Mobina Kashaniyan, Mehrdad Ashtiani, Amirhossein Ghassemi
Serverless computing provides automatic resource management and pay-per-use execution, but effective autoscaling remains challenging because of dynamic workloads, cold-start latenc…
Interpretable Adaptive Sampling for LLM Test-Time Scaling
Mobina Kashaniyan, Ali Jannesari
Test-time scaling improves LLM reasoning by generating and aggregating multiple candidate answers, yet many pipelines use fixed per-query budgets that spend the same compute on eas…
PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu +2
Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequen…