5 papers
DiSCTT: Consensus-Guided Self-Curriculum for Efficient Test-Time Adaptation in Reasoning
Mohammad Mahdi Moradi, Sudhir Mudur
Test-time adaptation offers a promising avenue for improving reasoning performance in large language models without additional supervision, but existing approaches often apply a un…
Towards Learning-Based Formula 1 Race Strategies
Giona Fieni, Joschua Wüthrich, Marc-Philippe Neumann +2
This paper presents two complementary frameworks to optimize Formula 1 race strategies, jointly accounting for energy allocation, tire wear and pit stop timing. First, the race sce…
Continuous Self-Improvement of Large Language Models by Test-time Training with Verifier-Driven Sample Selection
Mohammad Mahdi Moradi, Hossam Amer, Sudhir Mudur +3
Learning to adapt pretrained language models to unlabeled, out-of-distribution data is a critical challenge, as models often falter on structurally novel reasoning tasks even while…
Balancing Computation Load and Representation Expressivity in Parallel Hybrid Neural Networks
Mohammad Mahdi Moradi, Walid Ahmed, Shuangyue Wen +3
Attention and State-Space Models (SSMs) when combined in a hybrid network in sequence or in parallel provide complementary strengths. In a hybrid sequential pipeline they alternate…
GC-KBVQA: A New Four-Stage Framework for Enhancing Knowledge Based Visual Question Answering Performance
Mohammad Mahdi Moradi, Sudhir Mudur
Knowledge-Based Visual Question Answering (KB-VQA) methods focus on tasks that demand reasoning with information extending beyond the explicit content depicted in the image. Early…