60 citations · 69 across the 11 of their papers we have counts for
5 papers · 1 filter
Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference
Md Mostafizer Rahman, Md Faizul Ibne Amin, Md Shahajada Mia +2
Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latency, energy consumption, and G…
Revision-Aware Success Prediction from Multi-Attempt Programming Trajectories
Md Faizul Ibne Amin, Yutaka Watanobe, Daniel M. Muepu +4
Programming outcome prediction plays a central role in data-driven programming education, supporting learner modeling, timely intervention, and adaptive assistance. Yet predicting…
LLM-as-a-Judge for Contest-Style Human-AI Co-Creation: A Reliability-Aware Evaluation Framework for Coding
Md Faizul Ibne Amin, Yutaka Watanobe, Daniel M. Muepu +3
Large language models now judge open-ended outputs and co-create code with programmers, yet judgments of human-AI co-creation must be reliable, comparable across models, and interp…
CodeT5-RNN: Reinforcing Contextual Embeddings for Enhanced Code Comprehension
Md Mostafizer Rahman, Ariful Islam Shiplu, Yutaka Watanobe +3
Contextual embeddings generated by LLMs exhibit strong positional inductive biases, which can limit their ability to fully capture long-range, order-sensitive dependencies in highl…
Error Understanding in Program Code: A Systematic Study of LLM-DL Combinations for Multi-label Classification
Md Faizul Ibne Amin, Yutaka Watanobe, Md. Mostafizer Rahman +2
Programming is a core skill in CS and SE, yet identifying and resolving code errors remains challenging for practitioners. LLMs have shown remarkable capabilities in NL understandi…