← Search

Keivan Alizadeh

4 accepted papers

2026

Barriers for Learning in an Evolving World: Mathematical Understanding of Loss of Plasticity

ICLR 2026poster

Deep learning models excel in stationary settings but suffer from loss of plasticity (LoP) in non-stationary environments. While prior literature characterizes LoP through symptoms like rank collapse of representations, it often lacks a mechanistic explanation for why gradient descent fails to recov…

Cited by 0SourcecodeScholar
2025

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

ICLR 2025poster

Recent advancements in Large Language Models (LLMs) have sparked interest in their mathematical reasoning capabilities. While performance on the widely popular GSM8K benchmark has improved, questions remain about whether reported evaluation metrics are reliable, and reasoning abilities of LLMs have…

Cited by 209SourcePDFScholar
2025

The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity

NeurIPS 2025poster

Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limita…

Cited by 0SourceScholar
2024

LLM in a flash: Efficient Large Language Model Inference with Limited Memory

ACL 2024long

Large language models (LLMs) are central to modern natural language processing, delivering exceptional performance in various tasks. However, their substantial computational and memory requirements present challenges, especially for devices with limited DRAM capacity. This paper tackles the challeng…

Cited by 113SourcePDFScholar