Multi-Objective Structured Pruning of LLMs for Latency and Model Size Optimization

arXiv:2607.22583 · cs.AI, cs.CL · Submitted 2026-06-08 · Read on arXiv

cs.AI, cs.CL

Submitted: 2026-06-08

Updated: 2026-10-17

Comments: Accepted for presentation at the ICTAI 2026 Conference

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Pruning — 7× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Edge Deployment — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 8 episodes
• Multi-Layer Perceptron — 1× in this paper
• Zero-shot performance — 1× in this paper · explained in 2 episodes

Related papers

• MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
• Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
• Agents' Last Exam
• The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
• MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
• Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems