40 papers
APQF: Agentic Profiling-Guided Structured Pruning and Mixed-Precision Quantization with Adaptive Fine-Tuning
Sadegh Jafari, Mohiuddin Bilwal, Fan Zhou +2
Modern deep neural networks achieve strong performance, but their scale makes them costly and slow, especially on resource-constrained edge devices. Pruning and quantization addres…
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…
Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation
Quazi Ishtiaque Mahmud, Nesreen K. Ahmed, Ali Jannesari
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for optimized code generation. However, existing…
VarRate: Training-Free Variable-Rate KV Cache Compression for Long-Context LLMs
Shahrzad Esmat, Dhawal Shah, Ali Jannesari
The key-value (KV) cache is the main memory bottleneck in long-context large language model (LLM) inference. Two leading training-free families are both structurally limited: token…
HiDVFS: Hierarchical Multi-Agent DVFS for Real-Time OpenMP DAG Workloads
Mohammad Pivezhandi, Abusayeed Saifullah, Ali Jannesari
Leakage power in multicore embedded systems now rivals dynamic power, so DVFS schedulers must respect deadlines and thermal limits, not just average makespan. Existing heuristics l…
POTracker: Optimizing Large Language Models for Standard-Compliant Power Outage Report Generation
Hung Phan, Aniroop Naladala, Dubey Avanindra +3
Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict…