Bits Under ZK-LLM: Evaluating Zero-Knowledge-Friendly Quantization for Verifiable Private LLM Inference
cs.AI
Submitted: 2026-09-29
Updated: 2026-09-29
Code: https://github.com/Plonky3/Plonky3
Terminology
Sources
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
- LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
- Measuring Massive Multitask Language Understanding
- VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs
- Pointer Sentinel Mixture Models
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
- Detecting Pretraining Data from Large Language Models
- A Simple and Effective Pruning Approach for Large Language Models
- ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
- Accurate nonrelativistic ground-state energies of 3d transition metal atoms
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