{"total":23,"rows":[{"id":"2607.02770","title":"Gemma 4 Technical Report","created":"2026-07-02","cited_by_count":259,"area":"mllm"},{"id":"2304.13013","title":"Stable and low-precision training for large-scale vision-language models","created":"2023-04-25","cited_by_count":75,"area":"mllm"},{"id":"2404.14047","title":"An empirical study of LLaMA3 quantization: from LLMs to MLLMs","created":"2024-04-22","cited_by_count":64,"area":"mllm"},{"id":"2502.07855","title":"Vision-Language Models for Edge Networks: A Comprehensive Survey","created":"2025-02-11","cited_by_count":29,"area":"mllm"},{"id":"2601.20088","title":"Quantization-Aware Distillation for NVFP4 Inference Accuracy Recovery","created":"2026-01-27","cited_by_count":22,"area":"mllm"},{"id":"2403.04908","title":"Self-Adapting Large Visual-Language Models to Edge Devices across Visual Modalities","created":"2024-03-07","cited_by_count":7,"area":"mllm"},{"id":"2409.17634","title":"P4Q: Learning to Prompt for Quantization in Visual-language Models","created":"2024-09-26","cited_by_count":7,"area":"mllm"},{"id":"2509.18763","title":"Bi-VLM: Pushing Ultra-Low Precision Post-Training Quantization Boundaries in Vision-Language Models","created":"2025-09-23","cited_by_count":5,"area":"mllm"},{"id":"2503.04982","title":"LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression","created":"2025-03-06","cited_by_count":4,"area":"mllm"},{"id":"2510.21879","title":"TernaryCLIP: Efficiently Compressing Vision-Language Models with Ternary Weights and Distilled Knowledge","created":"2025-10-23","cited_by_count":4,"area":"mllm"},{"id":"2509.21173","title":"Less Precise Can Be More Reliable: A Systematic Evaluation of Quantization's Impact on VLMs Beyond Accuracy","created":"2025-09-25","cited_by_count":3,"area":"mllm"},{"id":"2507.01042","title":"Can Argus Judge Them All? Comparing VLMs Across Domains","created":"2025-06-23","cited_by_count":2,"area":"mllm"},{"id":"2509.12263","title":"InPhyRe Discovers: Large Multimodal Models Struggle in Inductive Physical Reasoning","created":"2025-09-12","cited_by_count":2,"area":"mllm"},{"id":"2601.15287","title":"Towards Understanding Best Practices for Quantization of Vision-Language Models","created":"2026-01-21","cited_by_count":2,"area":"mllm"},{"id":"2601.14722","title":"Typhoon OCR: Open Vision-Language Model For Thai Document Extraction","created":"2026-01-21","cited_by_count":1,"area":"mllm"},{"id":"2602.07849","title":"LQA: A Lightweight Quantized-Adaptive Framework for Vision-Language Models on the Edge","created":"2026-02-08","cited_by_count":1,"area":"mllm"},{"id":"2604.17320","title":"Towards Joint Quantization and Token Pruning of Vision-Language Models","created":"2026-04-19","cited_by_count":1,"area":"mllm"},{"id":"2605.30968","title":"Variational Adapter for Cross-modal Similarity Representation","created":"2026-05-29","cited_by_count":1,"area":"mllm"},{"id":"2510.10560","title":"BitMar: Low-Bit Multimodal Fusion with Episodic Memory for Edge Devices","created":"2025-10-12","cited_by_count":0,"area":"mllm"},{"id":"2511.18504","title":"Extreme Model Compression for Edge Vision-Language Models: Sparse Temporal Token Fusion and Adaptive Neural Compression","created":"2025-11-23","cited_by_count":0,"area":"mllm"},{"id":"2609.16689","title":"Efficient Quantization-Aware Distillation with Cross-Modal Alignment for Edge Vision-Language Models","created":"2026-09-15","cited_by_count":0,"area":"mllm"},{"id":"2609.22897","title":"Scout: Open-World Species Recognition on the Edge","created":"2026-09-19","cited_by_count":0,"area":"mllm"},{"id":"2609.34765","title":"Beyond Reconstruction Loss in Post-Training Quantization: Balanced Fitting for Large Vision-Language Models","created":"2026-09-28","cited_by_count":0,"area":"mllm"}],"next_cursor":null}