VILA-Lab: Vision and Language Acceleration Lab ORG

A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171

A Systematic Analysis and Discussion of Claude Code for Designing Today's and Future AI Agent Systems

The official GitHub repo for the survey paper "A Survey on Diffusion Language Models".

An Automated AI Agent Tool for Plotting Your Data in Any Paper's Figure Style.

[NeurIPS25 & ICML25 Workshop on Reliable and Responsible Foundation Models] A Simple Baseline Achieving Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1. Paper at: https://arxiv.org/abs/2503.10635

(NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.

Open-LLM-Leaderboard: Open-Style Question Evaluation. Paper at https://arxiv.org/abs/2406.07545

Are gradient information useful for pruning of LLMs?

[ICLR 2026 🔥] Official pytorch implementation for "Attention Is All You Need for KV Cache in Diffusion LLMs"

(ACL 2025 Main) Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation

[ICLR 2026] Optimization-free Dataset Distillation for Object Detection. Paper at: https://arxiv.org/abs/2506.01942

(CVPR 2025) Official implementation to DELT: A Simple Diversity-driven EarlyLate Training for Dataset Distillation which outperforms SOTA top 1-acc by +1.3% and increases diversity per class by +5%

i-mae Pytorch Repo

ActiveSAM: Image-Conditional Class Pruning for Fast and Accurate Open-Vocabulary Segmentation

Official implementation of paper: From Masks to Pixels and Meaning: A New Taxonomy, Benchmark, and Metrics for VLM Image Tampering (ECCV2026)

Official implementation of paper: Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targeting

Official code for our paper "Sink-Aware Pruning for Diffusion Language Models"

Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark. Paper at: https://arxiv.org/abs/2503.20786

Operation-guided benchmark for AI-text detection under progressive human–AI revision trajectories.

[ACL 2026 Findings] P-TTS: inference-time reasoning data augmentation that scales prompt-space with principled instructions, matching/exceeding larger-data baselines.

Scaling Linear Mode Connectivity and Merging to Billion-Parameter Pretrained Transformers

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M-Attack-Website

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