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Works126 from public data
- 3,499
- Understanding R1-Zero-Like Training: A Critical Perspective1,439
This work critically examine R1-Zero-like training by analyzing its two core components: base models and RL, and introduces Dr. GRPO, an unbiased optimization method that improves token efficiency while maintaining reasoning performance.
- Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks1,164
A translation-invariant attack method to generate more transferable adversarial examples against the defense models, which fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the current defense techniques.
- Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser1,059
High-level representation guided denoiser (HGD) is proposed as a defense for image classification by using a loss function defined as the difference between the target model's outputs activated by the clean image and denoised image.
- LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition435
This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks.
- Adversarial Attacks and Defences Competition364
In this chapter, the structure and organization of the NIPS 2017 competition is described and the solutions developed by several of the top-placing teams are described.
- Better Diffusion Models Further Improve Adversarial Training327
This paper gives an affirmative answer by employing the most recent diffusion model which has higher efficiency and image quality compared with DDPM and achieves state-of-the-art performance on RobustBench using only generated data (no external datasets).
- 308
- 218
- Scaling up Masked Diffusion Models on Text213
The first scaling law for MDMs is established, demonstrating a scaling rate comparable to autoregressive models (ARMs) and a relatively small compute gap.
- RegMix: Data Mixture as Regression for Language Model Pre-training196
RegMix is proposed to automatically identify a high-performing data mixture by formulating it as a regression task and consistently outperforms human selection in experiments involving models up to 7B models trained on 100B tokens, while matching or exceeding DoReMi using just 10% of the computational resources.
- A Recipe for Watermarking Diffusion Models196
This work conducts comprehensive analyses and derives a recipe for efficiently watermarking state-of-the-art DMs (e.g., Stable Diffusion), via training from scratch or finetuning, providing a foundation for future research on water marking DMs.
- Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast182
The results show that feeding an (infectious) adversarial image into the memory of any randomly chosen agent is sufficient to achieve infectious jailbreak, and derive a simple principle for determining whether a defense mechanism can provably restrain the spread of infectious jailbreak.
- 168
- On Memorization in Diffusion Models140
This study quantifies the impact of the influential factors on memorization behaviors in diffusion models, and defines effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum.
- Black-box Detection of Backdoor Attacks with Limited Information and Data140
This paper proposes a black-box backdoor detection (B3D) method to identify backdoor attacks with only query access to the model and introduces a gradient-free optimization algorithm to reverse-engineer the potential trigger for each class, which helps to reveal the existence of backdoor attacks.
- Improved Techniques for Optimization-Based Jailbreaking on Large Language Models134
Improved techniques for optimization-based jailbreaks like GCG are presented and demonstrated that they can help GCG outperform state-of-the-art jailbreaking attacks and achieve nearly 100% attack success rate.
- Weak-to-Strong Jailbreaking on Large Language Models130
The weak-to-strong jailbreaking attack is proposed, an efficient inference time attack for aligned LLMs to produce harmful text, based on the observation that jailbroken and aligned models only differ in their initial decoding distributions.
- Towards Face Encryption by Generating Adversarial Identity Masks127
This work proposes a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks which can be overlaid on facial images, such that the original identities can be concealed without sacrificing the visual quality.
- Finetuning Text-to-Image Diffusion Models for Fairness120
The method markedly reduces gender, racial, and their intersectional biases for occupational prompts and supports diverse perspectives of fairness beyond absolute equality, which is demonstrated by controlling age to a young and old distribution while simultaneously debiasing gender and race.
- Bag of Tricks for Training Data Extraction from Language Models100
The experimental results show that several previously overlooked tricks can be crucial to the success of training data extraction, and proposed tricks outperform the baseline by a large margin in most cases, providing a much stronger baseline for future research.
- 98
- Discovering Adversarial Examples with Momentum98
A strong attack algorithm named momentum iterative fast gradient sign method (MI-FGSM) is proposed to discover adversarial examples and can serve as a benchmark attack algorithm for evaluating the robustness of various models and defense methods.
- 89
- Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety76
This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models, Large Language Models, Vision-Language Pre-training models, Vision-Language Models, Diffusion Models, and large-model-powered Agents, and presents a comprehensive taxonomy of safety threats to these models.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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