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Our Revolution Virtual Lobby Training
Our Revolution Virtual Lobby Training Your elected officials in Congress need to hear from you to build support for progressive policies and make democracy work for the many! Come learn about Our Revolution’s virtual lobby model, how you can get involved, and opportunities to join your neighbors to meet with your Senators and Representatives. Event Details: Date: Sunday, June 22, 2025 Time: 7pm ET (4pm PT) Location: Virtual Training - Zoom Link

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Zhao Yang, Jiwei Zhu, Bing Su
https://arxiv.org/abs/2506.01833 https://

SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model
Inspired by the success of unsupervised pre-training paradigms, researchers have applied these approaches to DNA pre-training. However, we argue that these approaches alone yield suboptimal results because pure DNA sequences lack sufficient information, since their functions are regulated by genomic profiles like chromatin accessibility. Here, we demonstrate that supervised training for genomic profile prediction serves as a more effective alternative to pure sequence pre-training. Furthermore,…

Your Agent Can Defend Itself against Backdoor Attacks
Li Changjiang, Liang Jiacheng, Cao Bochuan, Chen Jinghui, Wang Ting
https://arxiv.org/abs/2506.08336 …

Your Agent Can Defend Itself against Backdoor Attacks
Despite their growing adoption across domains, large language model (LLM)-powered agents face significant security risks from backdoor attacks during training and fine-tuning. These compromised agents can subsequently be manipulated to execute malicious operations when presented with specific triggers in their inputs or environments. To address this pressing risk, we present ReAgent, a novel defense against a range of backdoor attacks on LLM-based agents. Intuitively, backdoor attacks often res…

This https://arxiv.org/abs/2504.13416 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

STAMP Your Content: Proving Dataset Membership via Watermarked Rephrasings
Given how large parts of publicly available text are crawled to pretrain large language models (LLMs), data creators increasingly worry about the inclusion of their proprietary data for model training without attribution or licensing. Their concerns are also shared by benchmark curators whose test-sets might be compromised. In this paper, we present STAMP, a framework for detecting dataset membership-i.e., determining the inclusion of a dataset in the pretraining corpora of LLMs. Given an origi…