Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data, thereby introducing new dimensions of potential attacks and complex risk combinations. In this paper, we begin with a detailed analysis aimed at disentangling risks through step-by-step reasoning within multimodal inputs. We find that systematic multimodal risk disentanglement substantially enhances the risk awareness of MLLMs. Via leveraging the strong discriminative abilities of multimodal risk disentanglement, we further introduce \textbf{DREAM} (\textit{\textbf{D}isentangling \textbf{R}isks to \textbf{E}nhance Safety \textbf{A}lignment in \textbf{M}LLMs}), a novel approach that enhances safety alignment in MLLMs through supervised fine-tuning and iterative Reinforcement Learning from AI Feedback (RLAIF). Experimental results show that DREAM significantly boosts safety during both inference and training phases without compromising performance on normal tasks (namely oversafety), achieving a 16.17\% improvement in the SIUO safe\&effective score compared to GPT-4V. The data and code are available at https://github.com/Kizna1ver/DREAM.

--In recent years, Unsupervised Domain Adaptation (UDA) has gained significant attention in the field of Natural Language Processing (NLP) owing to its ability to enhance model generalization across diverse domains. However, its application for knowledge transfer between distinct legal domains remains largely unexplored. T o address the challenges posed by lengthy and complex legal texts and the limited availability of large-scale annotated datasets, we propose JurisCTC, a novel model designed to improve the accuracy of Legal Judgment Prediction (LJP) tasks. Unlike existing approaches, JurisCTC facilitates effective knowledge transfer across various legal domains and employs contrastive learning to distinguish samples from different domains. Specifically, for the LJP task, we enable knowledge transfer between civil and criminal law domains. Compared to other models and specific large language models (LLMs), JurisCTC demonstrates notable advancements, achieving peak accuracies of 76.59% and 78.83%, respectively. Legal Judgment Prediction (LJP) refers to the task of forecasting court outcomes based on the facts of a legal case, as well as other relevant information such as arguments and claims presented in the case description. This field aims to leverage computational techniques to predict judicial decisions, offering significant benefits across various legal contexts. Automated LJP systems have considerable practical value: they can assist legal professionals in analyzing cases and providing consultation services to the public, thereby reducing legal costs and improving access to justice.

Introduction Swarm robotics systems ( 1) have the potential to revolutionize many industries, from targeted material delivery ( 2) to precision farming ( 3, 4). Boosted by technical breakthroughs, such as cloud computing ( 5, 6), novel hardware design ( 7-9), and manufacturing techniques ( 10), swarms of robots are envisioned to play an important role in both industrial ( 12) and urban ( 13, 14) activities. The emergence of robot swarms has been acknowledged as one of the ten robotics grand challenges for the next 5-10 years that will have significant socioeconomic impact. Despite having such a promising future, many important aspects which need to be considered in realistic deployments are either underexplored or neglected ( 15). One of the main reasons why swarms of robots have not been widely adopted in real-world applications is because there is no consensus on how to design swarm robotics systems that include perception, action, and communication ( 15). In addition, recent research points out that the lack of security standards in the field is also hindering the adoption of this technology in data-sensitive areas (e.g., military, surveillance, public infrastructure) ( 16, 17). These research gaps are motivating scientists to focus on new fields of study such as applied swarm security (18, 19) and privacy ( 20, 21) as well as to revisit already accepted assumptions in the field. From the origins of swarm robotics research, robot swarms were assumed to be fault-tolerant by design, due to the large number of robot units involved ( 22-25). However, it has been shown that a small number of partially failed (with defective sensors, broken actuators, noisy communications devices, etc.) ( 26) or malicious robots ( 27,28) can have a significant impact on 2 Figure 1: T owards secure and secret cooperation in swarm robotics missions.

This paper challenges the assumption that courts should grant First Amendment protections to outputs from large generative AI models, such as GPT-4 and Gemini. We argue that because these models lack intentionality, their outputs do not constitute speech as understood in the context of established legal precedent, so there can be no speech to protect. Furthermore, if the model outputs are not speech, users cannot claim a First Amendment speech right to receive the outputs. We also argue that extending First Amendment rights to AI models would not serve the fundamental purposes of free speech, such as promoting a marketplace of ideas, facilitating self-governance, or fostering self-expression. In fact, granting First Amendment protections to AI models would be detrimental to society because it would hinder the government's ability to regulate these powerful technologies effectively, potentially leading to the unchecked spread of misinformation and other harms.

Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement and ethical concerns. Training LLMs on openly licensed text presents a first step towards addressing these issues, but prior data collection efforts have yielded datasets too small or low-quality to produce performant LLMs. To address this gap, we collect, curate, and release the Common Pile v0.1, an eight terabyte collection of openly licensed text designed for LLM pretraining. The Common Pile comprises content from 30 sources that span diverse domains including research papers, code, books, encyclopedias, educational materials, audio transcripts, and more. Crucially, we validate our efforts by training two 7 billion parameter LLMs on text from the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion tokens respectively. Both models attain competitive performance to LLMs trained on unlicensed text with similar computational budgets, such as Llama 1 and 2 7B. In addition to releasing the Common Pile v0.1 itself, we also release the code used in its creation as well as the training mixture and checkpoints for the Comma v0.1 models.

Studies of LLMs' political opinions mainly rely on evaluations of their open-ended responses. Recent work indicates that there is a misalignment between LLMs' responses and their internal intentions. This motivates us to probe LLMs' internal mechanisms and help uncover their internal political states. Additionally, we found that the analysis of LLMs' political opinions often relies on single-axis concepts, which can lead to concept confounds. In this work, we extend the single-axis to multi-dimensions and apply interpretable representation engineering techniques for more transparent LLM political concept learning. Specifically, we designed a four-dimensional political learning framework and constructed a corresponding dataset for fine-grained political concept vector learning. These vectors can be used to detect and intervene in LLM internals. Experiments are conducted on eight open-source LLMs with three representation engineering techniques. Results show these vectors can disentangle political concept confounds. Detection tasks validate the semantic meaning of the vectors and show good generalization and robustness in OOD settings. Intervention Experiments show these vectors can intervene in LLMs to generate responses with different political leanings.

Speech emotion recognition (SER) systems often exhibit gender bias. However, the effectiveness and robustness of existing debiasing methods in such multi-label scenarios remain underexplored. To address this gap, we present EMO-Debias, a large-scale comparison of 13 debiasing methods applied to multi-label SER. Our study encompasses techniques from pre-processing, regularization, adversarial learning, biased learners, and distributionally robust optimization. Experiments conducted on acted and naturalistic emotion datasets, using WavLM and XLSR representations, evaluate each method under conditions of gender imbalance. Our analysis quantifies the trade-offs between fairness and accuracy, identifying which approaches consistently reduce gender performance gaps without compromising overall model performance. The findings provide actionable insights for selecting effective debiasing strategies and highlight the impact of dataset distributions.

This paper examines the significance of weak permissions in criminal trials (\emph{judicial permission}). It introduces a dialogue game model to systematically address judicial permissions, considering different standards of proof and argumentation semantics.

The NLP research community has made publicly available numerous instruments for measuring representational harms caused by large language model (LLM)-based systems. These instruments have taken the form of datasets, metrics, tools, and more. In this paper, we examine the extent to which such instruments meet the needs of practitioners tasked with evaluating LLM-based systems. Via semi-structured interviews with 12 such practitioners, we find that practitioners are often unable to use publicly available instruments for measuring representational harms. We identify two types of challenges. In some cases, instruments are not useful because they do not meaningfully measure what practitioners seek to measure or are otherwise misaligned with practitioner needs. In other cases, instruments - even useful instruments - are not used by practitioners due to practical and institutional barriers impeding their uptake. Drawing on measurement theory and pragmatic measurement, we provide recommendations for addressing these challenges to better meet practitioner needs.

Elon Musk quickly began pulling the plug on federal projects amid an escalating feud with Donald Trump, but the high-stakes clash now further threatens the most prized asset in his empire. With Tesla shares in freefall and SpaceX contracts on the line, the ambitious megaproject Musk most needs to compete in the AI race could become collateral in the explosive back-and-forth. Built in Tennessee, the supercomputer Colossus powers Musk's artificial intelligence company, xAI. The vast facility cost an estimated 4 billion and Musk plans to spend tens of billions more expanding it in a bid to challenge AI giants OpenAI and Google. However, it is already mired in an explosive backlash that the president could seize on.