Daily Papers

Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude larger than existing public datasets. A total of 10 types of jets are simulated, including several types unexplored for tagging so far. Based on the large dataset, we propose a new Transformer-based architecture for jet tagging, called Particle Transformer (ParT). By incorporating pairwise particle interactions in the attention mechanism, ParT achieves higher tagging performance than a plain Transformer and surpasses the previous state-of-the-art, ParticleNet, by a large margin. The pre-trained ParT models, once fine-tuned, also substantially enhance the performance on two widely adopted jet tagging benchmarks. The dataset, code and models are publicly available at https://github.com/jet-universe/particle_transformer.

Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mimic humor but convey Islamophobic sentiments. This work presents a novel dataset and proposes a classifier based on the Vision-and-Language Transformer (ViLT) specifically tailored to identify anti-Muslim hate within memes by integrating both visual and textual representations. Our model leverages joint modal embeddings between meme images and incorporated text to capture nuanced Islamophobic narratives that are unique to meme culture, providing both high detection accuracy and interoperability.

Multimodal Large Language Models (MLLMs) are increasingly used for content moderation, yet their robustness in short-form video contexts remains underexplored. Current safety evaluations often rely on unimodal attacks, failing to address combined attack vulnerabilities. In this paper, we introduce a comprehensive framework for evaluating the tri-modal safety of MLLMs. First, we present the Short-Video Multimodal Adversarial (SVMA) dataset, comprising diverse short-form videos with human-guided synthetic adversarial attacks. Second, we propose ChimeraBreak, a novel tri-modal attack strategy that simultaneously challenges visual, auditory, and semantic reasoning pathways. Extensive experiments on state-of-the-art MLLMs reveal significant vulnerabilities with high Attack Success Rates (ASR). Our findings uncover distinct failure modes, showing model biases toward misclassifying benign or policy-violating content. We assess results using LLM-as-a-judge, demonstrating attack reasoning efficacy. Our dataset and findings provide crucial insights for developing more robust and safe MLLMs.

Audio visual segmentation (AVS) aims to segment the sounding objects for each frame of a given video. To distinguish the sounding objects from silent ones, both audio-visual semantic correspondence and temporal interaction are required. The previous method applies multi-frame cross-modal attention to conduct pixel-level interactions between audio features and visual features of multiple frames simultaneously, which is both redundant and implicit. In this paper, we propose an Audio-Queried Transformer architecture, AQFormer, where we define a set of object queries conditioned on audio information and associate each of them to particular sounding objects. Explicit object-level semantic correspondence between audio and visual modalities is established by gathering object information from visual features with predefined audio queries. Besides, an Audio-Bridged Temporal Interaction module is proposed to exchange sounding object-relevant information among multiple frames with the bridge of audio features. Extensive experiments are conducted on two AVS benchmarks to show that our method achieves state-of-the-art performances, especially 7.1% M_J and 7.6% M_F gains on the MS3 setting.

While recent advancements in large language model (LLM) alignment have enabled the effective identification of malicious objectives involving scene nesting and keyword rewriting, our study reveals that these methods remain inadequate at detecting malicious objectives expressed through context within nested harmless objectives. This study identifies a previously overlooked vulnerability, which we term Attack via Implicit Reference (AIR). AIR decomposes a malicious objective into permissible objectives and links them through implicit references within the context. This method employs multiple related harmless objectives to generate malicious content without triggering refusal responses, thereby effectively bypassing existing detection techniques.Our experiments demonstrate AIR's effectiveness across state-of-the-art LLMs, achieving an attack success rate (ASR) exceeding 90% on most models, including GPT-4o, Claude-3.5-Sonnet, and Qwen-2-72B. Notably, we observe an inverse scaling phenomenon, where larger models are more vulnerable to this attack method. These findings underscore the urgent need for defense mechanisms capable of understanding and preventing contextual attacks. Furthermore, we introduce a cross-model attack strategy that leverages less secure models to generate malicious contexts, thereby further increasing the ASR when targeting other models.Our code and jailbreak artifacts can be found at https://github.com/Lucas-TY/llm_Implicit_reference.

Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multi-network deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions for classified objects in each of them. It also provides redshift errors, using a network-of-networks that reproduces a Monte Carlo test on each spectrum, by randomizing their weight initialization. As a demonstration of the capability of the deep learning pipeline, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4% average classification accuracy over the 13 classes (larger than 90% for the majority of them), and an average redshift error of approximately 0.23% for galaxies and 2.1% for quasars. We further train/test the same pipeline to classify spectra and predict redshifts for a sample of 200k 4MOST mock spectra and 21k publicly released DESI spectra. On 4MOST mock data, we reach 93.4% accuracy in 10-class classification and an average redshift error of 0.55% for galaxies and 0.3% for active galactic nuclei. On DESI data, we reach 96% accuracy in (star/galaxy/quasar only) classification and an average redshift error of 2.8% for galaxies and 4.8% for quasars, despite the small sample size available. GaSNet-II can process ~40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses of Stage-IV survey observations and an ideal tool for feedback loops aimed at night-by-night survey strategy optimization.

Recently the author presented a new approach to solving the coefficient problems for various classes of holomorphic functions f(z) = sumlimits_0^infty c_n z^n, not necessarily univalent. This approach is based on lifting the given polynomial coefficient functionals J(f) = J(c_{m_1}, dots, c_{m_s}), 2 < c_{m_1} < dots < c_{m_s} < infty, onto the Bers fiber space over universal Teichmuller space and applying the analytic and geometric features of Teichm\"{u}ller spaces, especially the Bers isomorphism theorem for Teichmuller spaces of punctured Riemann surfaces. In this paper, we extend this approach to more general classes of functions. In particular, this provides a strengthening of de Branges' theorem solving the Bieberbach conjecture.

The use of language models for automatically evaluating long-form text (LLM-as-a-judge) is becoming increasingly common, yet most LLM judges are optimized exclusively for English, with strategies for enhancing their multilingual evaluation capabilities remaining largely unexplored in the current literature. This has created a disparity in the quality of automatic evaluation methods for non-English languages, ultimately hindering the development of models with better multilingual capabilities. To bridge this gap, we introduce M-Prometheus, a suite of open-weight LLM judges ranging from 3B to 14B parameters that can provide both direct assessment and pairwise comparison feedback on multilingual outputs. M-Prometheus models outperform state-of-the-art open LLM judges on multilingual reward benchmarks spanning more than 20 languages, as well as on literary machine translation (MT) evaluation covering 4 language pairs. Furthermore, M-Prometheus models can be leveraged at decoding time to significantly improve generated outputs across all 3 tested languages, showcasing their utility for the development of better multilingual models. Lastly, through extensive ablations, we identify the key factors for obtaining an effective multilingual judge, including backbone model selection and training on natively multilingual feedback data instead of translated data. We release our models, training dataset, and code.

JWST has detected many overmassive galactic systems at z > 4, where the mass of the black hole, M_bullet, is 10-100 times larger than expected from local relations, given the host's stellar mass, M_star. This Letter presents a model to describe these overmassive systems in the high-z Universe. We suggest that the black hole mass is the main driver of high-z star formation quenching. SMBHs globally impact their high-z galaxies because their hosts are physically small, and the black holes have duty cycles close to unity at z > 4. In this regime, we assume that black hole mass growth is regulated by the quasar's output, while stellar mass growth is quenched by it and uncorrelated to the global properties of the host halo. We find that the ratio M_bullet/M_star controls the average star formation efficiency: if M_bullet/M_star > 8times 10^{18} (n Lambda/f_{edd})[(Omega_b M_h)/(Omega_m M_star) - 1], then the galaxy is unable to form stars efficiently. Once this ratio exceeds the threshold, a runaway process brings the originally overmassive system towards the local M_bullet - M_star relation. Furthermore, the M_bullet - M_star relation evolves with redshift as propto (1+z)^{5/2}. At z sim 5, we find an overmassive factor of sim 55, in excellent agreement with current JWST data and the high-z relation inferred from those. Extending the black hole horizon farther in redshift and lower in mass will test this model and improve our understanding of the early co-evolution of black holes and galaxies.

In this work we investigate neutron stars (NS) in f(R,L_m) theory of gravity for the case f(R,L_m) = R + L_m + sigmaRL_m, where R is the Ricci scalar and L_m the Lagrangian matter density. In the term sigmaRL_m, sigma represents the coupling between the gravitational and particles fields. For the first time the hydrostatic equilibrium equations in the theory are solved considering realistic equations of state and NS masses and radii obtained are subject to joint constrains from massive pulsars, the gravitational wave event GW170817 and from the PSR J0030+0451 mass-radius from NASA's Neutron Star Interior Composition Explorer ({it NICER}) data. We show that in this theory of gravity, the mass-radius results can accommodate massive pulsars, while the general theory of relativity can hardly do it. The theory also can explain the observed NS within the radius region constrained by the GW170817 and PSR J0030+0451 observations for masses around 1.4~M_{odot}.

This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions. Considering the principles respected in any society, TAI is often characterized by a few attributes, some of which have led to confusion in regulatory or engineering contexts. We argue against using terms such as Responsible or Ethical AI as substitutes for TAI. And to help clarify any confusion, we suggest leaving them behind. Given the subjectivity and complexity inherent in TAI, developing a universal framework is deemed infeasible. Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability. We examine the ongoing regulatory landscape, with a focus on initiatives in the EU, China, and the USA. We recognize that differences in AI regulations based on geopolitical and geographical reasons pose an additional challenge for multinational companies. We identify risk as a core factor in AI regulation and TAI. For example, as outlined in the EU-AI Act, organizations must gauge the risk level of their AI products to act accordingly (or risk hefty fines). We compare modalities of TAI implementation and how multiple cross-functional teams are engaged in the overall process. Thus, a brute force approach for enacting TAI renders its efficiency and agility, moot. To address this, we introduce our framework Set-Formalize-Measure-Act (SFMA). Our solution highlights the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests. Finally, over-regulation driven by panic of powerful AI models can, in fact, harm TAI too. Based on GitHub user-activity data, in 2023, AI open-source projects rose to top projects by contributor account. Enabling innovation in TAI hinges on the independent contributions of the open-source community.

Carbonaceous nano-grains are a significant component of interstellar dust and dominate the mid-infrared emission of photodissociation regions (PDRs). We study the evolution of nano-grains across the illuminated edge of the Horsehead PDR, especially their abundance and size properties. This work is part of the Physics and Chemistry of PDR Fronts program studying dust and gas in PDRs with JWST. We use NIRCam+MIRI photometric bands and NIRSpec+MRS spectroscopy to map the illuminated edge. We model dust emission using the THEMIS dust model with the SOC radiative transfer code. Detailed modeling of high angular resolution JWST data allows us to obtain constraints on nano-grain properties. We find that diffuse ISM dust cannot account for the observed data, requiring evolved grains. A sharp density increase is observed at the illuminated edge, consistent with ALMA observations revealing a sharp transition between molecular and ionized gas. Although the PDR length could not be directly determined, we estimate an upper limit of approximately 0.015 pc. This implies a lower limit on small grain abundance (greater than 0.003), showing small grains are not depleted at the Horsehead edge, unlike in the Orion Bar. Our findings indicate a high-density environment and less steep size distribution for nano-grains at the illuminated edge versus the diffuse ISM. This implies nano-grain destruction mechanisms might be less efficient in the Horsehead's moderate-UV field than in more intense PDRs. These results support a model where nano-grain population recovery is slower in moderate-UV environments, leading to a unique dust size distribution at the edge of the Horsehead Nebula.

Recent advancements in instruction fine-tuning, alignment methods such as reinforcement learning from human feedback (RLHF), and optimization techniques like direct preference optimization (DPO) have significantly enhanced the adaptability of large language models (LLMs) to user preferences. However, despite these innovations, many LLMs continue to exhibit biases toward Western, Anglo-centric, or American cultures, with performance on English data consistently surpassing that of other languages. This reveals a persistent cultural gap in LLMs, which complicates their ability to accurately process culturally rich and diverse figurative language such as proverbs. To address this, we introduce Jawaher, a benchmark designed to assess LLMs' capacity to comprehend and interpret Arabic proverbs. Jawaher includes proverbs from various Arabic dialects, along with idiomatic translations and explanations. Through extensive evaluations of both open- and closed-source models, we find that while LLMs can generate idiomatically accurate translations, they struggle with producing culturally nuanced and contextually relevant explanations. These findings highlight the need for ongoing model refinement and dataset expansion to bridge the cultural gap in figurative language processing.

Our understanding of the physical properties of star-forming galaxies during the Epoch of Reionization (EoR, at z > 6) suffers from degeneracies among the apparent properties of the stars, the nebular gas, and the dust. These degeneracies are most prominent with photometry, which has insufficient (1) spectral resolution and (2) rest-frame spectral coverage. We explore ways to break these degeneracies with a sample of N = 22 high-redshift star-forming galaxies at 7 < z_{phot} leq 9, using some of the deepest existing imaging from JWST/NIRCam and JWST/MIRI with JADES. Key to this study is the imaging from JWST/MIRI at 7.7 mum, which provides coverage of the rest-frame I-band at the observed redshifts. We infer stellar population properties and rest-frame colors using a variety of filter sets and star formation history assumptions to explore the impact of these choices. Evaluating these quantities both with and without the 7.7 mum data point shows that dense spectral coverage with JWST/NIRCam (eight or more filters, including at least one medium-band) can compensate for lacking the rest-frame I-band coverage for the vast majority (approx 80%) of our sample. Furthermore, these galaxy properties are most consistently determined by assuming the delayed-tau star formation history, which provides the smallest offsets and scatters around these offsets when including JWST/MIRI. Within extragalactic surveys like JADES and CEERS, our findings suggest that robust characterization of the stellar population properties and rest-frame colors for high-redshift star-forming galaxies is possible with JWST/NIRCam alone at z approx 8.

In remote control systems, transmitting large data volumes (e.g. video feeds) from wireless sensors to faraway controllers is challenging when the uplink channel capacity is limited (e.g. RedCap devices or massive wireless sensor networks). Furthermore, the controllers often only need the information-rich components of the original data. To address this, we propose a Time-Series Joint Embedding Predictive Architecture (TS-JEPA) and a semantic actor trained through self-supervised learning. This approach harnesses TS-JEPA's semantic representation power and predictive capabilities by capturing spatio-temporal correlations in the source data. We leverage this to optimize uplink channel utilization, while the semantic actor calculates control commands directly from the encoded representations, rather than from the original data. We test our model through multiple parallel instances of the well-known inverted cart-pole scenario, where the approach is validated through the maximization of stability under constrained uplink channel capacity.

Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Therefore, the ability to extract meaningful embeddings to represent players is crucial in developing an effective tracking and re-identification system. In this paper, a multi-purpose part-based person representation method, called PRTreID, is proposed that performs three tasks of role classification, team affiliation, and re-identification, simultaneously. In contrast to available literature, a single network is trained with multi-task supervision to solve all three tasks, jointly. The proposed joint method is computationally efficient due to the shared backbone. Also, the multi-task learning leads to richer and more discriminative representations, as demonstrated by both quantitative and qualitative results. To demonstrate the effectiveness of PRTreID, it is integrated with a state-of-the-art tracking method, using a part-based post-processing module to handle long-term tracking. The proposed tracking method outperforms all existing tracking methods on the challenging SoccerNet tracking dataset.

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches.

The near-infrared (NIR) emission of the youngest protostars still needs to be characterized to better understand the evolution of their accretion and ejection activity. We analyze James Webb Space Telescope NIRSpec 1.7 -- 5.3 mum observations of two deeply embedded sources in the S68N protostellar core in Serpens. The North Central (NC) source exhibits a highly obscured spectrum (A_K ~ 4.8 mag) that is modeled with a pre-main-sequence photosphere and a hot disk component. The photospheric parameters are consistent with a young, low-mass photosphere, as suggested by the low surface gravity, log g of 1.95 pm 0.15 cm s^{-2}. The hot disk suggests that accretion onto the central protostellar embryo is ongoing, although prototypical accretion-tracing emission lines HI are not detected. The South Central (SC) source, which is even more embedded (A_K ~ 8 mag; no continuum is detected shortward of 3.6 mum) appears to be driving the large-scale S68N protostellar outflow, and launches a collimated hot molecular jet detected in \Ht and CO ro-vibrational lines. Shock modeling of the \Ht (ro)vibrational lines establishes that fast C-type shocks (geq 30 km s^{-1}), with high pre-shock density (geq 10^7 cm^{-3}), and strong magnetic field (b ~ 3--10, where B = b,times,textrm{n_{H} (cm^{-3})},muG) best match the data. The bright CO fundamental line forest suggests energetic excitation, with the contribution of non-LTE effects, ie irradiation pumping. Detected OH and CH^{+} ro-vibrational lines support this hypothesis. These two Class 0 protostars seem to be in very young evolutionary stages and still have to acquire the bulk of their final stellar masses. These results demonstrate that JWST enables unprecedented diagnostics of these first stages of the protostellar evolutionary phase.

Reward models (RMs) have driven the state-of-the-art performance of LLMs today by enabling the integration of human feedback into the language modeling process. However, RMs are primarily trained and evaluated in English, and their capabilities in multilingual settings remain largely understudied. In this work, we conduct a systematic evaluation of several reward models in multilingual settings. We first construct the first-of-its-kind multilingual RM evaluation benchmark, M-RewardBench, consisting of 2.87k preference instances for 23 typologically diverse languages, that tests the chat, safety, reasoning, and translation capabilities of RMs. We then rigorously evaluate a wide range of reward models on M-RewardBench, offering fresh insights into their performance across diverse languages. We identify a significant gap in RMs' performances between English and non-English languages and show that RM preferences can change substantially from one language to another. We also present several findings on how different multilingual aspects impact RM performance. Specifically, we show that the performance of RMs is improved with improved translation quality. Similarly, we demonstrate that the models exhibit better performance for high-resource languages. We release M-RewardBench dataset and the codebase in this study to facilitate a better understanding of RM evaluation in multilingual settings.

Recent advancements in large language models (LLMs) have given rise to the LLM-as-a-judge paradigm, showcasing their potential to deliver human-like judgments. However, in the field of machine translation (MT) evaluation, current LLM-as-a-judge methods fall short of learned automatic metrics. In this paper, we propose Multidimensional Multi-Agent Debate (M-MAD), a systematic LLM-based multi-agent framework for advanced LLM-as-a-judge MT evaluation. Our findings demonstrate that M-MAD achieves significant advancements by (1) decoupling heuristic MQM criteria into distinct evaluation dimensions for fine-grained assessments; (2) employing multi-agent debates to harness the collaborative reasoning capabilities of LLMs; (3) synthesizing dimension-specific results into a final evaluation judgment to ensure robust and reliable outcomes. Comprehensive experiments show that M-MAD not only outperforms all existing LLM-as-a-judge methods but also competes with state-of-the-art reference-based automatic metrics, even when powered by a suboptimal model like GPT-4o mini. Detailed ablations and analysis highlight the superiority of our framework design, offering a fresh perspective for LLM-as-a-judge paradigm. Our code and data are publicly available at https://github.com/SU-JIAYUAN/M-MAD.

In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading technologies for processing structured data, especially graph data. Traditional GNNs often face challenges in capturing information from distant vertices effectively. In contrast, Graph Transformer models are particularly adept at managing long-distance node relationships. Despite these advantages, Graph Transformer models still encounter issues with computational and storage efficiency when scaled to large graph datasets. To address these challenges, we propose an innovative Graph Neural Network (GNN) architecture that integrates a Top-m attention mechanism aggregation component and a neighborhood aggregation component, effectively enhancing the model's ability to aggregate relevant information from both local and extended neighborhoods at each layer. This method not only improves computational efficiency but also enriches the node features, facilitating a deeper analysis of complex graph structures. Additionally, to assess the effectiveness of our proposed model, we have applied it to citation sentiment prediction, a novel task previously unexplored in the GNN field. Accordingly, we constructed a dedicated citation network, ArXivNet. In this dataset, we specifically annotated the sentiment polarity of the citations (positive, neutral, negative) to enable in-depth sentiment analysis. Our approach has shown superior performance across a variety of tasks including vertex classification, link prediction, sentiment prediction, graph regression, and visualization. It outperforms existing methods in terms of effectiveness, as demonstrated by experimental results on multiple datasets.

Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes, finding applications in various domains. To achieve the personalization capability, existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset, which can be non-trivial for general users, resource-intensive, and time-consuming. Despite attempts to develop finetuning-free methods, their generation quality is much lower compared to their finetuning counterparts. In this paper, we propose Joint-Image Diffusion (\jedi), an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning, we propose a scalable synthetic dataset generation technique. Once trained, our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality, both quantitatively and qualitatively, significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines.

This paper addresses the problem of sentiment analysis for Jopara, a code-switching language between Guarani and Spanish. We first collect a corpus of Guarani-dominant tweets and discuss on the difficulties of finding quality data for even relatively easy-to-annotate tasks, such as sentiment analysis. Then, we train a set of neural models, including pre-trained language models, and explore whether they perform better than traditional machine learning ones in this low-resource setup. Transformer architectures obtain the best results, despite not considering Guarani during pre-training, but traditional machine learning models perform close due to the low-resource nature of the problem.

We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment. Previous computational studies of job-related phenomena have used corpora collected from workplace social media that are hosted internally by the employers, and so lacks independence from latent job-related coercion and the broader context that an open domain, general-purpose medium such as Twitter provides. Our new corpus promises to be a benchmark for the extraction of job-related topics and advanced analysis and modeling, and can potentially benefit a wide range of research communities in the future.

We present a consistent Kaluza-Klein truncation of D=11 supergravity on an arbitrary seven-dimensional Sasaki-Einstein space (SE_7) to a D=4 theory containing a metric, a gauge-field, a complex scalar field and a real scalar field. We use this D=4 theory to construct various black hole solutions that describe the thermodynamics of the d=3 CFTs dual to skew-whiffed AdS_4 X SE_7 solutions. We show that these CFTs have a rich phase diagram, including holographic superconductivity with, generically, broken parity and time reversal invariance. At zero temperature the superconducting solutions are charged domain walls with a universal emergent conformal symmetry in the far infrared.

We present an assessment of the effects of stellar wind magnetic and mechanical components on the habitability of Earth-like exoplanets orbiting the inner and outer radii of the habitable zone (HZ) of M dwarfs. We consider stars with masses in the range of 0.09 - 0.75 M_odot and planets with a surface dipolar magnetic field of 0.5 G. We estimate the size of the magnetospheres of such exoplanets using the pressure balance equation including the contribution of magnetic and ram pressures from stellar winds. We explore different scenarios, including fast and slow stellar winds, to assess the relevance of kinetic contribution. Furthermore, the effect of tidal locking and potential deviations from the Parker spiral, typically used to describe the interplanetary magnetic field, are analyzed. We show that for low mass stars (M < 0.15 M_odot), the ram pressure exerted by stellar winds affects the size of the magnetosphere more than the stellar wind magnetic pressure. Interestingly, when the ram pressure is not much stronger than the magnetic pressure, typically for higher mass stars, the inclusion of ram pressure can be beneficial to the magnetosphere due to the magnetopause currents. A magnetosphere with the size of that of modern Earth is difficult to achieve with the current assumptions. However, an early Earth magnetosphere is achieved by roughly half of our hypothetical planets orbiting the outer radius of the HZ in most of the considered cases. We find that deviations from the Parker spiral can affect the results significantly, reducing the magnetosphere by 56% in extreme cases. Most of the hypothetical planets are most likely (or might be) tidally locked, with the notable exception of those orbiting the outer HZ of GJ 846 and V1005 Ori.

The paradigm of LLM-as-a-judge is emerging as a scalable and efficient alternative to human evaluation, demonstrating strong performance on well-defined tasks. However, its reliability in open-ended tasks with dynamic environments and complex interactions remains unexplored. To bridge the gap, we introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development, with support for both non-interactive evaluation based on static observations and continuous interactive evaluation with a dynamic web environment. WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics to ensure high-quality ground truth. Using this benchmark, we comprehensively evaluate various evaluators, including LLMs, MLLMs, and agentic workflows. We systematically investigate the impact of different paradigms and guidance mechanisms. Our experiments reveal a significant gap between LLM judges and human experts. In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias. Overall, WebDevJudge presents a significant challenge to LLM-as-a-judge, offering insights to guide future research toward developing more reliable and capable automated evaluators for complicated scenarios. Code and data are available at https://github.com/lcy2723/WebDevJudge.

Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-augmented LLMs hold tremendous promise for automating the generation of such computational plans. However, the lack of standardized benchmarks for evaluating LLMs as planners for multi-step multi-modal tasks has prevented a systematic study of planner design decisions. Should LLMs generate a full plan in a single shot or step-by-step? Should they invoke tools directly with Python code or through structured data formats like JSON? Does feedback improve planning? To answer these questions and more, we introduce m&m's: a benchmark containing 4K+ multi-step multi-modal tasks involving 33 tools that include multi-modal models, (free) public APIs, and image processing modules. For each of these task queries, we provide automatically generated plans using this realistic toolset. We further provide a high-quality subset of 1,565 task plans that are human-verified and correctly executable. With m&m's, we evaluate 6 popular LLMs with 2 planning strategies (multi-step vs. step-by-step planning), 2 plan formats (JSON vs. code), and 3 types of feedback (parsing/verification/execution). Finally, we summarize takeaways from our extensive experiments. Our dataset and code are available on HuggingFace (https://huggingface.co/datasets/zixianma/mnms) and Github (https://github.com/RAIVNLab/mnms).

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr.

Working adults spend nearly one third of their daily time at their jobs. In this paper, we study job-related social media discourse from a community of users. We use both crowdsourcing and local expertise to train a classifier to detect job-related messages on Twitter. Additionally, we analyze the linguistic differences in a job-related corpus of tweets between individual users vs. commercial accounts. The volumes of job-related tweets from individual users indicate that people use Twitter with distinct monthly, daily, and hourly patterns. We further show that the moods associated with jobs, positive and negative, have unique diurnal rhythms.

We present a 70ks Chandra observation of the radio galaxy 3C293. This galaxy belongs to the class of molecular hydrogen emission galaxies (MOHEGs) that have very luminous emission from warm molecular hydrogen. In radio galaxies, the molecular gas appears to be heated by jet-driven shocks, but exactly how this mechanism works is still poorly understood. With Chandra, we observe X-ray emission from the jets within the host galaxy and along the 100 kpc radio jets. We model the X-ray spectra of the nucleus, the inner jets, and the X-ray features along the extended radio jets. Both the nucleus and the inner jets show evidence of 10^7 K shock-heated gas. The kinetic power of the jets is more than sufficient to heat the X-ray emitting gas within the host galaxy. The thermal X-ray and warm H2 luminosities of 3C293 are similar, indicating similar masses of X-ray hot gas and warm molecular gas. This is consistent with a picture where both derive from a multiphase, shocked interstellar medium (ISM). We find that radio-loud MOHEGs that are not brightest cluster galaxies (BCGs), like 3C293, typically have LH2/LX~1 and MH2/MX~1, whereas MOHEGs that are BCGs have LH2/LX~0.01 and MH2/MX~0.01. The more massive, virialized, hot atmosphere in BCGs overwhelms any direct X-ray emission from current jet-ISM interaction. On the other hand, LH2/LX~1 in the Spiderweb BCG at z=2, which resides in an unvirialized protocluster and hosts a powerful radio source. Over time, jet-ISM interaction may contribute to the establishment of a hot atmosphere in BCGs and other massive elliptical galaxies.

The stellar structures of star-forming galaxies (SFGs) undergo significant size growth during their mass assembly and must pass through a compaction phase as they evolve into quiescent galaxies (QGs). To shed light on the mechanisms behind this structural evolution, we study the morphology of the star-forming components of 665 SFGs at 0<z<2.5 measured using JWST/MIRI observation and compare them with the morphology of their stellar components taken from the literature. The stellar and star-forming components of most SFGs (66%) have extended disk-like structures that are aligned with each other and are of the same size. The star-forming components of these galaxies follow a mass-size relation, similar to that followed by their stellar components. At the highest mass, the optical S\'ersic index of these SFGs increases to 2.5, suggesting the presence of a dominant stellar bulge. Because their star-forming components remain disk-like, these bulges cannot have formed by secular in-situ growth. We identify a second population of galaxies lying below the MIR mass-size relation, with compact star-forming components embedded in extended stellar components (EC galaxy). These galaxies are overall rare (15%) but become more dominant (30%) at high mass (>10^{10.5}M_odot). The compact star-forming components of these galaxies are also concentrated and slightly spheroidal, suggesting that this compaction phase can build dense bulge in-situ. Finally, we identify a third population of SFGs (19%), with both compact stellar and star-forming components. The density of their stellar cores resemble those of QGs and are compatible with being the descendants of EC galaxy. Overall, the structural evolution of SFGs is mainly dominated by a secular inside-out growth, which can, however, be interrupted by violent compaction phase(s) that can build dominant stellar bulges like those in massive SFGs or QGs.

Image restoration is a low-level vision task which is to restore degraded images to noise-free images. With the success of deep neural networks, the convolutional neural networks surpass the traditional restoration methods and become the mainstream in the computer vision area. To advance the performanceof denoising algorithms, we propose a blind real image denoising network (SRMNet) by employing a hierarchical architecture improved from U-Net. Specifically, we use a selective kernel with residual block on the hierarchical structure called M-Net to enrich the multi-scale semantic information. Furthermore, our SRMNet has competitive performance results on two synthetic and two real-world noisy datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/TentativeGitHub/SRMNet.

Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://github.com/FanChiMao/HWMNet.

We investigate the correlation between stellar mass (M*) and star formation rate (SFR) across the stellar mass range log10(M*/Msun)~6-11. We consider almost 50,000 star-forming galaxies at z~3-7, leveraging data from COSMOS/SMUVS, JADES/GOODS-SOUTH, and MIDIS/XDF. This is the first study spanning such a wide stellar mass range without relying on gravitational lensing effects. We locate our galaxies on the SFR-M* plane to assess how the location of galaxies in the star-formation main sequence (MS) and starburst (SB) region evolves with stellar mass and redshift. We find that the two star-forming modes tend to converge at log10(M*/Msun) < 7, with all galaxies found in the SB mode. However, deeper observations will be instrumental for reaching lower SFRs and Msun to further validate this scenario. By dissecting our galaxy sample in stellar mass and redshift, we show that the emergence of the star-formation MS is stellar-mass dependent: while in galaxies with log10(M*/Msun) > 9 the MS is already well in place at z = 5-7, for galaxies with log10(M*/Msun)~7-8 it only becomes significant at z<4. Overall, our results are in line with previous findings that the SB mode dominates amongst low stellar-mass galaxies. The earlier emergence of the MS for massive galaxies is consistent with galaxy downsizing.

Technical debt (TD) is a term used to describe the additional work and costs that emerge when developers have opted for a quick and easy solution to a problem, rather than a more effective and well-designed, but time-consuming approach. Self-Admitted Technical Debts (SATDs) are a specific type of technical debts that developers intentionally document and acknowledge, typically via textual comments. While these self-admitted comments are a useful tool for identifying technical debts, most of the existing approaches focus on capturing crucial tokens associated with various categories of TD, neglecting the rich information embedded within the source code itself. Recent research has focused on detecting SATDs by analyzing comments embedded in source code, and there has been little work dealing with technical debts contained in the source code. To fill such a gap, in this study, through the analysis of comments and their associated source code from 974 Java projects hosted in the Stack corpus, we curated the first ever dataset of TD identified by code comments, coupled with its associated source code. Through an empirical evaluation, we found out that the comments of the resulting dataset help enhance the prediction performance of state-of-the-art SATD detection models. More importantly, including the classified source code significantly improves the accuracy in predicting various types of technical debt. In this respect, our work is two-fold: (i) We believe that our dataset will catalyze future work in the domain, inspiring various research issues related to the recognition of technical debt; (ii) The proposed classifiers may serve as baselines for other studies on the detection of TD by means of the curated dataset.

Equipping large language models (LLMs) with latent-space memory has attracted increasing attention as they can extend the context window of existing language models. However, retaining information from the distant past remains a challenge. For example, MemoryLLM (Wang et al., 2024a), as a representative work with latent-space memory, compresses past information into hidden states across all layers, forming a memory pool of 1B parameters. While effective for sequence lengths up to 16k tokens, it struggles to retain knowledge beyond 20k tokens. In this work, we address this limitation by introducing M+, a memory-augmented model based on MemoryLLM that significantly enhances long-term information retention. M+ integrates a long-term memory mechanism with a co-trained retriever, dynamically retrieving relevant information during text generation. We evaluate M+ on diverse benchmarks, including long-context understanding and knowledge retention tasks. Experimental results show that M+ significantly outperforms MemoryLLM and recent strong baselines, extending knowledge retention from under 20k to over 160k tokens with similar GPU memory overhead.

We investigate a critical yet under-explored question in Large Vision-Language Models (LVLMs): Do LVLMs genuinely comprehend interleaved image-text in the document? Existing document understanding benchmarks often assess LVLMs using question-answer formats, which are information-sparse and difficult to guarantee the coverage of long-range dependencies. To address this issue, we introduce a novel and challenging Multimodal Document Summarization Benchmark (M-DocSum-Bench), which comprises 500 high-quality arXiv papers, along with interleaved multimodal summaries aligned with human preferences. M-DocSum-Bench is a reference-based generation task and necessitates the generation of interleaved image-text summaries using provided reference images, thereby simultaneously evaluating capabilities in understanding, reasoning, localization, and summarization within complex multimodal document scenarios. To facilitate this benchmark, we develop an automated framework to construct summaries and propose a fine-grained evaluation method called M-DocEval. Moreover, we further develop a robust summarization baseline, i.e., M-DocSum-7B, by progressive two-stage training with diverse instruction and preference data. The extensive results on our M-DocSum-Bench reveal that the leading LVLMs struggle to maintain coherence and accurately integrate information within long and interleaved contexts, often exhibiting confusion between similar images and a lack of robustness. Notably, M-DocSum-7B achieves state-of-the-art performance compared to larger and closed-source models (including GPT-4o, Gemini Pro, Claude-3.5-Sonnet and Qwen2.5-VL-72B, etc.), demonstrating the potential of LVLMs for improved interleaved image-text understanding. The code, data, and models are available at https://github.com/stepfun-ai/M-DocSum-Bench.

De novo drug design requires simultaneously generating novel molecules outside of training data and predicting their target properties, making it a hard task for generative models. To address this, we propose Joint Transformer that combines a Transformer decoder, Transformer encoder, and a predictor in a joint generative model with shared weights. We formulate a probabilistic black-box optimization algorithm that employs Joint Transformer to generate novel molecules with improved target properties and outperforms other SMILES-based optimization methods in de novo drug design.

We present the stellar mass-metallicity relation (MZR) and mass-metallicity-star formation relation ("fundamental metallicity relation"; FMR) of 18 massive (log(M/M_odot) = 9.5-11) main-sequence galaxies at z~5 from the ALPINE-CRISTAL-JWST sample. This sample complements recent studies by JWST at up to two orders of magnitude lower stellar masses. The metallicities are derived using strong optical lines, and verified by temperature-based oxygen abundance measurements for five galaxies for which faint auroral lines are detected. We find little evolution at the massive end of the MZR between z~5 and cosmic noon at z~2, suggesting a fast metal enrichment at early times. The FMR at z=5 exhibits a 5x larger scatter (preferentially to lower metallicities) compared the local FMR relation. This scatter can be explained by a bursty star formation and the direct build-up of metals in early galaxies as well as differences in age and outflow efficiencies. Capitalizing on all available samples, we find that the observed MZR and FMR over three orders of stellar mass is generally in good agreement with results from cosmological simulation, although some underestimate the metal enrichment at low stellar masses. This may be due to too efficient metal-rich outflows. We show that the ALPINE-CRISTAL-JWST galaxies likely joined the current FMR at z~10 and will evolve into massive (log(M/M_odot)~11.4) galaxies with super-solar metallicities by z=0.

Recent work with JWST has demonstrated its capability to identify and chemically characterize multiple populations in globular clusters down to the H-burning limit. In this study, we explore the kinematics of multiple populations in the globular cluster 47 Tucanae by combining data from JWST, HST, and Gaia. We analyzed velocity dispersion and anisotropy profiles from the cluster center out to sim10R_h. Our findings indicate that while 1G stars are isotropic, 2G stars are significantly radially anisotropic. These results align with the predictions of simulations of the dynamical evolution of clusters where 2G stars are initially more centrally concentrated than 1G stars. Furthermore, we subdivided the 2G population into two subpopulations: 2G_A and 2G_B, with the latter being more chemically extreme. We compared their dynamical profiles and found no significant differences. For the first time, we measured the degree of energy equipartition among the multiple populations of 47 Tucanae. Overall, within the analyzed radial range (sim2-4R_h), both populations exhibit a low degree of energy equipartition. The most significant differences between 1G and 2G stars are observed in the tangential velocity component, where 2G stars are characterized by a stronger degree of energy equipartition than 1G stars. In the radial component, the behavior of 1G and 2G stars is more variable, with differences largely dependent on radius. Finally, our analysis reveals that the ratio of rotational velocity to velocity dispersion is larger for the 2G population, while 1G stars exhibit higher skewness in their tangential proper motions, providing further evidence of differences in the kinematic properties of the 1G and 2G populations.

We present a spectroscopic analysis of two star-forming galaxies at z~5 observed with JWST/NIRSpec as part of the Early eXtragalactic Continuum and Emission Line Science (EXCELS) survey. The detection of the C III]lambdalambda1906,09, [O II]lambdalambda3726,29, [O III]lambdalambda4363,5007, and [N II]lambda6584 nebular emission lines enables investigation of the C/O, N/O, and C/N abundance ratios using the temperature-sensitive method. The two galaxies have stellar masses of log(M_{star}/M_{odot} ) = 8.13pm0.09 and log(M_{star}/M_{odot} )=8.52pm0.13 and corresponding metallicities of Z~0.2Z_{odot} and Z~0.3Z_{odot}. These metallicities are somewhat higher than is typical for other z>5 galaxies with similar stellar mass and are in fact comparable to high-redshift analogue galaxies at z~0. Both galaxies display evidence for N/O enhancement with respect to the z~0 sample, with log(N/O)=-1.07pm0.17 and log(N/O)=-0.86pm0.15 respectively. In contrast, we find low C abundances, with log(C/O)=-0.82pm0.22 and log(C/O)=-1.02pm0.22, consistent with the predicted yields of core-collapse supernovae. Following the trend observed in other high-redshift sources, we find that the C/N ratios are lower at fixed O/H compared to the majority of local galaxies. In contrast to the top-heavy IMF invoked in some studies to explain low C/N ratios in metal-poor galaxies, we find, via comparison to chemical evolution models, that a standard or bottom-heavy IMF better explains the observed abundance ratios in more enriched systems due to an increase in N-enrichment from intermediate mass (4-7M_{odot}) stars. Our results demonstrate that robust measurements of CNO abundances with JWST can reveal unique enrichment pathways in galaxies as a function of both metallicity and redshift.

We present new JWST-NIRSpec IFS data for the luminous infrared galaxy NGC7469: a nearby (70.6Mpc) active galaxy with a Sy 1.5 nucleus that drives a highly ionized gas outflow and a prominent nuclear star-forming ring. Using the superb sensitivity and high spatial resolution of the JWST instrument NIRSpec-IFS, we investigate the role of the Seyfert nucleus in the excitation and dynamics of the circumnuclear gas. Our analysis focuses on the [Fe ii], H2, and hydrogen recombination lines that trace the radiation/shocked-excited molecular and ionized ISM around the AGN. We investigate the gas excitation through H2/Br{\gamma} and [Fe ii]/Paeta emission line ratios and find that photoionization by the AGN dominates within the central 300 pc of the galaxy and together with a small region show ing signatures of shock-heated gas; these shock-heated regions are likely associated with a compact radio jet. In addition, the velocity field and velocity dispersion maps reveal complex gas kinematics. Rotation is the dominant feature, but we also identify non-circular motions consistent with gas inflows as traced by the velocity residuals and the spiral pattern in the Pa{\alpha} velocity dispersion map. The inflow is consistent with the mass outflow rate and two orders of magnitude higher than the AGN accretion rate. The compact nuclear radio jet has enough power to drive the highly ionized outflow. This scenario suggests that the inflow and outflow are in a self-regulating feeding-feedback process, with a contribution from the radio jet helping to drive the outflow.

We present results on the morphological and structural evolution of a total of 4265 galaxies observed with JWST at 1.5 < z < 8 in the JWST CEERS observations that overlap with the CANDELS EGS field. This is the biggest visually classified sample observed with JWST yet, sim20 times larger than previous studies, and allows us to examine in detail how galaxy structure has changed over this critical epoch. All sources were classified by six individual classifiers using a simple classification scheme aimed to produce disk/spheroid/peculiar classifications, whereby we determine how the relative number of these morphologies evolves since the Universe's first billion years. Additionally, we explore structural and quantitative morphology measurements using Morfometryka, and show that galaxies at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We find a strong dominance of morphologically selected disk galaxies up to z = 8, a far higher redshift than previously thought possible. We also find that the stellar mass and star formation rate densities are dominated by disk galaxies up to z sim 6, demonstrating that most stars in the universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we find is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classifications public for the community.

Pretrained language models underpin several AI applications, but their high computational cost for training limits accessibility. Initiatives such as BLOOM and StarCoder aim to democratize access to pretrained models for collaborative community development. However, such existing models face challenges: limited multilingual capabilities, continual pretraining causing catastrophic forgetting, whereas pretraining from scratch is computationally expensive, and compliance with AI safety and development laws. This paper presents Aurora-M, a 15B parameter multilingual open-source model trained on English, Finnish, Hindi, Japanese, Vietnamese, and code. Continually pretrained from StarCoderPlus on 435 billion additional tokens, Aurora-M surpasses 2 trillion tokens in total training token count. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. Aurora-M is rigorously evaluated across various tasks and languages, demonstrating robustness against catastrophic forgetting and outperforming alternatives in multilingual settings, particularly in safety evaluations. To promote responsible open-source LLM development, Aurora-M and its variants are released at https://huggingface.co/collections/aurora-m/aurora-m-models-65fdfdff62471e09812f5407 .

Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response to increasing input lengths, challenges memory and time efficiency. Unlike single-modality LLMs that manage only textual contexts, the KV cache of long-context MLLMs includes representations from multiple images with temporal and spatial relationships and related textual contexts. The predominance of image tokens means traditional optimizations for LLMs' KV caches are unsuitable for multimodal long-context settings, and no prior works have addressed this challenge. In this work, we introduce LOOK-M, a pioneering, fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache. We observe that during prompt prefill, the model prioritizes more textual attention over image features, and based on the multimodal interaction observation, a new proposed text-prior method is explored to compress the KV cache. Furthermore, to mitigate the degradation of image contextual information, we propose several compensatory strategies using KV pairs merging. LOOK-M demonstrates that with a significant reduction in KV Cache memory usage, such as reducing it by 80% in some cases, it not only achieves up to 1.5x faster decoding but also maintains or even enhances performance across a variety of long context multimodal tasks.

Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a novel benchmark construction framework that "distills" jailbreak attacks into high-quality and easily-updatable safety benchmarks. JBDistill utilizes a small set of development models and existing jailbreak attack algorithms to create a candidate prompt pool, then employs prompt selection algorithms to identify an effective subset of prompts as safety benchmarks. JBDistill addresses challenges in existing safety evaluation: the use of consistent evaluation prompts across models ensures fair comparisons and reproducibility. It requires minimal human effort to rerun the JBDistill pipeline and produce updated benchmarks, alleviating concerns on saturation and contamination. Extensive experiments demonstrate our benchmarks generalize robustly to 13 diverse evaluation models held out from benchmark construction, including proprietary, specialized, and newer-generation LLMs, significantly outperforming existing safety benchmarks in effectiveness while maintaining high separability and diversity. Our framework thus provides an effective, sustainable, and adaptable solution for streamlining safety evaluation.

Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property prediction, with synergistic benefits reaching beyond purely generative or predictive models. However, training joint models presents daunting architectural and optimization challenges. Here, we propose Hyformer, a transformer-based joint model that successfully blends the generative and predictive functionalities, using an alternating attention mechanism and a joint pre-training scheme. We show that Hyformer is simultaneously optimized for molecule generation and property prediction, while exhibiting synergistic benefits in conditional sampling, out-of-distribution property prediction and representation learning. Finally, we demonstrate the benefits of joint learning in a drug design use case of discovering novel antimicrobial~peptides.

The integration of Retrieval-Augmented Generation (RAG) with Multimodal Large Language Models (MLLMs) has revolutionized information retrieval and expanded the practical applications of AI. However, current systems struggle in accurately interpreting user intent, employing diverse retrieval strategies, and effectively filtering unintended or inappropriate responses, limiting their effectiveness. This paper introduces Contextual Understanding and Enhanced Search with MLLM (CUE-M), a novel multimodal search framework that addresses these challenges through a multi-stage pipeline comprising image context enrichment, intent refinement, contextual query generation, external API integration, and relevance-based filtering. CUE-M incorporates a robust filtering pipeline combining image-based, text-based, and multimodal classifiers, dynamically adapting to instance- and category-specific concern defined by organizational policies. Evaluations on a multimodal Q&A dataset and a public safety benchmark demonstrate that CUE-M outperforms baselines in accuracy, knowledge integration, and safety, advancing the capabilities of multimodal retrieval systems.

Widely deployed large language models (LLMs) can produce convincing yet incorrect outputs, potentially misleading users who may rely on them as if they were correct. To reduce such overreliance, there have been calls for LLMs to communicate their uncertainty to end users. However, there has been little empirical work examining how users perceive and act upon LLMs' expressions of uncertainty. We explore this question through a large-scale, pre-registered, human-subject experiment (N=404) in which participants answer medical questions with or without access to responses from a fictional LLM-infused search engine. Using both behavioral and self-reported measures, we examine how different natural language expressions of uncertainty impact participants' reliance, trust, and overall task performance. We find that first-person expressions (e.g., "I'm not sure, but...") decrease participants' confidence in the system and tendency to agree with the system's answers, while increasing participants' accuracy. An exploratory analysis suggests that this increase can be attributed to reduced (but not fully eliminated) overreliance on incorrect answers. While we observe similar effects for uncertainty expressed from a general perspective (e.g., "It's not clear, but..."), these effects are weaker and not statistically significant. Our findings suggest that using natural language expressions of uncertainty may be an effective approach for reducing overreliance on LLMs, but that the precise language used matters. This highlights the importance of user testing before deploying LLMs at scale.

Joint embedding (JE) architectures have emerged as a promising avenue for acquiring transferable data representations. A key obstacle to using JE methods, however, is the inherent challenge of evaluating learned representations without access to a downstream task, and an annotated dataset. Without efficient and reliable evaluation, it is difficult to iterate on architectural and training choices for JE methods. In this paper, we introduce LiDAR (Linear Discriminant Analysis Rank), a metric designed to measure the quality of representations within JE architectures. Our metric addresses several shortcomings of recent approaches based on feature covariance rank by discriminating between informative and uninformative features. In essence, LiDAR quantifies the rank of the Linear Discriminant Analysis (LDA) matrix associated with the surrogate SSL task -- a measure that intuitively captures the information content as it pertains to solving the SSL task. We empirically demonstrate that LiDAR significantly surpasses naive rank based approaches in its predictive power of optimal hyperparameters. Our proposed criterion presents a more robust and intuitive means of assessing the quality of representations within JE architectures, which we hope facilitates broader adoption of these powerful techniques in various domains.

We investigate Josephson arrays consisting of a dice-lattice network of superconducting weak links surrounding rhombic plaquettes of proximitized semiconductor. Josephson coupling of the weak links and electron density in the plaquettes are independently controlled by separate electrostatic gates. Applied magnetic flux results in an intricate pattern of switching currents associated with frustration, f. For depleted plaquettes, the switching current is nearly periodic in f, expected for a phase-only description, while occupied plaquettes yield a decreasing envelope of switching currents with increasing f. A model of flux dependence based on ballistic small-area junctions and diffusive large-area plaquettes yields excellent agreement with experiment.

We present an overview of the James Webb Space Telescope (JWST) Advanced Deep Extragalactic Survey (JADES), an ambitious program of infrared imaging and spectroscopy in the GOODS-S and GOODS-N deep fields, designed to study galaxy evolution from high redshift to cosmic noon. JADES uses about 770 hours of Cycle 1 guaranteed time largely from the Near-Infrared Camera (NIRCam) and Near-Infrared Spectrograph (NIRSpec) instrument teams. In GOODS-S, in and around the Hubble Ultra Deep Field and Chandra Deep Field South, JADES produces a deep imaging region of ~45 arcmin^2 with an average of 130 hrs of exposure time spread over 9 NIRCam filters. This is extended at medium depth in GOODS-S and GOODS-N with NIRCam imaging of ~175 arcmin^2 with an average exposure time of 20 hrs spread over 8-10 filters. In both fields, we conduct extensive NIRSpec multi-object spectroscopy, including 2 deep pointings of 55 hrs exposure time, 14 medium pointings of ~12 hrs, and 15 shallower pointings of ~4 hrs, targeting over 5000 HST and JWST-detected faint sources with 5 low, medium, and high-resolution dispersers covering 0.6-5.3 microns. Finally, JADES extends redward via coordinated parallels with the JWST Mid-Infrared Instrument (MIRI), featuring ~9 arcmin^2 with 43 hours of exposure at 7.7 microns and twice that area with 2-6.5 hours of exposure at 12.8 microns For nearly 30 years, the GOODS-S and GOODS-N fields have been developed as the premier deep fields on the sky; JADES is now providing a compelling start on the JWST legacy in these fields.

We present Jamba-1.5, new instruction-tuned large language models based on our Jamba architecture. Jamba is a hybrid Transformer-Mamba mixture of experts architecture, providing high throughput and low memory usage across context lengths, while retaining the same or better quality as Transformer models. We release two model sizes: Jamba-1.5-Large, with 94B active parameters, and Jamba-1.5-Mini, with 12B active parameters. Both models are fine-tuned for a variety of conversational and instruction-following capabilties, and have an effective context length of 256K tokens, the largest amongst open-weight models. To support cost-effective inference, we introduce ExpertsInt8, a novel quantization technique that allows fitting Jamba-1.5-Large on a machine with 8 80GB GPUs when processing 256K-token contexts without loss of quality. When evaluated on a battery of academic and chatbot benchmarks, Jamba-1.5 models achieve excellent results while providing high throughput and outperforming other open-weight models on long-context benchmarks. The model weights for both sizes are publicly available under the Jamba Open Model License and we release ExpertsInt8 as open source.

Recently, the physical capabilities of (M)LLMs have garnered increasing attention. However, existing benchmarks for physics suffer from two major gaps: they neither provide systematic and up-to-date coverage of real-world physics competitions such as physics Olympiads, nor enable direct performance comparison with humans. To bridge these gaps, we present HiPhO, the first benchmark dedicated to high school physics Olympiads with human-aligned evaluation. Specifically, HiPhO highlights three key innovations. (1) Comprehensive Data: It compiles 13 latest Olympiad exams from 2024-2025, spanning both international and regional competitions, and covering mixed modalities that encompass problems spanning text-only to diagram-based. (2) Professional Evaluation: We adopt official marking schemes to perform fine-grained grading at both the answer and step level, fully aligned with human examiners to ensure high-quality and domain-specific evaluation. (3) Comparison with Human Contestants: We assign gold, silver, and bronze medals to models based on official medal thresholds, thereby enabling direct comparison between (M)LLMs and human contestants. Our large-scale evaluation of 30 state-of-the-art (M)LLMs shows that: across 13 exams, open-source MLLMs mostly remain at or below the bronze level; open-source LLMs show promising progress with occasional golds; closed-source reasoning MLLMs can achieve 6 to 12 gold medals; and most models still have a significant gap from full marks. These results highlight a substantial performance gap between open-source models and top students, the strong physical reasoning capabilities of closed-source reasoning models, and the fact that there is still significant room for improvement. HiPhO, as a rigorous, human-aligned, and Olympiad-focused benchmark for advancing multimodal physical reasoning, is open-source and available at https://github.com/SciYu/HiPhO.

Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the L+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, L+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction.

In this paper, we present a simple but compelling argument, focusing on galaxy science, for preserving the main imagers and operational modes of the Hubble Space Telescope (HST) for as long as is technically feasible. While star-formation started at redshifts zgtrsim10-13, when the universe was less than 300-500 Myr old, the CSFH did not peak until zsimeq1.9, and has steadily declined since that time. Hence, at least half of all stars in the universe formed in the era where HST provides its unique rest-frame UV view of unobscured young, massive stars tracing cosmic star-formation. By rendering a subset of the 556.3 hours of available HST images in 12 filters of the Hubble Ultra Deep Field (HUDF) in an appropriate mix of colors, we illustrate the unique capabilities of HST for galaxy science emphasizing that rest-frame UV-optical wavelength range. We then contrast this with the 52.7 publicly available hours of JWST/NIRCam images in 8 filters of the same HUDF area from the JADES project, rendering these at the redder near-IR wavelengths to illustrate the unique capabilities of JWST to detect older stellar populations at higher redshifts, as well as very dusty stellar populations and Active Galactic Nuclei (AGN). HST uniquely probes (unobscured) young, hot, massive stars in galaxies, while JWST reveals more advanced stages of older stellar populations, as well as relatively short-lived phases where galaxies produce and shed a lot of dust from intense star-formation, and the very high redshift universe (zgtrsim10-11) not accessible by HST. We conclude that HST and JWST are highly complementary facilities that took decades to build to ensure decades of operation. To maximize return on investment on both HST and JWST, ways will need to be found to operate HST imaging instruments in all relevant modes for as long as possible into the JWST mission.

We present the data release and data reduction process for the Epoch 1 NIRCam observations for the Cosmic Evolution Early Release Science Survey (CEERS). These data consist of NIRCam imaging in six broadband filters (F115W, F150W, F200W, F277W, F356W and F444W) and one medium band filter (F410M) over four pointings, obtained in parallel with primary CEERS MIRI observations (Yang et al. in prep). We reduced the NIRCam imaging with the JWST Calibration Pipeline, with custom modifications and reduction steps designed to address additional features and challenges with the data. Here we provide a detailed description of each step in our reduction and a discussion of future expected improvements. Our reduction process includes corrections for known pre-launch issues such as 1/f noise, as well as in-flight issues including snowballs, wisps, and astrometric alignment. Many of our custom reduction processes were first developed with pre-launch simulated NIRCam imaging over the full 10 CEERS NIRCam pointings. We present a description of the creation and reduction of this simulated dataset in the Appendix. We provide mosaics of the real images in a public release, as well as our reduction scripts with detailed explanations to allow users to reproduce our final data products. These represent one of the first official public datasets released from the Directors Discretionary Early Release Science (DD-ERS) program.

Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction, evolving from rule-based automation scripts to sophisticated AI-driven systems capable of understanding and executing complex interface operations. This survey provides a comprehensive examination of the rapidly advancing field of LLM-based GUI Agents, systematically analyzing their architectural foundations, technical components, and evaluation methodologies. We identify and analyze four fundamental components that constitute modern GUI Agents: (1) perception systems that integrate text-based parsing with multimodal understanding for comprehensive interface comprehension; (2) exploration mechanisms that construct and maintain knowledge bases through internal modeling, historical experience, and external information retrieval; (3) planning frameworks that leverage advanced reasoning methodologies for task decomposition and execution; and (4) interaction systems that manage action generation with robust safety controls. Through rigorous analysis of these components, we reveal how recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms. We critically examine current evaluation frameworks, highlighting methodological limitations in existing benchmarks while proposing directions for standardization. This survey also identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control, while outlining promising research directions for enhancing GUI Agents' capabilities. Our systematic review provides researchers and practitioners with a thorough understanding of the field's current state and offers insights into future developments in intelligent interface automation.

The Automated Audio Captioning (AAC) task asks models to generate natural language descriptions of an audio input. Evaluating these machine-generated audio captions is a complex task that requires considering diverse factors, among them, auditory scene understanding, sound-object inference, temporal coherence, and the environmental context of the scene. While current methods focus on specific aspects, they often fail to provide an overall score that aligns well with human judgment. In this work, we propose CLAIR-A, a simple and flexible method that leverages the zero-shot capabilities of large language models (LLMs) to evaluate candidate audio captions by directly asking LLMs for a semantic distance score. In our evaluations, CLAIR-A better predicts human judgements of quality compared to traditional metrics, with a 5.8% relative accuracy improvement compared to the domain-specific FENSE metric and up to 11% over the best general-purpose measure on the Clotho-Eval dataset. Moreover, CLAIR-A offers more transparency by allowing the language model to explain the reasoning behind its scores, with these explanations rated up to 30% better by human evaluators than those provided by baseline methods. CLAIR-A is made publicly available at https://github.com/DavidMChan/clair-a.

We focus on addressing the dense backward propagation issue for training efficiency of N:M fine-grained sparsity that preserves at most N out of M consecutive weights and achieves practical speedups supported by the N:M sparse tensor core. Therefore, we present a novel method of Bi-directional Masks (Bi-Mask) with its two central innovations in: 1) Separate sparse masks in the two directions of forward and backward propagation to obtain training acceleration. It disentangles the forward and backward weight sparsity and overcomes the very dense gradient computation. 2) An efficient weight row permutation method to maintain performance. It picks up the permutation candidate with the most eligible N:M weight blocks in the backward to minimize the gradient gap between traditional uni-directional masks and our bi-directional masks. Compared with existing uni-directional scenario that applies a transposable mask and enables backward acceleration, our Bi-Mask is experimentally demonstrated to be more superior in performance. Also, our Bi-Mask performs on par with or even better than methods that fail to achieve backward acceleration. Project of this paper is available at https://github.com/zyxxmu/Bi-Mask.

When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describe them in the radiological report. To automate this process, we propose a multitask universal lesion analysis network (MULAN) for joint detection, tagging, and segmentation of lesions in a variety of body parts, which greatly extends existing work of single-task lesion analysis on specific body parts. MULAN is based on an improved Mask R-CNN framework with three head branches and a 3D feature fusion strategy. It achieves the state-of-the-art accuracy in the detection and tagging tasks on the DeepLesion dataset, which contains 32K lesions in the whole body. We also analyze the relationship between the three tasks and show that tag predictions can improve detection accuracy via a score refinement layer.

Stellar rotation periods are valuable both for constraining models of angular momentum loss and for under- standing how magnetic features impact inferences of exoplanet parameters. Building on our previous work in the northern hemisphere, we have used long-term, ground-based photometric monitoring from the MEarth Observatory to measure 234 rotation periods for nearby, southern hemisphere M dwarfs. Notable examples include the exoplanet hosts GJ 1132, LHS 1140, and Proxima Centauri. We find excellent agreement between our data and K2 photometry for the overlapping subset. Amongst the sample of stars with the highest quality datasets, we recover periods in 66%; as the length of the dataset increases, our recovery rate approaches 100%. The longest rotation periods we detect are around 140 days, which we suggest represent the periods that are reached when M dwarfs are as old as the local thick disk (about 9 Gyr).

We report the discovery and characterisation of WASP-180Ab, a hot Jupiter confirmed by the detection of its Doppler shadow and by measuring its mass using radial velocities. We find the 0.9 pm 0.1 M_{rm Jup}, 1.24 pm 0.04 R_{rm Jup} planet to be in a misaligned, retrograde orbit around an F7 star with T_{rm eff} = 6500K and a moderate rotation speed of vsini = 19.9 km s^{-1}. The host star is the primary of a V = 10.7 binary, where a secondary separated by 5'' (sim1200 AU) contributes sim30% of the light. WASP-180Ab therefore adds to a small sample of transiting hot Jupiters known in binary systems. A 4.6-day modulation seen in the WASP data is likely to be the rotational modulation of the companion star, WASP-180B.

Low-mass X-ray binaries with a neutron star as the primary object show a complex array of phenomenology during outbursts. The observed variability in X-ray emission primarily arises from changes in the innermost regions of the accretion disk, neutron star surface, and corona. In this work, we present the results of a comprehensive X-ray spectral and timing analysis of the neutron star transient MAXI J1807+132 during its 2023 outburst using data from the NICER observatory. The outburst is marked by a very rapid rise in the count rate by about a factor of 20 in a day. The source undergoes full state transitions and displays hysteresis effect in the hardness and rms intensity diagrams. Spectral analysis with a three-component model is consistent with disk truncation during the hard states and reaching the last stable orbit during the intermediate and soft states. We discuss the different values of the last stable radius in the context of possible distance of the source and magnetic field strength. The characteristic frequencies throughout the hard and intermediate states are found to be strongly correlated with the inner radius of the disk. Together with the spectral and fast variability properties, we attempt to trace the evolution of the size of the corona along the outburst. Following the main outburst, the source undergoes a high amplitude reflare wherein it shows a complex behavior with relatively high variability (10 %), but low hardness.

Recent advances in image deraining have focused on training powerful models on mixed multiple datasets comprising diverse rain types and backgrounds. However, this approach tends to overlook the inherent differences among rainy images, leading to suboptimal results. To overcome this limitation, we focus on addressing various rainy images by delving into meaningful representations that encapsulate both the rain and background components. Leveraging these representations as instructive guidance, we put forth a Context-based Instance-level Modulation (CoI-M) mechanism adept at efficiently modulating CNN- or Transformer-based models. Furthermore, we devise a rain-/detail-aware contrastive learning strategy to help extract joint rain-/detail-aware representations. By integrating CoI-M with the rain-/detail-aware Contrastive learning, we develop CoIC, an innovative and potent algorithm tailored for training models on mixed datasets. Moreover, CoIC offers insight into modeling relationships of datasets, quantitatively assessing the impact of rain and details on restoration, and unveiling distinct behaviors of models given diverse inputs. Extensive experiments validate the efficacy of CoIC in boosting the deraining ability of CNN and Transformer models. CoIC also enhances the deraining prowess remarkably when real-world dataset is included.

We revisit a pragmatic inference problem in dialog: understanding indirect responses to questions. Humans can interpret 'I'm starving.' in response to 'Hungry?', even without direct cue words such as 'yes' and 'no'. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today's systems are only as sensitive to these pragmatic moves as their language model allows. We create and release the first large-scale English language corpus 'Circa' with 34,268 (polar question, indirect answer) pairs to enable progress on this task. The data was collected via elaborate crowdsourcing, and contains utterances with yes/no meaning, as well as uncertain, middle-ground, and conditional responses. We also present BERT-based neural models to predict such categories for a question-answer pair. We find that while transfer learning from entailment works reasonably, performance is not yet sufficient for robust dialog. Our models reach 82-88% accuracy for a 4-class distinction, and 74-85% for 6 classes.

Large Language Models (LLMs) have achieved remarkable success across diverse tasks, yet they remain vulnerable to adversarial attacks, notably the well-known jailbreak attack. In particular, the Greedy Coordinate Gradient (GCG) attack has demonstrated efficacy in exploiting this vulnerability by optimizing adversarial prompts through a combination of gradient heuristics and greedy search. However, the efficiency of this attack has become a bottleneck in the attacking process. To mitigate this limitation, in this paper we rethink the generation of the adversarial prompts through an optimization lens, aiming to stabilize the optimization process and harness more heuristic insights from previous optimization iterations. Specifically, we propose the Momentum Accelerated GCG (MAC) attack, which integrates a momentum term into the gradient heuristic to boost and stabilize the random search for tokens in adversarial prompts. Experimental results showcase the notable enhancement achieved by MAC over baselines in terms of attack success rate and optimization efficiency. Moreover, we demonstrate that MAC can still exhibit superior performance for transfer attacks and models under defense mechanisms. Our code is available at https://github.com/weizeming/momentum-attack-llm.

Despite the implementation of safety alignment strategies, large language models (LLMs) remain vulnerable to jailbreak attacks, which undermine these safety guardrails and pose significant security threats. Some defenses have been proposed to detect or mitigate jailbreaks, but they are unable to withstand the test of time due to an insufficient understanding of jailbreak mechanisms. In this work, we investigate the mechanisms behind jailbreaks based on the Linear Representation Hypothesis (LRH), which states that neural networks encode high-level concepts as subspaces in their hidden representations. We define the toxic semantics in harmful and jailbreak prompts as toxic concepts and describe the semantics in jailbreak prompts that manipulate LLMs to comply with unsafe requests as jailbreak concepts. Through concept extraction and analysis, we reveal that LLMs can recognize the toxic concepts in both harmful and jailbreak prompts. However, unlike harmful prompts, jailbreak prompts activate the jailbreak concepts and alter the LLM output from rejection to compliance. Building on our analysis, we propose a comprehensive jailbreak defense framework, JBShield, consisting of two key components: jailbreak detection JBShield-D and mitigation JBShield-M. JBShield-D identifies jailbreak prompts by determining whether the input activates both toxic and jailbreak concepts. When a jailbreak prompt is detected, JBShield-M adjusts the hidden representations of the target LLM by enhancing the toxic concept and weakening the jailbreak concept, ensuring LLMs produce safe content. Extensive experiments demonstrate the superior performance of JBShield, achieving an average detection accuracy of 0.95 and reducing the average attack success rate of various jailbreak attacks to 2% from 61% across distinct LLMs.

Numerous examples of ram pressure stripping in galaxy clusters are present in literature; however, substantially less work has been focused on ram pressure stripping in lower mass groups. In this work we use the LOFAR Two-metre Sky Survey (LoTSS) to search for jellyfish galaxies in ~500 SDSS groups (z<0.05), making this the most comprehensive search for ram pressure stripping in groups to date. We identify 60 jellyfish galaxies in groups with extended, asymmetric radio continuum tails, which are found across the entire range of group mass from 10^{12.5} < M_group < 10^{14},h^{-1},M_odot. We compare the group jellyfish galaxies identified in this work with the LoTSS jellyfish galaxies in clusters presented in Roberts et al. (2021), allowing us to compare the effects of ram pressure stripping across three decades in group/cluster mass. We find that jellyfish galaxies are most commonly found in clusters, with the frequency decreasing towards the lowest mass groups. Both the orientation of observed radio continuum tails, and the positions of group jellyfish galaxies in phase space, suggest that galaxies are stripped more slowly in groups relative to clusters. Finally, we find that the star formation rates of jellyfish galaxies in groups are consistent with `normal' star-forming group galaxies, which is in contrast to cluster jellyfish galaxies that have clearly enhanced star formation rates. On the whole, there is clear evidence for ongoing ram pressure stripping in galaxy groups (down to very low group masses), though the frequency of jellyfish galaxies and the strength of ram pressure stripping appears smaller in groups than clusters. Differences in the efficiency of ram pressure stripping in groups versus clusters likely contributes to the positive trend between quenched fraction and host halo mass observed in the local Universe.

The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended solutions and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enable tuning open-source models, we construct a training corpus in a fully automatic manner for the question-answering task over such documents. Experiments show that our tuning approach achieves a relative improvement of 4.6% for the correctness of model responses, compared to the baseline open-source models. Our data, code, and models are available at https://multimodal-documents.github.io.

Mutual Reinforcement Effect (MRE) is an emerging subfield at the intersection of information extraction and model interpretability. MRE aims to leverage the mutual understanding between tasks of different granularities, enhancing the performance of both coarse-grained and fine-grained tasks through joint modeling. While MRE has been explored and validated in the textual domain, its applicability to visual and multimodal domains remains unexplored. In this work, we extend MRE to the multimodal information extraction domain for the first time. Specifically, we introduce a new task: Multimodal Mutual Reinforcement Effect (M-MRE), and construct a corresponding dataset to support this task. To address the challenges posed by M-MRE, we further propose a Prompt Format Adapter (PFA) that is fully compatible with various Large Vision-Language Models (LVLMs). Experimental results demonstrate that MRE can also be observed in the M-MRE task, a multimodal text-image understanding scenario. This provides strong evidence that MRE facilitates mutual gains across three interrelated tasks, confirming its generalizability beyond the textual domain.

We present OCTCube-M, a 3D OCT-based multi-modal foundation model for jointly analyzing OCT and en face images. OCTCube-M first developed OCTCube, a 3D foundation model pre-trained on 26,685 3D OCT volumes encompassing 1.62 million 2D OCT images. It then exploits a novel multi-modal contrastive learning framework COEP to integrate other retinal imaging modalities, such as fundus autofluorescence and infrared retinal imaging, into OCTCube, efficiently extending it into multi-modal foundation models. OCTCube achieves best performance on predicting 8 retinal diseases, demonstrating strong generalizability on cross-cohort, cross-device and cross-modality prediction. OCTCube can also predict cross-organ nodule malignancy (CT) and low cardiac ejection fraction as well as systemic diseases, such as diabetes and hypertension, revealing its wide applicability beyond retinal diseases. We further develop OCTCube-IR using COEP with 26,685 OCT and IR image pairs. OCTCube-IR can accurately retrieve between OCT and IR images, allowing joint analysis between 3D and 2D retinal imaging modalities. Finally, we trained a tri-modal foundation model OCTCube-EF from 4 million 2D OCT images and 400K en face retinal images. OCTCube-EF attains the best performance on predicting the growth rate of geographic atrophy (GA) across datasets collected from 6 multi-center global trials conducted in 23 countries. This improvement is statistically equivalent to running a clinical trial with more than double the size of the original study. Our analysis based on another retrospective case study reveals OCTCube-EF's ability to avoid false positive Phase-III results according to its accurate treatment effect estimation on the Phase-II results. In sum, OCTCube-M is a 3D multi-modal foundation model framework that integrates OCT and other retinal imaging modalities revealing substantial diagnostic and prognostic benefits.

Replicating the remarkable athleticism seen in animals has long been a challenge in robotics control. Although Reinforcement Learning (RL) has demonstrated significant progress in dynamic legged locomotion control, the substantial sim-to-real gap often hinders the real-world demonstration of truly dynamic movements. We propose a new framework to mitigate this gap through frequency-domain analysis-based impedance matching between simulated and real robots. Our framework offers a structured guideline for parameter selection and the range for dynamics randomization in simulation, thus facilitating a safe sim-to-real transfer. The learned policy using our framework enabled jumps across distances of 55 cm and heights of 38 cm. The results are, to the best of our knowledge, one of the highest and longest running jumps demonstrated by an RL-based control policy in a real quadruped robot. Note that the achieved jumping height is approximately 85% of that obtained from a state-of-the-art trajectory optimization method, which can be seen as the physical limit for the given robot hardware. In addition, our control policy accomplished stable walking at speeds up to 2 m/s in the forward and backward directions, and 1 m/s in the sideway direction.

We report on analysis of the two-color VR CCD observations of the newly discovered variable 2MASS J18024395+4003309=VSX J180243.9+400331 obtained using the 1-m telescope of the Mt. Lemmon Observatory (LOAO) in the field of the intermediate polar V1323 Her. The extended version of this conference talk we published in 2015JASS...32..127A. The variability was reported in 2012OAP....25..150A, and the object was monitored. The two-color observations covered all phase interval. The object is classified as an Algol-type variable with tidally distorted components, and shows an asymmetry of the maxima (the O\'Connell effect). For phenomenological modeling, we used the trigonometric polynomial approximation of statistically optimal degree, and a recent method "NAV" (New Algol Variable) using local specific shapes for the eclipse. Methodological aspects are described, especially for the case of few color observations. Estimates of the physical parameters based on analysis of phenomenological parameters, are presented.

Attempts to probe the atmospheres of rocky planets around M dwarfs present both promise and peril. While their favorable planet-to-star radius ratios enable searches for even thin secondary atmospheres, their high activity levels and high-energy outputs threaten atmosphere survival. Here, we present the 0.6--2.85\,mum transmission spectrum of the 1.1\,rm R_oplus, sim340\,K rocky planet TRAPPIST-1\,c obtained over two JWST NIRISS/SOSS transit observations. Each of the two spectra displays 100--500\,ppm signatures of stellar contamination. Despite being separated by 367\,days, the retrieved spot and faculae properties are consistent between the two visits, resulting in nearly identical transmission spectra. Jointly retrieving for stellar contamination and a planetary atmosphere reveals that our spectrum can rule out hydrogen-dominated, lesssim300times solar metallicity atmospheres with effective surface pressures down to 10\,mbar at the 3-sigma level. For high-mean molecular weight atmospheres, where O_2 or N_2 is the background gas, our spectrum disfavors partial pressures of more than sim10\,mbar for H_2O, CO, NH_3 and CH_4 at the 2-sigma level. Similarly, under the assumption of a 100\% H_2O, NH_3, CO, or CH_4 atmosphere, our spectrum disfavors thick, >1\,bar atmospheres at the 2-sigma level. These non-detections of spectral features are in line with predictions that even heavier, CO_2-rich, atmospheres would be efficiently lost on TRAPPIST-1\,c given the cumulative high-energy irradiation experienced by the planet. Our results further stress the importance of robustly accounting for stellar contamination when analyzing JWST observations of exo-Earths around M dwarfs, as well as the need for high-fidelity stellar models to search for the potential signals of thin secondary atmospheres.

We present the Red Unknowns: Bright Infrared Extragalactic Survey (RUBIES), providing JWST/NIRSpec spectroscopy of red sources selected across ~150 arcmin^2 from public JWST/NIRCam imaging in the UDS and EGS fields. RUBIES novel observing strategy offers a well-quantified selection function: the survey is optimised to reach high (>70%) completeness for bright and red (F150W-F444W>2) sources that are very rare. To place these rare sources in context, we simultaneously observe a reference sample of the 2<z<7 galaxy population, sampling sources at a rate that is inversely proportional to their number density in the 3D space of F444W magnitude, F150W-F444W colour, and photometric redshift. In total, RUBIES observes ~3000 targets across 1<z_{phot}<10 with both the PRISM and G395M dispersers, and ~1500 targets at z_{phot}>3 using only the G395M disperser. The RUBIES data reveal a highly diverse population of red sources that span a broad redshift range (z_{spec}sim1-9), with photometric redshift scatter and outlier fraction that are 3 times higher than for similarly bright sources that are less red. This diversity is not apparent from the photometric SEDs. Only spectroscopy reveals that the SEDs encompass a mixture of galaxies with dust-obscured star formation, extreme line emission, a lack of star formation indicating early quenching, and luminous active galactic nuclei. As a first demonstration of our broader selection function we compare the stellar masses and rest-frame U-V colours of the red sources and our reference sample: red sources are typically more massive (M_*sim10^{10-11.5} M_odot) across all redshifts. However, we find that the most massive systems span a wide range in U-V colour. We describe our data reduction procedure and data quality, and publicly release the reduced RUBIES data and vetted spectroscopic redshifts of the first half of the survey through the DJA.

We investigate the stellar shape and size-mass relationship of X-ray selected Active Galactic Nuclei (AGN) host galaxies using the high-angular resolution and deep sensitivity in the near-infrared of the COSMOS-Web JWST survey field. We present the rest-frame 1-mu m size, stellar mass, Sersic index, axis-ratio, Gini-M_{20} parameters of 690 moderate luminosity AGNs between redshift 0-3 and with stellar mass log M_ssim 10.75. We find that AGN host galaxies have an effective radius of 1-5 kpc, which is between star-forming (SFG) and quiescent galaxies (QGs) of the same stellar mass. AGN hosts have similar size-mass trends as SFG and QGs, being smaller at higher redshift for the same stellar mass. The slope of the size-mass relationship of AGN host galaxies is steeper than that of star-forming galaxies. Their rest-frame 1mu m stellar morphology indicates a significant spheroidal component. We observed a low merger fraction (6%) in our sample as well as substructures similar to disks, bars, and spiral arms in the residual images, which are in tension with evolutionary pathways that require major mergers. However, it may also be due to the different timescales between mergers and AGN activity.

Singly-TASE operators for the numerical solution of stiff differential equations were proposed by Calvo et al. in J.Sci. Comput. 2023 to reduce the computational cost of Runge-Kutta-TASE (RKTASE) methods when the involved linear systems are solved by some LU factorization. In this paper we propose a modification of these methods to improve the efficiency by considering different TASE operators for each stage of the Runge-Kutta. We prove that the resulting RKTASE methods are equivalent to W-methods (Steihaug and Wolfbrandt, Mathematics of Computation,1979) and this allows us to obtain the order conditions of the proposed Modified Singly-RKTASE methods (MSRKTASE) through the theory developed for the W-methods. We construct new MSRKTASE methods of order two and three and demonstrate their effectiveness through numerical experiments on both linear and nonlinear stiff systems. The results show that the MSRKTASE schemes significantly enhance efficiency and accuracy compared to previous Singly-RKTASE schemes.

The wavelength-coverage and sensitivity of JWST now enables us to probe the rest-frame UV - optical spectral energy distributions (SEDs) of galaxies at high-redshift (z>4). From these SEDs it is, in principle, through SED fitting possible to infer key physical properties, including stellar masses, star formation rates, and dust attenuation. These in turn can be compared with the predictions of galaxy formation simulations allowing us to validate and refine the incorporated physics. However, the inference of physical properties, particularly from photometry alone, can lead to large uncertainties and potential biases. Instead, it is now possible, and common, for simulations to be forward-modelled to yield synthetic observations that can be compared directly to real observations. In this work, we measure the JWST broadband fluxes and colours of a robust sample of 5<z<10 galaxies using the Cosmic Evolution Early Release Science (CEERS) Survey. We then analyse predictions from a variety of models using the same methodology and compare the NIRCam/F277W magnitude distribution and NIRCam colours with observations. We find that the predicted and observed magnitude distributions are similar, at least at 5<z<8. At z>8 the distributions differ somewhat, though our observed sample size is small and thus susceptible to statistical fluctuations. Likewise, the predicted and observed colour evolution show broad agreement, at least at 5<z<8. There is however some disagreement between the observed and modelled strength of the strong line contribution. In particular all the models fails to reproduce the F410M-F444W colour at z>8, though, again, the sample size is small here.

JWST has now made it possible to probe the rest-frame optical line emission of high-redshift galaxies extending to z~9, and potentially beyond. To aid in the interpretation of these emerging constraints, in this work we explore predictions for [OIII] emission in high-redshift galaxies using the First Light and Reionisation Epoch Simulations (FLARES). We produce predictions for the [OIII] luminosity function, its correlation with the UV luminosity, and the distribution of equivalent widths (EWs). We also explore how the [OIII] EW correlates with physical properties including specific star formation rate, metallicity, and dust attenuation. Our predictions are largely consistent with recent observational constraints on the luminosity function, average equivalent widths, and line ratios. However, they fail to reproduce the observed tail of high-EW sources and the number density of extreme line emitters. Possibilities to explain these discrepancies include an additional source of ionising photons and/or greater stochasticity in star formation in the model or photometric scatter and/or bias in the observations. With JWST now rapidly building larger samples and a wider range of emission lines the answer to this remaining discrepancy should be available imminently.

The James Webb Space Telescope (JWST) is set to transform many areas of astronomy, one of the most exciting is the expansion of the redshift frontier to z>10. In its first year alone JWST should discover hundreds of galaxies, dwarfing the handful currently known. To prepare for these powerful observational constraints, we use the First Light And Reionisation Epoch (FLARES) simulations to predict the physical and observational properties of the z>10 population of galaxies accessible to JWST. This is the first time such predictions have been made using a hydrodynamical model validated at low redshift. Our predictions at z=10 are broadly in agreement with current observational constraints on the far-UV luminosity function and UV continuum slope beta, though the observational uncertainties are large. We note tension with recent constraints zsim 13 from Harikane et al. 2022 - compared to these constraints, FLARES predicts objects with the same space density should have an order of magnitude lower luminosity, though this is mitigated slightly if dust attenuation is negligible in these systems. Our predictions suggest that in JWST's first cycle alone, around 600 galaxies should be identified at z>10, with the first small samples available at z>13.

There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines. Today, many benchmarks exist to measure LLM knowledge and reasoning on textbook-style science questions, but few if any benchmarks are designed to evaluate language model performance on practical tasks required for scientific research, such as literature search, protocol planning, and data analysis. As a step toward building such benchmarks, we introduce the Language Agent Biology Benchmark (LAB-Bench), a broad dataset of over 2,400 multiple choice questions for evaluating AI systems on a range of practical biology research capabilities, including recall and reasoning over literature, interpretation of figures, access and navigation of databases, and comprehension and manipulation of DNA and protein sequences. Importantly, in contrast to previous scientific benchmarks, we expect that an AI system that can achieve consistently high scores on the more difficult LAB-Bench tasks would serve as a useful assistant for researchers in areas such as literature search and molecular cloning. As an initial assessment of the emergent scientific task capabilities of frontier language models, we measure performance of several against our benchmark and report results compared to human expert biology researchers. We will continue to update and expand LAB-Bench over time, and expect it to serve as a useful tool in the development of automated research systems going forward. A public subset of LAB-Bench is available for use at the following URL: https://huggingface.co/datasets/futurehouse/lab-bench

Traditional theories of optimization cannot describe the dynamics of optimization in deep learning, even in the simple setting of deterministic training. The challenge is that optimizers typically operate in a complex, oscillatory regime called the "edge of stability." In this paper, we develop theory that can describe the dynamics of optimization in this regime. Our key insight is that while the *exact* trajectory of an oscillatory optimizer may be challenging to analyze, the *time-averaged* (i.e. smoothed) trajectory is often much more tractable. To analyze an optimizer, we derive a differential equation called a "central flow" that characterizes this time-averaged trajectory. We empirically show that these central flows can predict long-term optimization trajectories for generic neural networks with a high degree of numerical accuracy. By interpreting these central flows, we are able to understand how gradient descent makes progress even as the loss sometimes goes up; how adaptive optimizers "adapt" to the local loss landscape; and how adaptive optimizers implicitly navigate towards regions where they can take larger steps. Our results suggest that central flows can be a valuable theoretical tool for reasoning about optimization in deep learning.

We use NIRSpec MSA spectroscopy and NIRCam Photometry to explore the properties of JADES-GS8-RL-1, a rapidly quenched, z=8.5 galaxy with a stellar mass of 10^{8.9}M_odot, a steep blue UV slope, a Balmer break, and no sign of strong emission lines. With a beta_{UV}=-2.8pm 0.2, as measured from the NIRSpec spectrum, JADES-GS8-RL-1 is consistent with negligible dust attenuation and little to no contribution from the nebular continuum alongside a probable high escape fraction. The beta_{UV} slope measured from photometry varies from -3.0 in the central regions to -2.2 at the outskirts suggesting possible regional differences in the escape fraction. There are no high-ionisation emission lines, only a tentative 2.9\sig detection of [OII]. Using photometry, this emission appears to be extended, possibly corresponding to weakly ionised gas expelled during or after the quenching process. JADES-GS8-RL-1 is spatially resolved with a half-light radius of 240 pc and has an exponential, disc-like morphology. It appears to have formed all its stars in a short burst within the past 100 Myr with a formation time of approx70 Myr and a quenching time of approx30 Myr. This quenching would have occurred rapidly, making it a more distant example of the kind of low-mass "mini-quenched" galaxies previously observed at high-z. Due to the extremely blue beta_{UV} slope, our best-fit model predicts a high value for \fesc of >10\%, consistent with the value derived from the beta_{UV} slope, which when combined with our extraordinarily low O32 upper limit suggests JADES-GS8-RL-1 is a fascinating example of a high-z "remnant leaker" in one of its earliest phases, deep in the epoch of reionisation.

One technique to visualize the training of neural networks is to perform PCA on the parameters over the course of training and to project to the subspace spanned by the first few PCA components. In this paper we compare this technique to the PCA of a high dimensional random walk. We compute the eigenvalues and eigenvectors of the covariance of the trajectory and prove that in the long trajectory and high dimensional limit most of the variance is in the first few PCA components, and that the projection of the trajectory onto any subspace spanned by PCA components is a Lissajous curve. We generalize these results to a random walk with momentum and to an Ornstein-Uhlenbeck processes (i.e., a random walk in a quadratic potential) and show that in high dimensions the walk is not mean reverting, but will instead be trapped at a fixed distance from the minimum. We finally compare the distribution of PCA variances and the PCA projected training trajectories of a linear model trained on CIFAR-10 and ResNet-50-v2 trained on Imagenet and find that the distribution of PCA variances resembles a random walk with drift.