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Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LALMs remain scarce for non-English languages, with Korean being one such underexplored case. In this paper, we introduce KoALa-Bench, a comprehensive benchmark for evaluating Korean speech understanding and speech faithfulness of LALMs. In particular, KoALa-Bench comprises six tasks. Four tasks evaluate fundamental speech understanding capabilities, including automatic speech recognition, speech translation, speech question answering, and speech instruction following, while the remaining two tasks evaluate speech faithfulness, motivated by our observation that several LALMs often fail to fully leverage the speech modality. Furthermore, to reflect Korea-specific knowledge, our benchmark incorporates listening questions from the Korean college scholastic ability test as well as content covering Korean cultural domains. We conduct extensive experiments across six models, including both white-box and black-box ones. Our benchmark, evaluation code, and leaderboard are publicly available at https://ksbench.github.io/Korean-Benchmark/.

Text-to-image (T2I) generative models achieve impressive visual fidelity but inherit and amplify demographic imbalances and cultural biases embedded in training data. We introduce T2I-BiasBench, a unified evaluation framework of thirteen complementary metrics that jointly captures demographic bias, element omission, and cultural collapse in diffusion models - the first framework to address all three dimensions simultaneously. We evaluate three open-source models - Stable Diffusion v1.5, BK-SDM Base, and Koala Lightning - against Gemini 2.5 Flash (RLHF-aligned) as a reference baseline. The benchmark comprises 1,574 generated images across five structured prompt categories. T2I-BiasBench integrates six established metrics with seven additional measures: four newly proposed (Composite Bias Score, Grounded Missing Rate, Implicit Element Missing Rate, Cultural Accuracy Ratio) and three adapted (Hallucination Score, Vendi Score, CLIP Proxy Score). Three key findings emerge: (1) Stable Diffusion v1.5 and BK-SDM exhibit bias amplification (>1.0) in beauty-related prompts; (2) contextual constraints such as surgical PPE substantially attenuate professional-role gender bias (Doctor CBS =

The Pandas API has been central to the success of pandas and its alternatives. Despite its importance, there is no benchmark for it, and we argue that we cannot repurpose existing benchmarks (from other domains) for the Pandas API. In this paper, we introduce requirements that are necessary for a Pandas API enchmark, and present the first benchmark that fulfills them: PandasBench. We argue that it should evaluate the real-world coverage of a technique. Yet, real-world coverage is not sufficient for a useful benchmark, and so we also: cleaned it from irrelevant code, adapted it for benchmark usage, and introduced input scaling. We claim that uniform scaling used in other benchmarks (e.g., TPC-H) is too coarse-grained for PandasBench, and use a non-uniform scaling scheme. PandasBench is the largest Pandas API benchmark to date, with 102 notebooks and 3,721 cells. We used PandasBench to evaluate Modin, Dask, Koalas, and Dias. This is the largest-scale evaluation of all these techniques to date. Prior works report significant speedups using constrained benchmarks, but we show that on a larger benchmark with real-world code, the most notebooks that got a speedup were 8/102 (~8%) for Mod

The localization of moving robots depends on the availability of good features from the environment. Sensor systems like Lidar are popular, but unique features can also be extracted from images of the ground. This work presents the Keypoint Localization Framework (KOALA), which utilizes deep neural networks that extract sufficient features from an industrial floor for accurate localization without having readable markers. For this purpose, we use a floor covering that can be produced as cheaply as common industrial floors. Although we do not use any filtering, prior, or temporal information, we can estimate our position in 75.7 % of all images with a mean position error of 2 cm and a rotation error of 2.4 %. Thus, the robot kidnapping problem can be solved with high precision in every frame, even while the robot is moving. Furthermore, we show that our framework with our detector and descriptor combination is able to outperform comparable approaches.

PyKOALA is an innovative Python-based library designed to provide a robust and flexible framework for Integral Field Spectroscopy (IFS) data reduction. By addressing the complexities of transforming raw measurements into scientifically valuable spectra, PyKOALA simplifies the data reduction pipeline while remaining instrument-agnostic and user-friendly. This proceeding outlines the challenges of IFS data reduction, PyKOALA's architecture, and its applications to observations by the KOALA+AAOmega instruments at the Anglo-Australian Telescope.

The recent surge in open-source text-to-video generation models has significantly energized the research community, yet their dependence on proprietary training datasets remains a key constraint. While existing open datasets like Koala-36M employ algorithmic filtering of web-scraped videos from early platforms, they still lack the quality required for fine-tuning advanced video generation models. We present Tiger200K, a manually curated high visual quality video dataset sourced from User-Generated Content (UGC) platforms. By prioritizing visual fidelity and aesthetic quality, Tiger200K underscores the critical role of human expertise in data curation, and providing high-quality, temporally consistent video-text pairs for fine-tuning and optimizing video generation architectures through a simple but effective pipeline including shot boundary detection, OCR, border detecting, motion filter and fine bilingual caption. The dataset will undergo ongoing expansion and be released as an open-source initiative to advance research and applications in video generative models. Project page: https://tinytigerpan.github.io/tiger200k/

Collecting data of students solving programming tasks is incredibly valuable for researchers and educators. It allows verifying that the students correctly apply the features and concepts they are taught, or finding students' misconceptions. However, existing data collection tools have limitations, e.g., no control over the granularity of the collected code, not collecting the specific events of the programming environment used, and overall being hard to configure. To overcome these limitations, we propose KOALA, a convenient and highly configurable tool for collecting code snapshots and feature usage from students solving programming tasks in JetBrains IDEs. The plugin can be installed in IDEs and configured to provide the students with the necessary tasks, enable or disable certain IDE features like code completion, and run surveys. During problem solving, the plugin collects code snapshots at the configured granularity, all IDE actions like running and debugging, as well as some data not collected in prior works, like employed hotkeys and switching focus between files. The collected data is sent to the server that comes with the tool, where it is stored and can be converted to t

Decision-focused learning (DFL) integrates predictive modeling and optimization by training predictors to optimize the downstream decision target rather than merely minimizing prediction error. To date, existing DFL methods typically rely on deterministic point predictions, which are often insufficient to capture the intrinsic stochasticity of real-world environments. To address this challenge, we propose the first diffusion-based DFL approach, which trains a diffusion model to represent the distribution of uncertain parameters and optimizes the decision by solving a stochastic optimization with samples drawn from the diffusion model. Our contributions are twofold. First, we formulate diffusion DFL using the reparameterization trick, enabling end-to-end training through diffusion. While effective, it is memory and compute-intensive due to the need to differentiate through the diffusion sampling process. Second, we propose a lightweight score function estimator that uses only several forward diffusion passes and avoids backpropagation through the sampling. This follows from our results that backpropagating through stochastic optimization can be approximated by a weighted score funct

We present the KOALA database, a new set of LTE, line-blanketed model atmospheres calculated with the code ATLAS9, together with the corresponding Opacity Distribution Functions and emergent fluxes. The latter were used also to calculated G-band bolometric corrections and theoretical magnitudes and colours for several photometric systems, i.e. UBVRI, 2MASS, Hypparcos-Tycho, SDSS, Galex, Euclid and Gaia DR3. With respect to the previous grids of ATLAS9 model atmospheres, we adopted the solar mixture by Caffau/Lodders and we extend the sampling in metallicity (from -5.0 to -2.5 dex with step of 0.5 dex, and from -2.5 dex to +0.5 dex with step of 0.25 dex) and in [alfa/Fe] (from -0.4 to +0.4 dex with a step of 0.2 dex). Also, we provide a finer sampling in Teff for Teff lower than 7000 K. This finer grid allows for more accurate interpolation of colours and in many cases it makes not necessary to compute a new model atmosphere, since one of the grid can be used directly. A total of 51663 model atmospheres and emergent fluxes have been computed. Finally, we discuss the impact of [M/H] and [alfa/Fe] on the thermal and pressure structures of the model atmospheres and on theoretical colou

We propose KOALA++, a scalable Kalman-based optimization algorithm that explicitly models structured gradient uncertainty in neural network training. Unlike second-order methods, which rely on expensive second order gradient calculation, our method directly estimates the parameter covariance matrix by recursively updating compact gradient covariance products. This design improves upon the original KOALA framework that assumed diagonal covariance by implicitly capturing richer uncertainty structure without storing the full covariance matrix and avoiding large matrix inversions. Across diverse tasks, including image classification and language modeling, KOALA++ achieves accuracy on par or better than state-of-the-art first- and second-order optimizers while maintaining the efficiency of first-order methods.

Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the

The Milky Way halo has been built-up over cosmic time through the accretion and dissolution of star clusters and dwarf galaxies as well as through their complex interactions with the Galactic disc. Traces of these accreted structures persist to the present day in the chemical and kinematic properties of stars and their orbits and allow for the disentangling of the accretion history of the Galaxy through observations of Milky Way stars. We utilised 6D phase-space information in combination with [Fe/H] measurements to facilitate a clustering analysis of stars using their kinematics and chemistry simultaneously, a technique known as chemo-kinematic tagging. Using t-distributed stochastic neighbour embedding (t-SNE), we performed dimensionality reduction and identify stars from clusters and streams that are co-localised in the kinematic and chemical parameter space. We included E, Jr, Jz, Lz, r_apo, r_peri, and eccentricity as well as [Fe/H] as input into the algorithm, and used a sample of 5347 stars from 229 individual Milky Way substructures compiled from various sources in the literature. Most notably, we recovered several large-scale structures that have been reported in the liter

With the continuous progress of visual generation technologies, the scale of video datasets has grown exponentially. The quality of these datasets plays a pivotal role in the performance of video generation models. We assert that temporal splitting, detailed captions, and video quality filtering are three crucial determinants of dataset quality. However, existing datasets exhibit various limitations in these areas. To address these challenges, we introduce Koala-36M, a large-scale, high-quality video dataset featuring accurate temporal splitting, detailed captions, and superior video quality. The essence of our approach lies in improving the consistency between fine-grained conditions and video content. Specifically, we employ a linear classifier on probability distributions to enhance the accuracy of transition detection, ensuring better temporal consistency. We then provide structured captions for the splitted videos, with an average length of 200 words, to improve text-video alignment. Additionally, we develop a Video Training Suitability Score (VTSS) that integrates multiple sub-metrics, allowing us to filter high-quality videos from the original corpus. Finally, we incorporate

Deep neural networks are highly susceptible to adversarial attacks, which pose significant risks to security- and safety-critical applications. We present KoALA (KL-L0 Adversarial detection via Label Agreement), a novel, semantics-free adversarial detector that requires no architectural changes or adversarial retraining. KoALA operates on a simple principle: it detects an adversarial attack when class predictions from two complementary similarity metrics disagree. These metrics - KL divergence and an L0-based similarity - are specifically chosen to detect different types of perturbations. The KL divergence metric is sensitive to dense, low-amplitude shifts, while the L0-based similarity is designed for sparse, high-impact changes. We provide a formal proof of correctness for our approach. The only training required is a simple fine-tuning step on a pre-trained image encoder using clean images to ensure the embeddings align well with both metrics. This makes KoALA a lightweight, plug-and-play solution for existing models and various data modalities. Our extensive experiments on ResNet/CIFAR-10 and CLIP/Tiny-ImageNet confirm our theoretical claims. When the theorem's conditions are m

Residual transformations enhance the representational depth and expressive power of large language models (LLMs). However, applying static residual transformations across all tokens in auto-regressive generation leads to a suboptimal trade-off between inference efficiency and generation fidelity. Existing methods, including Early Exiting, Skip Decoding, and Mixture-of-Depth address this by modulating the residual transformation based on token-level complexity. Nevertheless, these approaches predominantly consider the distance traversed by tokens through the model layers, neglecting the underlying velocity of residual evolution. We introduce Mixture of Multi-rate Residuals (M2R2), a framework that dynamically modulates residual velocity to improve early alignment, enhancing inference efficiency. Evaluations on reasoning oriented tasks such as Koala, Self-Instruct, WizardLM, and MT-Bench show M2R2 surpasses state-of-the-art distance-based strategies, balancing generation quality and speedup. In self-speculative decoding setup, M2R2 achieves up to 2.8x speedups on MT-Bench, outperforming methods like 2-model speculative decoding, Medusa, LookAhead Decoding, and DEED. In Mixture-of-Exp

Large language models (LLMs) play a key role in generating evidence-based and stylistic counter-arguments, yet their effectiveness in real-world applications has been underexplored. Previous research often neglects the balance between evidentiality and style, which are crucial for persuasive arguments. To address this, we evaluated the effectiveness of stylized evidence-based counter-argument generation in Counterfire, a new dataset of 38,000 counter-arguments generated by revising counter-arguments to Reddit's ChangeMyView community to follow different discursive styles. We evaluated generic and stylized counter-arguments from basic and fine-tuned models such as GPT-3.5, PaLM-2, and Koala-13B, as well as newer models (GPT-4o, Claude Haiku, LLaMA-3.1) focusing on rhetorical quality and persuasiveness. Our findings reveals that humans prefer stylized counter-arguments over the original outputs, with GPT-3.5 Turbo performing well, though still not reaching human standards of rhetorical quality nor persuasiveness indicating a persisting style-evidence tradeoff in counter-argument generation by LLMs. We conclude with an examination of ethical considerations in LLM persuasion research,

Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However, despite being trained on millions of short seconds-long videos, vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation, we propose a lightweight and self-supervised approach, Key frame-conditioned long video-LLM (Koala), that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks, where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly, we also empirically show that our approach not only helps a pretrained vLLM to understa

The training of large models, involving fine-tuning, faces the scarcity of high-quality data. Compared to the solutions based on centralized data centers, updating large models in the Internet of Things (IoT) faces challenges in coordinating knowledge from distributed clients by using their private and heterogeneous data. To tackle such a challenge, we propose KOALA (Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients) to impel the training of large models in IoT. Since the resources obtained by IoT clients are limited and restricted, it is infeasible to locally execute large models and also update them in a privacy-preserving manner. Therefore, we leverage federated learning and knowledge distillation to update large models through collaboration with their small models, which can run locally at IoT clients to process their private data separately and enable large-small model knowledge transfer through iterative learning between the server and clients. Moreover, to support clients with similar or different computing capacities, KOALA is designed with two kinds of large-small model joint learning modes, namely to be homogeneous or hetero

Large Language Models (LLMs) exhibit high inference latency due to their autoregressive decoding nature. While the draft head in speculative decoding mitigates this issue, its full potential remains unexplored. In this paper, we introduce KOALA (K-layer Optimized Adversarial Learning Architecture), an orthogonal approach to the draft head. By transforming the conventional single-layer draft head into a multi-layer architecture and incorporating adversarial learning into the traditional supervised training, KOALA significantly improves the accuracy of the draft head in predicting subsequent tokens, thus more closely mirroring the functionality of LLMs. Although this improvement comes at the cost of slightly increased drafting overhead, KOALA substantially unlocks the draft head's potential, greatly enhancing speculative decoding. We conducted comprehensive evaluations of KOALA, including both autoregressive and non-autoregressive draft heads across various tasks, demonstrating a latency speedup ratio improvement of 0.24x-0.41x, which is 10.57%-14.09% faster than the original draft heads.