OpenRaiser/Intern-Atlas huggingface dataset:
Temperature Optimisation in Data Centres
Venue: arXiv cs.OH 2025
Year: 2025

Raising server inlet temperatures tends to increase the server power consumption due to heightened server fan activity needed to compensate for the warmer air, and to decrease cooling infrastructure power consumption due to less intense and possibly less frequent chiller activity. Relying on primary data from two data centres of a colocation provider in Switzerland, this study succeeds in confirming and quantifying the first effect: In the upper half of the ASHRAE recommended temperature range and slightly above it, i.e., between 23 - 30 °C, the server power consumption correlated positively with the temperature, with a server room-wide temperature sensitivity of 0.35 - 0.5 %/°C. Based on the available data and due to ambivalent results, the study did not succeed in answering which effect is stronger for the entire data centre: the increase in server consumption or the savings in cooling consumption. Whether raising inlet temperatures beyond the ASHRAE upper limit (i.e., 27 °C) yields additional overall benefits thus remains an open question that would be best addressed through controlled experiments. A few pieces of evidence indicate nevertheless that the upper part of the current ASHRAE recommendation (i.e., around 25 - 27 °C) might be the current sweet spot for colocation DCs. The widely used power usage effectiveness (PUE) metric is not at all useful in this context. For practical reasons but semantically wrong, the server fan power consumption is included in the consumption of servers (and thus, of the IT infrastructure) and is thus on the wrong side of the PUE fraction. An inlet temperature raise, which will lead to increased fan consumption, will thus always yield a PUE decrease, irrespective of what happens to the overall data centre power consumption.
Explaining novel senses using definition generation with open language models
Venue: arXiv cs.CL 2025
Year: 2025

We apply definition generators based on open-weights large language models to the task of creating explanations of novel senses, taking target word usages as an input. To this end, we employ the datasets from the AXOLOTL'24 shared task on explainable semantic change modeling, which features Finnish, Russian and German languages. We fine-tune and provide publicly the open-source models performing higher than the best submissions of the aforementioned shared task, which employed closed proprietary LLMs. In addition, we find that encoder-decoder definition generators perform on par with their decoder-only counterparts.
Parallax: Efficient LLM Inference Service over Decentralized Environment
Venue: arXiv cs.DC 2025
Year: 2025

Deploying a large language model (LLM) inference service remains costly because centralized serving depends on specialized GPU clusters and high-bandwidth interconnects in datacenters. An appealing alternative is to leverage collaborative decentralized GPU pools. However, heterogeneity in GPU and limited interconnected network bandwidth, along with potentially dynamic availability, make efficient scheduling the central challenge in this scenario. In this paper, we present Parallax, a decentralized LLM serving system that turns a pool of heterogeneous GPUs into an efficient inference platform via a two-phase scheduler. Parallax decomposes planning into (i) model allocation, which places layers of each replica across diverse GPUs to jointly optimize latency and throughput under memory and link-bandwidth constraints, and (ii) request-time GPU pipeline selection, which stitches layers from different replicas into end-to-end execution chains that balance load and adapt to current conditions. We implement Parallax and evaluate it on open-source LLMs deployed over real volunteer nodes. Parallax consistently reduces latency and increases throughput relative to decentralized baselines, demonstrating that principled scheduling can make volunteer compute a practical, affordable substrate for LLM inference. Github Repo at: https://github.com/GradientHQ/parallax.
Auto-ARGUE: LLM-Based Report Generation Evaluation
Venue: arXiv cs.IR 2025
Year: 2025

Generation of long-form, citation-backed reports is a primary use case for retrieval augmented generation (RAG) systems. While open-source evaluation tools exist for various RAG tasks, ones tailored to report generation (RG) are lacking. Accordingly, we introduce Auto-ARGUE, a robust LLM-based implementation of the recently proposed ARGUE framework for RG evaluation. We present analysis of Auto-ARGUE on the RG pilot task from the TREC 2024 NeuCLIR track, showing good system-level correlations with human judgments. We further release a web app for visualization of Auto-ARGUE outputs.
I Like To Move It -- Computation Instead of Data in the Brain
Venue: arXiv cs.DC 2025
Year: 2025

The detailed functioning of the human brain is still poorly understood. Brain simulations are a well-established way to complement experimental research, but must contend with the computational demands of the approximately $10^{11}$ neurons and the $10^{14}$ synapses connecting them, the network of the latter referred to as the connectome. Studies suggest that changes in the connectome (i.e., the formation and deletion of synapses, also known as structural plasticity) are essential for critical tasks such as memory formation and learning. The connectivity update can be efficiently computed using a Barnes-Hut-inspired approximation that lowers the computational complexity from $O(n^2)$ to $O(n log n)$, where n is the number of neurons. However, updating synapses, which relies heavily on RMA, and the spike exchange between neurons, which requires all-to-all communication at every time step, still hinder scalability. We present a new algorithm that significantly reduces the communication overhead by moving computation instead of data. This shrinks the time it takes to update connectivity by a factor of six and the time it takes to exchange spikes by more than two orders of magnitude.
Hamster: A Large-Scale Study and Characterization of Developer-Written Tests
Venue: arXiv cs.SE 2025
Year: 2025

Automated test generation (ATG), which aims to reduce the cost of manual test suite development, has been investigated for decades and has produced countless techniques based on a variety of approaches: symbolic analysis, search-based, random and adaptive-random, learning-based, and, most recently, large-language-model-based approaches. However, despite this large body of research, there is still a gap in our understanding of the characteristics of developer-written tests and, consequently, in our assessment of how well ATG techniques and tools can generate realistic and representative tests. To bridge this gap, we conducted an extensive empirical study of developer-written tests for Java applications, covering 1.7 million test cases from open-source repositories. Our study is the first of its kind in studying aspects of developer-written tests that are mostly neglected in the existing literature, such as test scope, test fixtures and assertions, types of inputs, and use of mocking. Based on the characterization, we then compare existing tests with those generated by two state-of-the-art ATG tools. Our results highlight that a vast majority of developer-written tests exhibit characteristics that are beyond the capabilities of current ATG tools. Finally, based on the insights gained from the study, we identify promising research directions that can help bridge the gap between current tool capabilities and more effective tool support for developer testing practices. We hope that this work can set the stage for new advances in the field and bring ATG tools closer to generating the types of tests developers write.
Beyond Pixels: Efficient Dataset Distillation via Sparse Gaussian Representation
Venue: arXiv cs.CV 2025
Year: 2025

Dataset distillation has emerged as a promising paradigm that synthesizes compact, informative datasets capable of retaining the knowledge of large-scale counterparts, thereby addressing the substantial computational and storage burdens of modern model training. Conventional approaches typically rely on dense pixel-level representations, which introduce redundancy and are difficult to scale up. In this work, we propose GSDD, a novel and efficient sparse representation for dataset distillation based on 2D Gaussians. Instead of representing all pixels equally, GSDD encodes critical discriminative information in a distilled image using only a small number of Gaussian primitives. This sparse representation could improve dataset diversity under the same storage budget, enhancing coverage of difficult samples and boosting distillation performance. To ensure both efficiency and scalability, we adapt CUDA-based splatting operators for parallel inference and training, enabling high-quality rendering with minimal computational and memory overhead. Our method is simple yet effective, broadly applicable to different distillation pipelines, and highly scalable. Experiments show that GSDD achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet subsets, while remaining highly efficient encoding and decoding cost. Our code is available at https://github.com/j-cyoung/GSDatasetDistillation.
PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
Venue: arXiv cs.CV 2025
Year: 2025

The rapid rise of synthetic media has made deepfake detection a critical challenge for online safety and trust. Progress remains constrained by the scarcity of large, high-quality datasets. Although multimodal large language models (LLMs) exhibit strong reasoning capabilities, their performance on deepfake detection is poor, often producing explanations that are misaligned with visual evidence or hallucinatory. To address this limitation, we introduce a reasoning-annotated dataset for deepfake detection and propose Paragraph-level Relative Policy Optimization (PRPO), a reinforcement learning algorithm that aligns LLM reasoning with image content at the paragraph level. Experiments show that PRPO improves detection accuracy by a wide margin and achieves the highest reasoning score of 4.55/5.0. Ablation studies further demonstrate that PRPO significantly outperforms GRPO under test-time conditions. These results underscore the importance of grounding multimodal reasoning in visual evidence to enable more reliable and interpretable deepfake detection.
Tuning the Tuner: Introducing Hyperparameter Optimization for Auto-Tuning
Venue: arXiv cs.LG 2025
Year: 2025

Automatic performance tuning (auto-tuning) is widely used to optimize performance-critical applications across many scientific domains by finding the best program variant among many choices. Efficient optimization algorithms are crucial for navigating the vast and complex search spaces in auto-tuning. As is well known in the context of machine learning and similar fields, hyperparameters critically shape optimization algorithm efficiency. Yet for auto-tuning frameworks, these hyperparameters are almost never tuned, and their potential performance impact has not been studied. We present a novel method for general hyperparameter tuning of optimization algorithms for auto-tuning, thus "tuning the tuner". In particular, we propose a robust statistical method for evaluating hyperparameter performance across search spaces, publish a FAIR data set and software for reproducibility, and present a simulation mode that replays previously recorded tuning data, lowering the costs of hyperparameter tuning by two orders of magnitude. We show that even limited hyperparameter tuning can improve auto-tuner performance by 94.8% on average, and establish that the hyperparameters themselves can be optimized efficiently with meta-strategies (with an average improvement of 204.7%), demonstrating the often overlooked hyperparameter tuning as a powerful technique for advancing auto-tuning research and practice.
Feedback Forensics: A Toolkit to Measure AI Personality
Venue: arXiv cs.CL 2025
Year: 2025

Some traits making a "good" AI model are hard to describe upfront. For example, should responses be more polite or more casual? Such traits are sometimes summarized as model character or personality. Without a clear objective, conventional benchmarks based on automatic validation struggle to measure such traits. Evaluation methods using human feedback such as Chatbot Arena have emerged as a popular alternative. These methods infer "better" personality and other desirable traits implicitly by ranking multiple model responses relative to each other. Recent issues with model releases highlight limitations of these existing opaque evaluation approaches: a major model was rolled back over sycophantic personality issues, models were observed overfitting to such feedback-based leaderboards. Despite these known issues, limited public tooling exists to explicitly evaluate model personality. We introduce Feedback Forensics: an open-source toolkit to track AI personality changes, both those encouraged by human (or AI) feedback, and those exhibited across AI models trained and evaluated on such feedback. Leveraging AI annotators, our toolkit enables investigating personality via Python API and browser app. We demonstrate the toolkit's usefulness in two steps: (A) first we analyse the personality traits encouraged in popular human feedback datasets including Chatbot Arena, MultiPref and PRISM; and (B) then use our toolkit to analyse how much popular models exhibit such traits. We release (1) our Feedback Forensics toolkit alongside (2) a web app tracking AI personality in popular models and feedback datasets as well as (3) the underlying annotation data at https://github.com/rdnfn/feedback-forensics.
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
Venue: arXiv cs.LG 2025
Year: 2025

Recently, a new optimization method based on the linear minimization oracle (LMO), called Muon, has been attracting increasing attention since it can train neural networks faster than existing adaptive optimization methods, such as Adam. In this paper, we study how Muon can be utilized in federated learning. We first show that straightforwardly using Muon as the local optimizer of FedAvg does not converge to the stationary point since the LMO is a biased operator. We then propose FedMuon which can mitigate this issue. We also analyze how solving the LMO approximately affects the convergence rate and find that, surprisingly, FedMuon can converge for any number of Newton-Schulz iterations, while it can converge faster as we solve the LMO more accurately. Through experiments, we demonstrated that FedMuon can outperform the state-of-the-art federated learning methods.
LLM-Assisted Emergency Triage Benchmark: Bridging Hospital-Rich and MCI-Like Field Simulation
Venue: arXiv cs.LG 2025
Year: 2025

Research on emergency and mass casualty incident (MCI) triage has been limited by the absence of openly usable, reproducible benchmarks. Yet these scenarios demand rapid identification of the patients most in need, where accurate deterioration prediction can guide timely interventions. While the MIMIC-IV-ED database is openly available to credentialed researchers, transforming it into a triage-focused benchmark requires extensive preprocessing, feature harmonization, and schema alignment -- barriers that restrict accessibility to only highly technical users. We address these gaps by first introducing an open, LLM-assisted emergency triage benchmark for deterioration prediction (ICU transfer, in-hospital mortality). The benchmark then defines two regimes: (i) a hospital-rich setting with vitals, labs, notes, chief complaints, and structured observations, and (ii) an MCI-like field simulation limited to vitals, observations, and notes. Large language models (LLMs) contributed directly to dataset construction by (i) harmonizing noisy fields such as AVPU and breathing devices, (ii) prioritizing clinically relevant vitals and labs, and (iii) guiding schema alignment and efficient merging of disparate tables. We further provide baseline models and SHAP-based interpretability analyses, illustrating predictive gaps between regimes and the features most critical for triage. Together, these contributions make triage prediction research more reproducible and accessible -- a step toward dataset democratization in clinical AI.
Joint Communication and Parameter Estimation in MIMO Channels
Venue: arXiv cs.IT 2025
Year: 2025

We study a joint communication and sensing setting comprising a transmitter, a receiver, and a sensor, all equipped with multiple antennas. The transmitter sends an encoded signal over the channel with the dual purpose of communicating an information message to the receiver, and enabling the sensor to estimate a target parameter vector by generating back-scattered signals. We assume that the transmitter and sensor are co-located, or fully connected, giving the latter access to the transmitted signal. The target parameter vector is randomly drawn from a continuous distribution, yet remains fixed throughout the transmission block. We establish the fundamental performance trade-off between the communication and sensing tasks, captured in terms of a capacity-MSE function. In doing so, we identify optimal coding schemes for this multi-antenna joint communication and sensing setting. Moreover, we particularize our result to two practically-inspired scenarios where we showcase optimal schemes and trade-offs.
Introducing Large Language Models into the Design Flow of Time Sensitive Networking
Venue: arXiv cs.NI 2025
Year: 2025

The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment, referred to as TSN orchestration. This paper outlines the steps involved in TSN orchestration and the associated challenges. To assess the capabilities of existing LLM models, we conduct an initial proof-of-concept case study focused on TSN configuration across multiple models. Building on these insights, we propose an LLM-assisted orchestration framework. Unlike prior research on LLMs in computer networks, which has concentrated on general configuration and management, TSN-specific orchestration has not yet been investigated. We present the building blocks for automating TSN using LLMs, describe the proposed pipeline, and analyze opportunities and limitations for real-world deployment. Finally, we highlight key challenges and research directions, including the development of TSN-focused datasets, standardized benchmark suites, and the integration of external tools such as Network Calculus (NC) engines and simulators. This work provides the first roadmap toward assessing the feasibility of LLM-assisted TSN orchestration.
MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
Venue: arXiv cs.AI 2025
Year: 2025

Molecular docking is a core tool in drug discovery for predicting ligand-target interactions. Despite the availability of diverse search-based and machine learning approaches, no single docking algorithm consistently dominates, as performance varies by context. To overcome this challenge, algorithm selection frameworks such as GNNAS-Dock, built on graph neural networks, have been proposed. This study introduces an enhanced system, MC-GNNAS-Dock, with three key advances. First, a multi-criteria evaluation integrates binding-pose accuracy (RMSD) with validity checks from PoseBusters, offering a more rigorous assessment. Second, architectural refinements by inclusion of residual connections strengthen predictive robustness. Third, rank-aware loss functions are incorporated to sharpen rank learning. Extensive experiments are performed on a curated dataset containing approximately 3200 protein-ligand complexes from PDBBind. MC-GNNAS-Dock demonstrates consistently superior performance, achieving up to 5.4% (3.4%) gains under composite criteria of RMSD below 1Å (2Å) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.
An Annotation Scheme for Factuality and its Application to Parliamentary Proceedings
Venue: arXiv cs.CL 2025
Year: 2025

Factuality assesses the extent to which a language utterance relates to real-world information; it determines whether utterances correspond to facts, possibilities, or imaginary situations, and as such, it is instrumental for fact checking. Factuality is a complex notion that relies on multiple linguistic signals, and has been studied in various disciplines. We present a complex, multi-faceted annotation scheme of factuality that combines concepts from a variety of previous works. We developed the scheme for Hebrew, but we trust that it can be adapted to other languages. We also present a set of almost 5,000 sentences in the domain of parliamentary discourse that we manually annotated according to this scheme. We report on inter-annotator agreement, and experiment with various approaches to automatically predict (some features of) the scheme, in order to extend the annotation to a large corpus.
OntoAligner Meets Knowledge Graph Embedding Aligners
Venue: arXiv cs.AI 2025
Year: 2025

Ontology Alignment (OA) is essential for enabling semantic interoperability across heterogeneous knowledge systems. While recent advances have focused on large language models (LLMs) for capturing contextual semantics, this work revisits the underexplored potential of Knowledge Graph Embedding (KGE) models, which offer scalable, structure-aware representations well-suited to ontology-based tasks. Despite their effectiveness in link prediction, KGE methods remain underutilized in OA, with most prior work focusing narrowly on a few models. To address this gap, we reformulate OA as a link prediction problem over merged ontologies represented as RDF-style triples and develop a modular framework, integrated into the OntoAligner library, that supports 17 diverse KGE models. The system learns embeddings from a combined ontology and aligns entities by computing cosine similarity between their representations. We evaluate our approach using standard metrics across seven benchmark datasets spanning five domains: Anatomy, Biodiversity, Circular Economy, Material Science and Engineering, and Biomedical Machine Learning. Two key findings emerge: first, KGE models like ConvE and TransF consistently produce high-precision alignments, outperforming traditional systems in structure-rich and multi-relational domains; second, while their recall is moderate, this conservatism makes KGEs well-suited for scenarios demanding high-confidence mappings. Unlike LLM-based methods that excel at contextual reasoning, KGEs directly preserve and exploit ontology structure, offering a complementary and computationally efficient strategy. These results highlight the promise of embedding-based OA and open pathways for further work on hybrid models and adaptive strategies.