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ETAS GmbH

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Selected work

Representative Papers

Parallel Tempering for Diffusion-Based Combinatorial Optimization

Sep 29, 2026

When applying discrete diffusion models to combinatorial optimization, independent sampling improves solution quality but incurs substantial computational overhead. This work proposes PT-Denoise, an inference method that introduces parallel tempering into the diffusion denoising phase for the first time. Without retraining, it enables adaptive inter-trajectory interactions: low-energy trajectories concentrate at lower temperatures for fine-grained search, while high-energy states continue exploring at higher temperatures, thereby dynamically allocating sampling resources. Evaluated on graph-structured combinatorial optimization tasks, the proposed method significantly enhances the quality of the best solutions found with minimal additional computational cost.

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GraIP: A Benchmarking Framework For Neural Graph Inverse Problems

Jan 26, 2026

This work addresses the limitation of existing graph learning methods, which are often designed in isolation for specific tasks and lack a unified framework for tackling the inverse problem of inferring graph structure from observational data. To this end, the authors propose the Neural Graph Inverse Problem (GraIP) framework, which unifies diverse tasks—such as graph structure discovery, causal inference, and neural relational reasoning—as inverse problems of forward processes like message passing or network dynamics. This study presents the first systematic formulation of the GraIP theoretical paradigm, accompanied by a cross-task benchmark dataset and evaluation metrics. Extensive experiments demonstrate the framework’s generality and superior performance over existing baselines across multiple tasks, including graph rewiring, causal discovery, and neural relation inference.

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GraphBench: Next-generation graph learning benchmarking

Dec 04, 2025

Graph machine learning has long suffered from benchmark fragmentation: datasets are task-specific, evaluation protocols lack standardization, and out-of-distribution (OOD) generalization is rarely considered—severely hindering reproducibility and cross-model comparison. To address this, we introduce GraphBench, the first cross-domain, multi-task graph learning benchmark platform, supporting node-, edge-, and graph-level classification as well as generative tasks. GraphBench features standardized data splits, a unified evaluation protocol, an automated hyperparameter tuning framework, and—uniquely—integrates OOD generalization metrics into its core evaluation suite. We establish authoritative baselines using message-passing GNNs and graph Transformers, conducting systematic evaluations across 12 diverse datasets. GraphBench significantly improves evaluation consistency and result comparability, providing a reproducible, scalable, and standardized infrastructure for graph learning research.

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From ECU to VSOC: UDS Security Monitoring Strategies

Oct 29, 2025

Facing escalating cybersecurity threats targeting the Unified Diagnostic Services (UDS) protocol in modern vehicles, this paper proposes an end-to-end monitoring framework spanning ECU log acquisition, context-aware logging, and collaborative analysis at a remote Vehicle Security Operations Center (VSOC). Methodologically, it introduces a multi-scenario detection architecture grounded in a novel UDS attack taxonomy and designs a lightweight context-correlation analysis technique to significantly improve attack detection accuracy and interpretability. Experimental evaluation demonstrates comprehensive coverage of typical UDS attack vectors—including DoIP abuse and session/security access bypass—with a detection accuracy of 92.3%. Furthermore, the study identifies structural limitations of the AUTOSAR Security Event standard for real-time attack detection and proposes semantic enhancement and standardization extensions for in-vehicle logging. These contributions provide empirical support for the evolution of automotive cybersecurity standards.

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Recent publications

Latest Papers

Parallel Tempering for Diffusion-Based Combinatorial Optimization

Sep 29, 2026

When applying discrete diffusion models to combinatorial optimization, independent sampling improves solution quality but incurs substantial computational overhead. This work proposes PT-Denoise, an inference method that introduces parallel tempering into the diffusion denoising phase for the first time. Without retraining, it enables adaptive inter-trajectory interactions: low-energy trajectories concentrate at lower temperatures for fine-grained search, while high-energy states continue exploring at higher temperatures, thereby dynamically allocating sampling resources. Evaluated on graph-structured combinatorial optimization tasks, the proposed method significantly enhances the quality of the best solutions found with minimal additional computational cost.

0 citationsRead paper

GraIP: A Benchmarking Framework For Neural Graph Inverse Problems

Jan 26, 2026

This work addresses the limitation of existing graph learning methods, which are often designed in isolation for specific tasks and lack a unified framework for tackling the inverse problem of inferring graph structure from observational data. To this end, the authors propose the Neural Graph Inverse Problem (GraIP) framework, which unifies diverse tasks—such as graph structure discovery, causal inference, and neural relational reasoning—as inverse problems of forward processes like message passing or network dynamics. This study presents the first systematic formulation of the GraIP theoretical paradigm, accompanied by a cross-task benchmark dataset and evaluation metrics. Extensive experiments demonstrate the framework’s generality and superior performance over existing baselines across multiple tasks, including graph rewiring, causal discovery, and neural relation inference.

0 citationsRead paper

GraphBench: Next-generation graph learning benchmarking

Dec 04, 2025

Graph machine learning has long suffered from benchmark fragmentation: datasets are task-specific, evaluation protocols lack standardization, and out-of-distribution (OOD) generalization is rarely considered—severely hindering reproducibility and cross-model comparison. To address this, we introduce GraphBench, the first cross-domain, multi-task graph learning benchmark platform, supporting node-, edge-, and graph-level classification as well as generative tasks. GraphBench features standardized data splits, a unified evaluation protocol, an automated hyperparameter tuning framework, and—uniquely—integrates OOD generalization metrics into its core evaluation suite. We establish authoritative baselines using message-passing GNNs and graph Transformers, conducting systematic evaluations across 12 diverse datasets. GraphBench significantly improves evaluation consistency and result comparability, providing a reproducible, scalable, and standardized infrastructure for graph learning research.

0 citationsRead paper

From ECU to VSOC: UDS Security Monitoring Strategies

Oct 29, 2025

Facing escalating cybersecurity threats targeting the Unified Diagnostic Services (UDS) protocol in modern vehicles, this paper proposes an end-to-end monitoring framework spanning ECU log acquisition, context-aware logging, and collaborative analysis at a remote Vehicle Security Operations Center (VSOC). Methodologically, it introduces a multi-scenario detection architecture grounded in a novel UDS attack taxonomy and designs a lightweight context-correlation analysis technique to significantly improve attack detection accuracy and interpretability. Experimental evaluation demonstrates comprehensive coverage of typical UDS attack vectors—including DoIP abuse and session/security access bypass—with a detection accuracy of 92.3%. Furthermore, the study identifies structural limitations of the AUTOSAR Security Event standard for real-time attack detection and proposes semantic enhancement and standardization extensions for in-vehicle logging. These contributions provide empirical support for the evolution of automotive cybersecurity standards.

0 citationsRead paper