Repository for Publications and Research Data
News from the ETH Library
Recently Added
CoMap: A Collaborative 3D Sketch Mapping Game to Engage Spatial Communication in Search and Rescue
Item type: Conference Paper
Tianyi Xiao; Sailin Zhong; Peter Kiefer; et al. (2026)
Search and rescue (SAR) is a complex teamwork environment that requires efficient spatial communication between commanders and field teams with heterogeneous perspectives and asymmetric information. Maps are central artifacts in SAR, yet they are also a space of technological tension due to constantly changing situation at disaster sites. Sketch mapping is an effective method of externalizing and communicating spatial understanding, increasing situation awareness in spatial decision-making tasks including SAR. Current paper-based sketch mapping in SAR struggles to handle the three-dimensional nature of physical space and remote collaboration. We propose CoMap, a collaborative 3D sketch mapping system validated in a virtual reality fire-rescue game. In a within-subject study with 13 commander–field team pairs, CoMap enabled more accurate and efficient spatial communication than conventional 2D sketch mapping. Communication analysis further showed that CoMap fostered proactive descriptions. We distill three design implications for next-generation mapping tools to advance SAR training and real-world operations.
Search for heavy pseudoscalar and scalar bosons decaying to a top quark pair in proton-proton collisions at √s = 13 TeV
Item type: Journal Article
Hayrapetyan A.; Makarenko V.; Tumasyan A.; et al. (2025)
A search for pseudoscalar or scalar bosons decaying to a top quark pair (tt¯) in final states with one or two charged leptons is presented. The analyzed proton-proton collision data was recorded at √s = 13 TeV by the CMS experiment at the CERN LHC and corresponds to an integrated luminosity of 138 fb-1. The invariant mass mtt¯ of the reconstructed tt¯ system and variables sensitive to its spin and parity are used to discriminate against the standard model tt¯ background. Interference between pseudoscalar or scalar boson production and the standard model tt¯ continuum is included, leading to peak-dip structures in the mtt¯ distribution. An excess of the data above the background prediction, based on perturbative quantum chromodynamics (QCD) calculations, is observed near the kinematic tt¯ production threshold, while good agreement is found for high mtt¯. The data are consistent with the background prediction if the contribution from a simplified model of a color-singlet 1S[1]0 tt¯ quasi-bound state ηt, inspired by nonrelativistic QCD, is added. Upper limits at 95% confidence level are set on the coupling between the pseudoscalar or scalar bosons and the top quark for boson masses in the range 365.1000 GeV, relative widths between 0.5% and 25%, and two background scenarios with or without ηt contribution.
VRGaussianAvatar: Integrating 3D Gaussian Avatars into VR
Item type: Journal Article
Song H.; Yoon B.; Yang S.; et al. (2026)
We present VRGaussianAvatar, an integrated system that enables real-time full-body 3D Gaussian Splatting (3DGS) avatars in virtual reality using only head-mounted display (HMD) tracking signals. The system adopts a parallel pipeline with a VR Frontend and a GABackend. The VR Frontend uses inverse kinematics to estimate full-body pose and streams the resulting pose along with stereo camera parameters to the backend. The GA Backend stereoscopically renders a 3DGS avatar reconstructed from a single image. To improve stereo rendering efficiency, we introduce Binocular Batching, which jointly processes left and right eye views in a single batched pass to reduce redundant computation and support high-resolution VR displays. We evaluate VRGaussianAvatar with quantitative performance tests and a within-subject user study against image- and video-based mesh avatar baselines. Results show that VRGaussianAvatar sustains interactive VR performance and yields higher perceived appearance similarity, embodiment, and plausibility.
New QUBO Transformations to Improve Quantum and Simulated Annealing Performance for Quadratic Knapsack
Item type: Conference Paper
Borrajo N.; Ramírez J.M.; Nosrati F.; et al. (2026)
Recent advancements in quantum computing have demonstrated significant potential for solving combinatorial optimization problems, like the quadratic knapsack problem, a constrained binary optimization problem. However, current quantum and quantum-inspired algorithms often require transforming these constrained problems into an unconstrained form, known as Quadratic Unconstrained Binary Optimization (QUBO). Such transformations can significantly impact the algorithms’ speed and efficiency. In this study, we evaluate five existing transformation methods and propose four novel approaches. We assess all nine methods using Simulated Annealing and find that three of our approaches outperform existing methods in terms of execution time and the quality and quantity of feasible solutions found. Additionally, we tested these transformations on quantum annealers, which were unable to solve even small problem instances, due to limitations in connectivity and error rates. However, our results highlight the advantages of the new approaches, which reduce the total number of variables in the QUBO representation. This is a critical factor for enhanced performance on emerging quantum hardware, since it also reduces the required number of qubits and the embedding chain lengths.
Uncertainty Quantification for Large Language Models
Item type: Conference Paper
Panov M.; Shelmanov A.; Vashurin R.; et al. (2026)
Large language models (LLMs) power many NLP applications; yet, they can produce fluent but incorrect content (hallucinations), which threatens reliability and user trust. This tutorial introduces uncertainty quantification (UQ) for text generation: methods that attach an explicit reliability signal to model outputs and enable practical safeguards such as hallucination detection and selective generation. We begin with core uncertainty concepts and explain why techniques that work well for classification do not directly transfer to autoregressive generation. We then survey representative white-box and black-box approaches, from entropy- and probability-based scores to learned probes that leverage internal representations. Retrieval-augmented generation (RAG) has become a core design pattern for LLM applications. Incorporating retrieved evidence introduces both new challenges and valuable structures for uncertainty estimation. In the ECIR edition of the tutorial, we focus on UQ techniques tailored to RAG pipelines and briefly discuss how uncertainty can guide agentic workflows. Practical demonstrations are done using LM-Polygraph (https://github.com/IINemo/lm-polygraph), an open-source toolkit that consolidates more than forty recent UQ and calibration methods and provides a large-scale benchmark, making it easy to reproduce results and integrate UQ into applications with minimal code. Overall, the tutorial is intended to lower the barrier to entry for researchers and developers who want to evaluate existing UQ methods, design improved ones, and deploy uncertainty-aware LLM systems.