Papers
Research papers from arXiv and related sources
Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition
Implementing new features across an entire codebase presents a formidable challenge for Large Language Models (LLMs). This proactive task requires a deep understanding of the global system architec...
Mingwei Liu, Zhenxi Chen, Zheng Pei, Zihao Wang, Yanlin Wang, Zibin Zheng
SSMG-Nav: Enhancing Lifelong Object Navigation with Semantic Skeleton Memory Graph
Navigating to out-of-sight targets from human instructions in unfamiliar environments is a core capability for service robots. Despite substantial progress, most approaches underutilize reusable, p...
Haochen Niu, Lantao Zhang, Xingwu Ji, Rendong Ying, Peilin Liu, Fei Wen
Non-verbal Real-time Human-AI Interaction in Constrained Robotic Environments
We study the ongoing debate regarding the statistical fidelity of AI-generated data compared to human-generated data in the context of non-verbal communication using full body motion. Concretely, w...
Dragos Costea, Alina Marcu, Cristina Lazar, Marius Leordeanu
Experimental realization and self-testing of semisymmetric informationally complete measurements via a one-dimensional photonic quantum walk
Generalized quantum measurements play a crucial role in quantum mechanics, and symmetric informationally complete positive operator-valued measurements (SIC POVMs) provide a powerful and flexible f...
Xu Xu, Han-Yu Cheng, Meng-Yun Ma, Chao-Jie Sun, Yan Wang, Li-Jiong Shen, Zhe Sun, Qi-Ping Su, Chu...
ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment and thus safe use...
Xunlei Chen, Jinyu Guo, Yuang Li, Zhaokun Wang, Yi Gong, Jie Zou, Jiwei Wei, Wenhong Tian
Can LLMs Hack Enterprise Networks? -- Replicated Computational Results (RCR) Report
This is the Replicated Computational Results (RCR) Report for the paper ``Can LLMs Hack Enterprise Networks?" The paper empirically investigates the efficacy and effectiveness of different LLMs for...
Andreas Happe, Jürgen Cito
nchellwig at SemEval-2026 Task 3: Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis using Large Language Models
We present Self-Consistent Structured Generation (SCSG) for Dimensional Aspect-Based Sentiment Analysis in SemEval-2026 Task 3 (Track A). SCSG enhances prediction reliability by executing a LoRA-ad...
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz, Christian Wolff
Information and communications technologies for carbon sinks from economics and engineering perspectives
Climate change has intensified the urgency of effective carbon sink solutions, yet the integration of Information and Communications Technologies (ICT) in these systems remains fragmented despite i...
Yuze Dong, Jinsong Wu
Learning Shortest Paths with Generative Flow Networks
In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in no...
Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution
Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, which fundamentally l...
Guoxin Shi, Haoyu Wang, Zaihui Yang, Yuxing Wang, Yongzhe Chang
LLM-as-an-Annotator: Training Lightweight Models with LLM-Annotated Examples for Aspect Sentiment Tuple Prediction
Training models for Aspect-Based Sentiment Analysis (ABSA) tasks requires manually annotated data, which is expensive and time-consuming to obtain. This paper introduces LA-ABSA, a novel approach t...
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz, Christian Wolff
Search for the charmonium weak decay $ψ(2S)\to D_s^-π^+ + c.c.$ and $ψ(2S)\to D_s^-ρ^+ + c.c.$
We search for the weak decays $ψ(2S)\to D_s^-π^+ + c.c.$ and $ψ(2S)\to D_s^-ρ^+ + c.c.$ for the first time. The search is based on $(2712.4\pm14.3)\times 10^6$ events containing the charmonium stat...
BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, C. S. Akondi, R. Alibert...
FreeAct: Freeing Activations for LLM Quantization
Quantization is pivotal for mitigating the significant memory and computational overhead of Large Language Models (LLMs). While emerging transformation-based methods have successfully enhanced quan...
Xiaohao Liu, Xiaobo Xia, Manyi Zhang, Ji-Fu Li, Xianzhi Yu, Fei Shen, Xiu Su, See-Kiong Ng, Tat-S...
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation
Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g.\ interviews conducted by a technical ...
Harry Stuart, Masahiro Kaneko, Timothy Baldwin
AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions
We introduce AnnoABSA, the first web-based annotation tool to support the full spectrum of Aspect-Based Sentiment Analysis (ABSA) tasks. The tool is highly customizable, enabling flexible configura...
Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz, Christian Wolff
Testing the isothermal Jeans model for self-interacting dark matter halos in the collapse phase
We benchmark the semi-analytical isothermal Jeans model against a high-resolution isolated N-body simulation that follows a self-interacting dark matter (SIDM) halo into deep core collapse. The mod...
Shubo Li, Moritz S. Fischer, Zixiang Jia, Fangzhou Jiang, Ran Li, Hai-Bo Yu
CHLU: The Causal Hamiltonian Learning Unit as a Symplectic Primitive for Deep Learning
Current deep learning primitives dealing with temporal dynamics suffer from a fundamental dichotomy: they are either discrete and unstable (LSTMs) \citep{pascanu_difficulty_2013}, leading to explod...
Pratik Jawahar, Maurizio Pierini
Efficient Test-Time Optimization for Depth Completion via Low-Rank Decoder Adaptation
Zero-shot depth completion has gained attention for its ability to generalize across environments without sensor-specific datasets or retraining. However, most existing approaches rely on diffusion...
Minseok Seo, Wonjun Lee, Jaehyuk Jang, Changick Kim
Distinguishing thermal and pseudothermal light by testing the Siegert relation
Thermal light, including blackbody radiation and spontaneous emission, exhibits photon bunching. Thermal light sources, however, typically yield low spectral densities, limiting their practical uti...
Xi Jie Yeo, Justin Yu Xiang Peh, Darren Ming Zhi Koh, Christian Kurtsiefer, Peng Kian Tan
DGNet: Discrete Green Networks for Data-Efficient Learning of Spatiotemporal PDEs
Spatiotemporal partial differential equations (PDEs) underpin a wide range of scientific and engineering applications. Neural PDE solvers offer a promising alternative to classical numerical method...
Yingjie Tan, Quanming Yao, Yaqing Wang