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❓Wie können Musikwissenschaftler:innen mit einem Knowledge Graph interagieren und relevante Informationen abrufen, ohne SPARQL-Expert:innen zu sein?
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Incremental (k, z)-Clustering on Graphs
Emilio Cruciani, Sebastian Forster, Antonis Skarlatos
https://arxiv.org/abs/2602.08542 https://arxiv.org/pdf/2602.08542 https://arxiv.org/html/2602.08542
arXiv:2602.08542v1 Announce Type: new
Abstract: Given a weighted undirected graph, a number of clusters $k$, and an exponent $z$, the goal in the $(k, z)$-clustering problem on graphs is to select $k$ vertices as centers that minimize the sum of the distances raised to the power $z$ of each vertex to its closest center. In the dynamic setting, the graph is subject to adversarial edge updates, and the goal is to maintain explicitly an exact $(k, z)$-clustering solution in the induced shortest-path metric.
While efficient dynamic $k$-center approximation algorithms on graphs exist [Cruciani et al. SODA 2024], to the best of our knowledge, no prior work provides similar results for the dynamic $(k,z)$-clustering problem. As the main result of this paper, we develop a randomized incremental $(k, z)$-clustering algorithm that maintains with high probability a constant-factor approximation in a graph undergoing edge insertions with a total update time of $\tilde O(k m^{1 o(1)} k^{1 \frac{1}{\lambda}} m)$, where $\lambda \geq 1$ is an arbitrary fixed constant. Our incremental algorithm consists of two stages. In the first stage, we maintain a constant-factor bicriteria approximate solution of size $\tilde{O}(k)$ with a total update time of $m^{1 o(1)}$ over all adversarial edge insertions. This first stage is an intricate adaptation of the bicriteria approximation algorithm by Mettu and Plaxton [Machine Learning 2004] to incremental graphs. One of our key technical results is that the radii in their algorithm can be assumed to be non-decreasing while the approximation ratio remains constant, a property that may be of independent interest.
In the second stage, we maintain a constant-factor approximate $(k,z)$-clustering solution on a dynamic weighted instance induced by the bicriteria approximate solution. For this subproblem, we employ a dynamic spanner algorithm together with a static $(k,z)$-clustering algorithm.
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GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum
Shuwen Xu, Yao Xu, Jiaxiang Liu, Chenhao Yuan, Wenshuo Peng, Jun Zhao, Kang Liu
https://arxiv.org/abs/2603.28533 https://arxiv.org/pdf/2603.28533 https://arxiv.org/html/2603.28533
arXiv:2603.28533v1 Announce Type: new
Abstract: Agentic knowledge graph question answering (KGQA) requires an agent to iteratively interact with knowledge graphs (KGs), posing challenges in both training data scarcity and reasoning generalization. Specifically, existing approaches often restrict agent exploration: prompting-based methods lack autonomous navigation training, while current training pipelines usually confine reasoning to predefined trajectories. To this end, this paper proposes \textit{GraphWalker}, a novel agentic KGQA framework that addresses these challenges through \textit{Automated Trajectory Synthesis} and \textit{Stage-wise Fine-tuning}. GraphWalker adopts a two-stage SFT training paradigm: First, the agent is trained on structurally diverse trajectories synthesized from constrained random-walk paths, establishing a broad exploration prior over the KG; Second, the agent is further fine-tuned on a small set of expert trajectories to develop reflection and error recovery capabilities. Extensive experiments demonstrate that our stage-wise SFT paradigm unlocks a higher performance ceiling for a lightweight reinforcement learning (RL) stage, enabling GraphWalker to achieve state-of-the-art performance on CWQ and WebQSP. Additional results on GrailQA and our constructed GraphWalkerBench confirm that GraphWalker enhances generalization to out-of-distribution reasoning paths. The code is publicly available at https://github.com/XuShuwenn/GraphWalker
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The Leader's Handbook Study Series
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In his forward to the Leader’s Handbook Ackoff offers a format to study the book which we will follow in these sessions:
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The Leader's Handbook Study Series — In2:InThinking Network
Description: In The Leader’s Handbook , Peter Scholtes offers a comprehensive expansion of Dr. W. Edwards Deming’s System of Profound Knowledge. Drawing from his deep expertise in organizational development, adult learning, people and intrinsic motivation, Scholtes operationalizes Deming’s teach

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
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https://arxiv.org/abs/2603.26668 https://mastoxiv.page/@arXiv_csIR_bot/116322781593134028
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Shalin Shah, Srikanth Ryali, Ramasubbu Venkatesh
https://arxiv.org/abs/2603.26670 https://mastoxiv.page/@arXiv_csIR_bot/116322784870180864
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Seonok Kim
https://arxiv.org/abs/2603.26683 https://mastoxiv.page/@arXiv_csIR_bot/116322841916406330
- Agentic AI for Human Resources: LLM-Driven Candidate Assessment
Yuksel, Anees, Elneima, Hewavitharana, Al-Badrashiny, Sawaf
https://arxiv.org/abs/2603.26710 https://mastoxiv.page/@arXiv_csIR_bot/116322937601675587
- SEAR: Schema-Based Evaluation and Routing for LLM Gateways
Zecheng Zhang, Han Zheng, Yue Xu
https://arxiv.org/abs/2603.26728 https://mastoxiv.page/@arXiv_csDB_bot/116322627580095245
- SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model
Guifeng Deng, Pan Wang, Jiquan Wang, Shuying Rao, Junyi Xie, Wanjun Guo, Tao Li, Haiteng Jiang
https://arxiv.org/abs/2603.26738 https://mastoxiv.page/@arXiv_csCV_bot/116322739676378309
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Chen Zheng, Yuxuan Lai, Haoyang Lu, Wentao Ma, Jitao Yang, Jian Wang
https://arxiv.org/abs/2603.26768 https://mastoxiv.page/@arXiv_csCV_bot/116323078149576728
- Learning to Select Visual In-Context Demonstrations
Eugene Lee, Yu-Chi Lin, Jiajie Diao
https://arxiv.org/abs/2603.26775 https://mastoxiv.page/@arXiv_csLG_bot/116322648878995047
- CRISP: Characterizing Relative Impact of Scholarly Publications
Hannah Collison, Benjamin Van Durme, Daniel Khashabi
https://arxiv.org/abs/2603.26791 https://mastoxiv.page/@arXiv_csDL_bot/116322621679820997
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Xinyi Duan, Yuanrong Tang, Jiangtao Gong
https://arxiv.org/abs/2603.26807 https://mastoxiv.page/@arXiv_csIR_bot/116322959557860848
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Jenny S Wang, Aliya Saperstein, Emma Pierson
https://arxiv.org/abs/2603.26930 https://mastoxiv.page/@arXiv_csCY_bot/116322780637316287
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Felix Haas, Sebastian P. Bayerl
https://arxiv.org/abs/2603.26939 https://mastoxiv.page/@arXiv_csSD_bot/116322704289189130
- FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
Ravi, Ying, Nesterov, Krishnan, Uskuplu, Xia, Aswedige, Nashold
https://arxiv.org/abs/2603.26996 https://mastoxiv.page/@arXiv_csAI_bot/116322625941412681
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Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna
https://arxiv.org/abs/2603.27001 https://mastoxiv.page/@arXiv_eessAS_bot/116322763598554193
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Jovana Kondic, et al.
https://arxiv.org/abs/2603.27064 https://mastoxiv.page/@arXiv_csCV_bot/116323214468792735
- daVinci-LLM:Towards the Science of Pretraining
Qin, Liu, Mi, Xie, Huang, Si, Lu, Feng, Wu, Liu, Luo, Hou, Guo, Qiao, Liu
https://arxiv.org/abs/2603.27164 https://mastoxiv.page/@arXiv_csAI_bot/116322653467105951
- LightMover: Generative Light Movement with Color and Intensity Controls
Zhou, Wang, Kim, Shu, Yu, Hold-Geoffroy, Chaturvedi, Wu, Lin, Cohen
https://arxiv.org/abs/2603.27209 https://mastoxiv.page/@arXiv_csCV_bot/116323263295656104
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Tan, Zhang, Li, Yu, Zhong, Zhou, Dong, Hong
https://arxiv.org/abs/2603.27303 https://mastoxiv.page/@arXiv_csAI_bot/116322838641595927
- Inference-Time Structural Reasoning for Compositional Vision-Language Understanding
Amartya Bhattacharya
https://arxiv.org/abs/2603.27349 https://mastoxiv.page/@arXiv_csCV_bot/116323280006044500
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Alexandre Cristov\~ao Maiorano
https://arxiv.org/abs/2603.27355 https://mastoxiv.page/@arXiv_csAI_bot/116322987708962414
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[1/5]:
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https://arxiv.org/abs/2502.18273 https://mastoxiv.page/@arXiv_csCL_bot/114069031700102129
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Anja Ryser, Yingqiang Gao, Sarah Ebling
https://arxiv.org/abs/2504.00780 https://mastoxiv.page/@arXiv_csCL_bot/114267149909002069
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Fabio Celli, Georgios Spathulas
https://arxiv.org/abs/2504.02572 https://mastoxiv.page/@arXiv_csCL_bot/114278467094094490
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Jiageng Wu, et al.
https://arxiv.org/abs/2504.19467 https://mastoxiv.page/@arXiv_csCL_bot/114420036189999973
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Anastasia Zhukova, Thomas Walton, Christian E. Lobm\"uller, Bela Gipp
https://arxiv.org/abs/2508.09096 https://mastoxiv.page/@arXiv_csCL_bot/115020938764936882
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Jun Seo Kim, Hyemi Kim, Woo Joo Oh, Hongjin Cho, Hochul Lee, Hye Hyeon Kim
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Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka
https://arxiv.org/abs/2510.08284 https://mastoxiv.page/@arXiv_csCL_bot/115349533441895984
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