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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Learning Evaluation Models from Large Language Models for Sequence Generation
Wang, Zhou, Chang, Liu, Zhang, Du, Xiao, Zhang, Zhu
https://arxiv.org/abs/2308.04386 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/110858347619335772
- MockLLM: A Multi-Agent Behavior Collaboration Framework for Online Job Seeking and Recruiting
Hongda Sun, Hongzhan Lin, Haiyu Yan, Yang Song, Xin Gao, Rui Yan
https://arxiv.org/abs/2405.18113
- Capturing Style in Author and Document Representation
Enzo Terreau, Antoine Gourru, Julien Velcin
https://arxiv.org/abs/2407.13358 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/112811825734411424
- Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Perso...
Alireza Salemi, Hamed Zamani
https://arxiv.org/abs/2409.09510
- Learning to Rank for Multiple Retrieval-Augmented Models through Iterative Utility Maximization
Alireza Salemi, Hamed Zamani
https://arxiv.org/abs/2410.09942 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113311129163471763
- A Troublemaker with Contagious Jailbreak Makes Chaos in Honest Towns
Tianyi Men, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao
https://arxiv.org/abs/2410.16155 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113350851853547191
- SceneGenAgent: Precise Industrial Scene Generation with Coding Agent
Xiao Xia, Dan Zhang, Zibo Liao, Zhenyu Hou, Tianrui Sun, Jing Li, Ling Fu, Yuxiao Dong
https://arxiv.org/abs/2410.21909 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113395761808224082
- Prompting with Phonemes: Enhancing LLMs' Multilinguality for Non-Latin Script Languages
Nguyen, Mahajan, Yadav, Salazar, Yu, Hashemi, Maheshwary
https://arxiv.org/abs/2411.02398 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113429970773753104
- CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement
Leitian Tao, Xiang Chen, Tong Yu, Tung Mai, Ryan Rossi, Yixuan Li, Saayan Mitra
https://arxiv.org/abs/2411.05199 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113463056002627712
- OpenNER 1.0: Standardized Open-Access Named Entity Recognition Datasets in 50 Languages
Chester Palen-Michel, Maxwell Pickering, Maya Kruse, Jonne S\"alev\"a, Constantine Lignos
https://arxiv.org/abs/2412.09587 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113644218384986889
- Do Large Language Models Advocate for Inferentialism?
Yuzuki Arai, Sho Tsugawa
https://arxiv.org/abs/2412.14501 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113683825673436379
- Reward-Guided Speculative Decoding for Efficient LLM Reasoning
Liao, Xu, Dong, Li, Monz, Savarese, Sahoo, Xiong
https://arxiv.org/abs/2501.19324 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/113938656684817759
- Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
Borkar, Jagielski, Lee, Mireshghallah, Smith, Choquette-Choo
https://arxiv.org/abs/2502.15680 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114057719598830407
- Evaluating Large Language Models for Automated Clinical Abstraction in Pulmonary Embolism Registr...
Alwakeel, Buck, Martin, Aslam, Rajagopal, Pei, Podgoreanu, Lindsell, Wong
https://arxiv.org/abs/2503.21004 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114238829015127676
- LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
Zou, Huang, Wu, Chen, Miao, Nguyen, Zhou, Zhang, Fang, He, Li, Li, Jiang, Liu, Yu
https://arxiv.org/abs/2505.00753 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114453994824459151
- Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
Yaorui Shi, Sihang Li, Chang Wu, Zhiyuan Liu, Junfeng Fang, Hengxing Cai, An Zhang, Xiang Wang
https://arxiv.org/abs/2505.11277 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114533276160074683
- A3 : an Analytical Low-Rank Approximation Framework for Attention
Wong, Zhang, Cao, Gimenes, Constantinides, Luk, Zhao
https://arxiv.org/abs/2505.12942 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114539100871221308
- Thinkless: LLM Learns When to Think
Gongfan Fang, Xinyin Ma, Xinchao Wang
https://arxiv.org/abs/2505.13379 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114539110987186123
- Explainability of Large Language Models using SMILE: Statistical Model-agnostic Interpretability ...
Zeinab Dehghani, Mohammed Naveed Akram, Koorosh Aslansefat, Adil Khan
https://arxiv.org/abs/2505.21657 initial toot: https://mastoxiv.page/@arXiv_csCL_bot/114589890676312038
toXiv_bot_toot

Fast entropy-regularized SDP relaxations for permutation synchronization
Michael Lindsey, Yunpeng Shi
https://arxiv.org/abs/2506.20191 https://

Fast entropy-regularized SDP relaxations for permutation synchronization
We introduce fast randomized algorithms for solving semidefinite programming (SDP) relaxations of the partial permutation synchronization (PPS) problem, a core task in multi-image matching with significant relevance to 3D reconstruction. Our methods build on recent advances in entropy-regularized semidefinite programming and are tailored to the unique structure of PPS, in which the unknowns are partial permutation matrices aligning sparse and noisy pairwise correspondences across images. We pro…

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Exploring Dark Energy via Non-Minimal Coupling in $f(Q,L_m)$ Gravity with Gong-Zhang Parameterization
Yash B. Ingole, K. V. Somwanshi, S. R. Bhoyar, S. N. Bayaskar
https://arxiv.org/abs/2506.21083

Exploring Dark Energy via Non-Minimal Coupling in $f(Q,L_m)$ Gravity with Gong-Zhang Parameterization
In this study, we investigate the late-time accelerated expansion of the universe within the framework of non-minimally coupled $f(Q,L_m)$ gravity, where $Q$ is the non-metricity scalar and $L_m$ is the matter Lagrangian. We derive modified Friedmann equations in a flat FLRW background and employ the \textit{Gong-Zhang} parameterization for the DE equation of state (EoS), allowing an analytical form of the Hubble parameter $H(z)$. The model parameters are constrained using recent Cosmic Chronom…

My fair city of Roanoke attained “Bee City USA” status in 2022, which aims to “promote healthy, sustainable habitats for bees and other pollinators, responsible for the reproduction of nearly 90% of the world’s flowering plant species and one in every three bites of food we eat.”
Last night, a couple of garden clubs teamed up with the city to screen a new PBS Nature documentary, “My Garden of a Thousand Bees”.

My Garden of a Thousand Bees
New PBS documentary following wildlife filmmaker Martin Dohrn as he films more than 60 species of bees in his urban garden in Bristol, England, during the COVID-19 2020 lockdown.

Reflection-less filter for superconducting quantum circuits
Jessica Kedziora, Eric Q. Bui, Alec Yen, Andres E. Lombo, Kaidong Peng, Terence J. Weir, Kevin P. O'Brien
https://arxiv.org/abs/2506.20645

Reflection-less filter for superconducting quantum circuits
Protecting superconducting quantum circuits from non-ideal return loss, including out-of-band circulator behavior and enhancing the performance of broadband quantum-limited amplifiers can be accomplished using a superconducting version of a special class of microwave filters known as reflection-less filters. These filters can simultaneously permit low pass band loss to preserve quantum efficiency and broad band reflection-less characteristics in the stop and pass bands. The filter also suppress…

A parametric tensor ROM for the shallow water dam break problem
Md Rezwan Bin Mizan, Maxim Olshanskii, Ilya Timofeyev
https://arxiv.org/abs/2506.20007 http…

A parametric tensor ROM for the shallow water dam break problem
We develop a variant of a tensor reduced-order model (tROM) for the parameterized shallow-water dam-break problem. This hyperbolic system presents multiple challenges for model reduction, including a slow decay of the Kolmogorov $N$-width of the solution manifold, shock formation, and the loss of smooth solution dependence on parameters. These issues limit the performance of traditional Proper Orthogonal Decomposition based ROMs. Our tROM approach, based on a low-rank tensor decomposition, buil…

What they say: What they mean:
New Different colors from previous version.
All New Not compatible with previous version.
Exclusive Nobody else has documentation.
Unmatched Almost as good as the competition.
Design Simplicity The company wouldn't give us any money.
Fool-proof Operation All parameters are hard-coded.
Advanced Design Nobody really understands it.
Here At Last Didn't get it done on time.
Field Tested We do…

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